litellm/litellm/tests/test_streaming.py

#### What this tests ####
#    This tests streaming for the completion endpoint

import sys, os, asyncio
import traceback
import time, pytest
from pydantic import BaseModel
from typing import Tuple

sys.path.insert(
    0, os.path.abspath("../..")
)  # Adds the parent directory to the system path
from dotenv import load_dotenv

load_dotenv()
import litellm
from litellm import (
    completion,
    acompletion,
    AuthenticationError,
    BadRequestError,
    RateLimitError,
    ModelResponse,
)

litellm.logging = False
litellm.set_verbose = True
litellm.num_retries = 3
litellm.cache = None

score = 0


def logger_fn(model_call_object: dict):
    print(f"model call details: {model_call_object}")


user_message = "Hello, how are you?"
messages = [{"content": user_message, "role": "user"}]


first_openai_chunk_example = {
    "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp",
    "object": "chat.completion.chunk",
    "created": 1694881253,
    "model": "gpt-4-0613",
    "choices": [
        {
            "index": 0,
            "delta": {"role": "assistant", "content": ""},
            "finish_reason": None,  # it's null
        }
    ],
}


def validate_first_format(chunk):
    # write a test to make sure chunk follows the same format as first_openai_chunk_example
    assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary."
    assert isinstance(chunk["id"], str), "'id' should be a string."
    assert isinstance(chunk["object"], str), "'object' should be a string."
    assert isinstance(chunk["created"], int), "'created' should be an integer."
    assert isinstance(chunk["model"], str), "'model' should be a string."
    assert isinstance(chunk["choices"], list), "'choices' should be a list."
    assert not hasattr(chunk, "usage"), "Chunk cannot contain usage"

    for choice in chunk["choices"]:
        assert isinstance(choice["index"], int), "'index' should be an integer."
        assert isinstance(choice["delta"]["role"], str), "'role' should be a string."
        assert "messages" not in choice
        # openai v1.0.0 returns content as None
        assert (choice["finish_reason"] is None) or isinstance(
            choice["finish_reason"], str
        ), "'finish_reason' should be None or a string."


second_openai_chunk_example = {
    "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp",
    "object": "chat.completion.chunk",
    "created": 1694881253,
    "model": "gpt-4-0613",
    "choices": [
        {"index": 0, "delta": {"content": "Hello"}, "finish_reason": None}  # it's null
    ],
}


def validate_second_format(chunk):
    assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary."
    assert isinstance(chunk["id"], str), "'id' should be a string."
    assert isinstance(chunk["object"], str), "'object' should be a string."
    assert isinstance(chunk["created"], int), "'created' should be an integer."
    assert isinstance(chunk["model"], str), "'model' should be a string."
    assert isinstance(chunk["choices"], list), "'choices' should be a list."
    assert not hasattr(chunk, "usage"), "Chunk cannot contain usage"

    for choice in chunk["choices"]:
        assert isinstance(choice["index"], int), "'index' should be an integer."
        assert hasattr(choice["delta"], "role"), "'role' should be a string."
        # openai v1.0.0 returns content as None
        assert (choice["finish_reason"] is None) or isinstance(
            choice["finish_reason"], str
        ), "'finish_reason' should be None or a string."


last_openai_chunk_example = {
    "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp",
    "object": "chat.completion.chunk",
    "created": 1694881253,
    "model": "gpt-4-0613",
    "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
}

"""
Final chunk (sdk):
chunk: ChatCompletionChunk(id='chatcmpl-96mM3oNBlxh2FDWVLKsgaFBBcULmI', 
choices=[Choice(delta=ChoiceDelta(content=None, function_call=None, role=None, 
tool_calls=None), finish_reason='stop', index=0, logprobs=None)], 
created=1711402871, model='gpt-3.5-turbo-0125', object='chat.completion.chunk', system_fingerprint='fp_3bc1b5746c')
"""


def validate_last_format(chunk):
    """
    Ensure last chunk has no remaining content or tools
    """
    assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary."
    assert isinstance(chunk["id"], str), "'id' should be a string."
    assert isinstance(chunk["object"], str), "'object' should be a string."
    assert isinstance(chunk["created"], int), "'created' should be an integer."
    assert isinstance(chunk["model"], str), "'model' should be a string."
    assert isinstance(chunk["choices"], list), "'choices' should be a list."
    assert not hasattr(chunk, "usage"), "Chunk cannot contain usage"

    for choice in chunk["choices"]:
        assert isinstance(choice["index"], int), "'index' should be an integer."
        assert choice["delta"]["content"] is None
        assert choice["delta"]["function_call"] is None
        assert choice["delta"]["role"] is None
        assert choice["delta"]["tool_calls"] is None
        assert isinstance(
            choice["finish_reason"], str
        ), "'finish_reason' should be a string."


def streaming_format_tests(idx, chunk) -> Tuple[str, bool]:
    extracted_chunk = ""
    finished = False
    print(f"chunk: {chunk}")
    if idx == 0:  # ensure role assistant is set
        validate_first_format(chunk=chunk)
        role = chunk["choices"][0]["delta"]["role"]
        assert role == "assistant"
    elif idx == 1:  # second chunk
        validate_second_format(chunk=chunk)
    if idx != 0:  # ensure no role
        if "role" in chunk["choices"][0]["delta"]:
            pass  # openai v1.0.0+ passes role = None
    if chunk["choices"][0][
        "finish_reason"
    ]:  # ensure finish reason is only in last chunk
        validate_last_format(chunk=chunk)
        finished = True
    if (
        "content" in chunk["choices"][0]["delta"]
        and chunk["choices"][0]["delta"]["content"] is not None
    ):
        extracted_chunk = chunk["choices"][0]["delta"]["content"]
    print(f"extracted chunk: {extracted_chunk}")
    return extracted_chunk, finished


tools_schema = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

# def test_completion_cohere_stream():
# # this is a flaky test due to the cohere API endpoint being unstable
#     try:
#         messages = [
#             {"role": "system", "content": "You are a helpful assistant."},
#             {
#                 "role": "user",
#                 "content": "how does a court case get to the Supreme Court?",
#             },
#         ]
#         response = completion(
#             model="command-nightly", messages=messages, stream=True, max_tokens=50,
#         )
#         complete_response = ""
#         # Add any assertions here to check the response
#         has_finish_reason = False
#         for idx, chunk in enumerate(response):
#             chunk, finished = streaming_format_tests(idx, chunk)
#             has_finish_reason = finished
#             if finished:
#                 break
#             complete_response += chunk
#         if has_finish_reason is False:
#             raise Exception("Finish reason not in final chunk")
#         if complete_response.strip() == "":
#             raise Exception("Empty response received")
#         print(f"completion_response: {complete_response}")
#     except Exception as e:
#         pytest.fail(f"Error occurred: {e}")

# test_completion_cohere_stream()


def test_completion_azure_stream_special_char():
    litellm.set_verbose = True
    messages = [{"role": "user", "content": "hi. respond with the <xml> tag only"}]
    response = completion(model="azure/chatgpt-v-2", messages=messages, stream=True)
    response_str = ""
    for part in response:
        response_str += part.choices[0].delta.content or ""
    print(f"response_str: {response_str}")
    assert len(response_str) > 0


def test_completion_cohere_stream_bad_key():
    try:
        litellm.cache = None
        api_key = "bad-key"
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        response = completion(
            model="command-nightly",
            messages=messages,
            stream=True,
            max_tokens=50,
            api_key=api_key,
        )
        complete_response = ""
        # Add any assertions here to check the response
        has_finish_reason = False
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            if finished:
                break
            complete_response += chunk
        if has_finish_reason is False:
            raise Exception("Finish reason not in final chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except AuthenticationError as e:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_cohere_stream_bad_key()


def test_completion_azure_stream():
    try:
        litellm.set_verbose = False
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        response = completion(
            model="azure/chatgpt-v-2", messages=messages, stream=True, max_tokens=50
        )
        complete_response = ""
        # Add any assertions here to check the response
        for idx, init_chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, init_chunk)
            complete_response += chunk
            custom_llm_provider = init_chunk._hidden_params["custom_llm_provider"]
            print(f"custom_llm_provider: {custom_llm_provider}")
            assert custom_llm_provider == "azure"
            if finished:
                assert isinstance(init_chunk.choices[0], litellm.utils.StreamingChoices)
                break
        if complete_response.strip() == "":
            raise Exception("Empty response received")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_azure_stream()
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_completion_predibase_streaming(sync_mode):
    try:
        litellm.set_verbose = True

        if sync_mode:
            response = completion(
                model="predibase/llama-3-8b-instruct",
                tenant_id="c4768f95",
                api_base="https://serving.app.predibase.com",
                api_key=os.getenv("PREDIBASE_API_KEY"),
                messages=[{"role": "user", "content": "What is the meaning of life?"}],
                stream=True,
            )

            complete_response = ""
            for idx, init_chunk in enumerate(response):
                chunk, finished = streaming_format_tests(idx, init_chunk)
                complete_response += chunk
                custom_llm_provider = init_chunk._hidden_params["custom_llm_provider"]
                print(f"custom_llm_provider: {custom_llm_provider}")
                assert custom_llm_provider == "predibase"
                if finished:
                    assert isinstance(
                        init_chunk.choices[0], litellm.utils.StreamingChoices
                    )
                    break
            if complete_response.strip() == "":
                raise Exception("Empty response received")
        else:
            response = await litellm.acompletion(
                model="predibase/llama-3-8b-instruct",
                tenant_id="c4768f95",
                api_base="https://serving.app.predibase.com",
                api_key=os.getenv("PREDIBASE_API_KEY"),
                messages=[{"role": "user", "content": "What is the meaning of life?"}],
                stream=True,
            )

            # await response

            complete_response = ""
            idx = 0
            async for init_chunk in response:
                chunk, finished = streaming_format_tests(idx, init_chunk)
                complete_response += chunk
                custom_llm_provider = init_chunk._hidden_params["custom_llm_provider"]
                print(f"custom_llm_provider: {custom_llm_provider}")
                assert custom_llm_provider == "predibase"
                idx += 1
                if finished:
                    assert isinstance(
                        init_chunk.choices[0], litellm.utils.StreamingChoices
                    )
                    break
            if complete_response.strip() == "":
                raise Exception("Empty response received")

        print(f"complete_response: {complete_response}")
    except litellm.Timeout as e:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


def test_completion_azure_function_calling_stream():
    try:
        litellm.set_verbose = False
        user_message = "What is the current weather in Boston?"
        messages = [{"content": user_message, "role": "user"}]
        response = completion(
            model="azure/chatgpt-functioncalling",
            messages=messages,
            stream=True,
            tools=tools_schema,
        )
        # Add any assertions here to check the response
        for chunk in response:
            print(chunk)
            if chunk["choices"][0]["finish_reason"] == "stop":
                break
            print(chunk["choices"][0]["finish_reason"])
            print(chunk["choices"][0]["delta"]["content"])
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_azure_function_calling_stream()


@pytest.mark.skip("Flaky ollama test - needs to be fixed")
def test_completion_ollama_hosted_stream():
    try:
        litellm.set_verbose = True
        response = completion(
            model="ollama/phi",
            messages=messages,
            max_tokens=10,
            num_retries=3,
            timeout=20,
            api_base="https://test-ollama-endpoint.onrender.com",
            stream=True,
        )
        # Add any assertions here to check the response
        complete_response = ""
        # Add any assertions here to check the response
        for idx, init_chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, init_chunk)
            complete_response += chunk
            if finished:
                assert isinstance(init_chunk.choices[0], litellm.utils.StreamingChoices)
                break
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"complete_response: {complete_response}")
    except Exception as e:
        if "try pulling it first" in str(e):
            return
        pytest.fail(f"Error occurred: {e}")


# test_completion_ollama_hosted_stream()


def test_completion_claude_stream():
    try:
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        response = completion(
            model="claude-instant-1.2", messages=messages, stream=True, max_tokens=50
        )
        complete_response = ""
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            complete_response += chunk
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")
    
# test_completion_claude_stream()
def test_completion_claude_2_stream():
    litellm.set_verbose = True
    response = completion(
        model="claude-2",
        messages=[{"role": "user", "content": "hello from litellm"}],
        stream=True,
    )
    complete_response = ""
    # Add any assertions here to check the response
    idx = 0
    for chunk in response:
        print(chunk)
        # print(chunk.choices[0].delta)
        chunk, finished = streaming_format_tests(idx, chunk)
        if finished:
            break
        complete_response += chunk
        idx += 1
    if complete_response.strip() == "":
        raise Exception("Empty response received")
    print(f"completion_response: {complete_response}")


@pytest.mark.asyncio
async def test_acompletion_claude_2_stream():
    litellm.set_verbose = True
    response = await litellm.acompletion(
        model="claude-2",
        messages=[{"role": "user", "content": "hello from litellm"}],
        stream=True,
    )
    complete_response = ""
    # Add any assertions here to check the response
    idx = 0
    async for chunk in response:
        print(chunk)
        # print(chunk.choices[0].delta)
        chunk, finished = streaming_format_tests(idx, chunk)
        if finished:
            break
        complete_response += chunk
        idx += 1
    if complete_response.strip() == "":
        raise Exception("Empty response received")
    print(f"completion_response: {complete_response}")


def test_completion_palm_stream():
    try:
        litellm.set_verbose = False
        print("Streaming palm response")
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        print("testing palm streaming")
        response = completion(model="palm/chat-bison", messages=messages, stream=True)

        complete_response = ""
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            print(chunk)
            # print(chunk.choices[0].delta)
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            complete_response += chunk
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except litellm.Timeout as e:
        pass
    except litellm.APIError as e:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_palm_stream()


def test_completion_gemini_stream():
    try:
        litellm.set_verbose = True
        print("Streaming gemini response")
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        print("testing gemini streaming")
        response = completion(model="gemini/gemini-pro", messages=messages, stream=True)
        print(f"type of response at the top: {response}")
        complete_response = ""
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            print(chunk)
            # print(chunk.choices[0].delta)
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            complete_response += chunk
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except litellm.APIError as e:
        pass
    except Exception as e:
        if "429 Resource has been exhausted":
            return
        pytest.fail(f"Error occurred: {e}")


@pytest.mark.asyncio
async def test_acompletion_gemini_stream():
    try:
        litellm.set_verbose = True
        print("Streaming gemini response")
        messages = [
            # {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "What do you know?",
            },
        ]
        print("testing gemini streaming")
        response = await acompletion(
            model="gemini/gemini-pro", messages=messages, max_tokens=50, stream=True
        )
        print(f"type of response at the top: {response}")
        complete_response = ""
        idx = 0
        # Add any assertions here to check, the response
        async for chunk in response:
            print(f"chunk in acompletion gemini: {chunk}")
            print(chunk.choices[0].delta)
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            print(f"chunk: {chunk}")
            complete_response += chunk
            idx += 1
        print(f"completion_response: {complete_response}")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
    except litellm.APIError as e:
        pass
    except litellm.RateLimitError as e:
        pass
    except Exception as e:
        if "429 Resource has been exhausted" in str(e):
            pass
        else:
            pytest.fail(f"Error occurred: {e}")


# asyncio.run(test_acompletion_gemini_stream())


def test_completion_mistral_api_stream():
    try:
        litellm.set_verbose = True
        print("Testing streaming mistral api response")
        response = completion(
            model="mistral/mistral-medium",
            messages=[
                {
                    "role": "user",
                    "content": "Hey, how's it going?",
                }
            ],
            max_tokens=10,
            stream=True,
        )
        complete_response = ""
        has_finish_reason = False
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                has_finish_reason = True
                break
            complete_response += chunk
        if has_finish_reason == False:
            raise Exception("finish reason not set")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except litellm.APIError as e:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


def test_completion_mistral_api_mistral_large_function_call_with_streaming():
    litellm.set_verbose = True
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]
    messages = [
        {
            "role": "user",
            "content": "What's the weather like in Boston today in fahrenheit?",
        }
    ]
    try:
        # test without max tokens
        response = completion(
            model="mistral/mistral-large-latest",
            messages=messages,
            tools=tools,
            tool_choice="auto",
            stream=True,
        )
        idx = 0
        for chunk in response:
            print(f"chunk in response: {chunk}")
            if idx == 0:
                assert (
                    chunk.choices[0].delta.tool_calls[0].function.arguments is not None
                )
                assert isinstance(
                    chunk.choices[0].delta.tool_calls[0].function.arguments, str
                )
                validate_first_streaming_function_calling_chunk(chunk=chunk)
            elif idx == 1 and chunk.choices[0].finish_reason is None:
                validate_second_streaming_function_calling_chunk(chunk=chunk)
            elif chunk.choices[0].finish_reason is not None:  # last chunk
                validate_final_streaming_function_calling_chunk(chunk=chunk)
            idx += 1
        # raise Exception("it worked!")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_mistral_api_stream()


def test_completion_deep_infra_stream():
    # deep infra,currently includes role in the 2nd chunk
    # waiting for them to make a fix on this
    litellm.set_verbose = True
    try:
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        print("testing deep infra streaming")
        response = completion(
            model="deepinfra/meta-llama/Llama-2-70b-chat-hf",
            messages=messages,
            stream=True,
            max_tokens=80,
        )

        complete_response = ""
        # Add any assertions here to check the response
        has_finish_reason = False
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                has_finish_reason = True
                break
            complete_response += chunk
        if has_finish_reason == False:
            raise Exception("finish reason not set")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except Exception as e:
        if "Model busy, retry later" in str(e):
            pass
        pytest.fail(f"Error occurred: {e}")


# test_completion_deep_infra_stream()


@pytest.mark.skip()
def test_completion_nlp_cloud_stream():
    try:
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        print("testing nlp cloud streaming")
        response = completion(
            model="nlp_cloud/finetuned-llama-2-70b",
            messages=messages,
            stream=True,
            max_tokens=20,
        )

        complete_response = ""
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            complete_response += chunk
            if finished:
                break
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except Exception as e:
        print(f"Error occurred: {e}")
        pytest.fail(f"Error occurred: {e}")


# test_completion_nlp_cloud_stream()


def test_completion_claude_stream_bad_key():
    try:
        litellm.cache = None
        litellm.set_verbose = True
        api_key = "bad-key"
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        response = completion(
            model="claude-instant-1",
            messages=messages,
            stream=True,
            max_tokens=50,
            api_key=api_key,
        )
        complete_response = ""
        # Add any assertions here to check the response
        has_finish_reason = False
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                has_finish_reason = True
                break
            complete_response += chunk
        if has_finish_reason == False:
            raise Exception("finish reason not set")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"1234completion_response: {complete_response}")
        raise Exception("Auth error not raised")
    except AuthenticationError as e:
        print("Auth Error raised")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_claude_stream_bad_key()
# test_completion_replicate_stream()


def test_vertex_ai_stream():
    from litellm.tests.test_amazing_vertex_completion import load_vertex_ai_credentials

    load_vertex_ai_credentials()
    litellm.set_verbose = True
    litellm.vertex_project = "adroit-crow-413218"
    import random

    test_models = ["gemini-1.0-pro"]
    for model in test_models:
        try:
            print("making request", model)
            response = completion(
                model=model,
                messages=[
                    {"role": "user", "content": "write 10 line code code for saying hi"}
                ],
                stream=True,
            )
            complete_response = ""
            is_finished = False
            for idx, chunk in enumerate(response):
                print(f"chunk in response: {chunk}")
                chunk, finished = streaming_format_tests(idx, chunk)
                if finished:
                    is_finished = True
                    break
                complete_response += chunk
            if complete_response.strip() == "":
                raise Exception("Empty response received")
            print(f"completion_response: {complete_response}")
            assert is_finished == True
        except litellm.RateLimitError as e:
            pass
        except Exception as e:
            pytest.fail(f"Error occurred: {e}")


# def test_completion_vertexai_stream():
#     try:
#         import os
#         os.environ["VERTEXAI_PROJECT"] = "pathrise-convert-1606954137718"
#         os.environ["VERTEXAI_LOCATION"] = "us-central1"
#         messages = [
#             {"role": "system", "content": "You are a helpful assistant."},
#             {
#                 "role": "user",
#                 "content": "how does a court case get to the Supreme Court?",
#             },
#         ]
#         response = completion(
#             model="vertex_ai/chat-bison", messages=messages, stream=True, max_tokens=50
#         )
#         complete_response = ""
#         has_finish_reason = False
#         # Add any assertions here to check the response
#         for idx, chunk in enumerate(response):
#             chunk, finished = streaming_format_tests(idx, chunk)
#             has_finish_reason = finished
#             if finished:
#                 break
#             complete_response += chunk
#         if has_finish_reason is False:
#             raise Exception("finish reason not set for last chunk")
#         if complete_response.strip() == "":
#             raise Exception("Empty response received")
#         print(f"completion_response: {complete_response}")
#     except InvalidRequestError as e:
#         pass
#     except Exception as e:
#         pytest.fail(f"Error occurred: {e}")

# test_completion_vertexai_stream()


# def test_completion_vertexai_stream_bad_key():
#     try:
#         import os
#         messages = [
#             {"role": "system", "content": "You are a helpful assistant."},
#             {
#                 "role": "user",
#                 "content": "how does a court case get to the Supreme Court?",
#             },
#         ]
#         response = completion(
#             model="vertex_ai/chat-bison", messages=messages, stream=True, max_tokens=50
#         )
#         complete_response = ""
#         has_finish_reason = False
#         # Add any assertions here to check the response
#         for idx, chunk in enumerate(response):
#             chunk, finished = streaming_format_tests(idx, chunk)
#             has_finish_reason = finished
#             if finished:
#                 break
#             complete_response += chunk
#         if has_finish_reason is False:
#             raise Exception("finish reason not set for last chunk")
#         if complete_response.strip() == "":
#             raise Exception("Empty response received")
#         print(f"completion_response: {complete_response}")
#     except InvalidRequestError as e:
#         pass
#     except Exception as e:
#         pytest.fail(f"Error occurred: {e}")

# test_completion_vertexai_stream_bad_key()

# def test_completion_replicate_stream():
# TEMP Commented out - replicate throwing an auth error
#     try:
#         litellm.set_verbose = True
#         messages = [
#             {"role": "system", "content": "You are a helpful assistant."},
#             {
#                 "role": "user",
#                 "content": "how does a court case get to the Supreme Court?",
#             },
#         ]
#         response = completion(
#             model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", messages=messages, stream=True, max_tokens=50
#         )
#         complete_response = ""
#         has_finish_reason = False
#         # Add any assertions here to check the response
#         for idx, chunk in enumerate(response):
#             chunk, finished = streaming_format_tests(idx, chunk)
#             has_finish_reason = finished
#             if finished:
#                 break
#             complete_response += chunk
#         if has_finish_reason is False:
#             raise Exception("finish reason not set for last chunk")
#         if complete_response.strip() == "":
#             raise Exception("Empty response received")
#         print(f"completion_response: {complete_response}")
#     except InvalidRequestError as e:
#         pass
#     except Exception as e:
#         pytest.fail(f"Error occurred: {e}")


def test_bedrock_claude_3_streaming():
    try:
        litellm.set_verbose = True
        response: ModelResponse = completion(  # type: ignore
            model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
            messages=messages,
            max_tokens=10,  # type: ignore
            stream=True,
        )
        complete_response = ""
        # Add any assertions here to check the response
        has_finish_reason = False
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                has_finish_reason = True
                break
            complete_response += chunk
        if has_finish_reason == False:
            raise Exception("finish reason not set")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except RateLimitError:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


@pytest.mark.asyncio
async def test_claude_3_streaming_finish_reason():
    try:
        litellm.set_verbose = True
        messages = [
            {"role": "system", "content": "Be helpful"},
            {"role": "user", "content": "What do you know?"},
        ]
        response: ModelResponse = await litellm.acompletion(  # type: ignore
            model="claude-3-opus-20240229",
            messages=messages,
            stream=True,
            max_tokens=10,
        )
        complete_response = ""
        # Add any assertions here to-check the response
        num_finish_reason = 0
        async for chunk in response:
            print(f"chunk: {chunk}")
            if isinstance(chunk, ModelResponse):
                if chunk.choices[0].finish_reason is not None:
                    num_finish_reason += 1
        assert num_finish_reason == 1
    except RateLimitError:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


@pytest.mark.skip(reason="Replicate changed exceptions")
def test_completion_replicate_stream_bad_key():
    try:
        api_key = "bad-key"
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "how does a court case get to the Supreme Court?",
            },
        ]
        response = completion(
            model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
            messages=messages,
            stream=True,
            max_tokens=50,
            api_key=api_key,
        )
        complete_response = ""
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            complete_response += chunk
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except AuthenticationError as e:
        # this is an auth error with a bad key
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_replicate_stream_bad_key()


def test_completion_bedrock_claude_stream():
    try:
        litellm.set_verbose = False
        response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=[
                {
                    "role": "user",
                    "content": "Be as verbose as possible and give as many details as possible, how does a court case get to the Supreme Court?",
                }
            ],
            temperature=1,
            max_tokens=20,
            stream=True,
        )
        print(response)
        complete_response = ""
        has_finish_reason = False
        # Add any assertions here to check the response
        first_chunk_id = None
        for idx, chunk in enumerate(response):
            # print
            if idx == 0:
                first_chunk_id = chunk.id
            else:
                assert (
                    chunk.id == first_chunk_id
                ), f"chunk ids do not match: {chunk.id} != first chunk id{first_chunk_id}"
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            complete_response += chunk
            if finished:
                break
        if has_finish_reason is False:
            raise Exception("finish reason not set for last chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
    except RateLimitError:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_bedrock_claude_stream()


def test_completion_bedrock_ai21_stream():
    try:
        litellm.set_verbose = False
        response = completion(
            model="bedrock/ai21.j2-mid-v1",
            messages=[
                {
                    "role": "user",
                    "content": "Be as verbose as possible and give as many details as possible, how does a court case get to the Supreme Court?",
                }
            ],
            temperature=1,
            max_tokens=20,
            stream=True,
        )
        print(response)
        complete_response = ""
        has_finish_reason = False
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            # print
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            complete_response += chunk
            if finished:
                break
        if has_finish_reason is False:
            raise Exception("finish reason not set for last chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except RateLimitError:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_bedrock_ai21_stream()


def test_completion_bedrock_mistral_stream():
    try:
        litellm.set_verbose = False
        response = completion(
            model="bedrock/mistral.mixtral-8x7b-instruct-v0:1",
            messages=[
                {
                    "role": "user",
                    "content": "Be as verbose as possible and give as many details as possible, how does a court case get to the Supreme Court?",
                }
            ],
            temperature=1,
            max_tokens=20,
            stream=True,
        )
        print(response)
        complete_response = ""
        has_finish_reason = False
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            # print
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            complete_response += chunk
            if finished:
                break
        if has_finish_reason is False:
            raise Exception("finish reason not set for last chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except RateLimitError:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


def test_sagemaker_weird_response():
    """
    When the stream ends, flush any remaining holding chunks.
    """
    try:
        from litellm.llms.sagemaker import TokenIterator
        import json
        import json
        from litellm.llms.sagemaker import TokenIterator

        chunk = """<s>[INST] Hey, how's it going? [/INST],
        I'm doing well, thanks for asking! How about you? Is there anything you'd like to chat about or ask? I'm here to help with any questions you might have."""

        data = "\n".join(
            map(
                lambda x: f"data: {json.dumps({'token': {'text': x.strip()}})}",
                chunk.strip().split(","),
            )
        )
        stream = bytes(data, encoding="utf8")

        # Modify the array to be a dictionary with "PayloadPart" and "Bytes" keys.
        stream_iterator = iter([{"PayloadPart": {"Bytes": stream}}])

        token_iter = TokenIterator(stream_iterator)

        # for token in token_iter:
        #     print(token)
        litellm.set_verbose = True

        logging_obj = litellm.Logging(
            model="berri-benchmarking-Llama-2-70b-chat-hf-4",
            messages=messages,
            stream=True,
            litellm_call_id="1234",
            function_id="function_id",
            call_type="acompletion",
            start_time=time.time(),
        )
        response = litellm.CustomStreamWrapper(
            completion_stream=token_iter,
            model="berri-benchmarking-Llama-2-70b-chat-hf-4",
            custom_llm_provider="sagemaker",
            logging_obj=logging_obj,
        )
        complete_response = ""
        for idx, chunk in enumerate(response):
            # print
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            complete_response += chunk
            if finished:
                break
        assert len(complete_response) > 0
    except Exception as e:
        pytest.fail(f"An exception occurred - {str(e)}")


# test_sagemaker_weird_response()


@pytest.mark.asyncio
async def test_sagemaker_streaming_async():
    try:
        messages = [{"role": "user", "content": "Hey, how's it going?"}]
        litellm.set_verbose = True
        response = await litellm.acompletion(
            model="sagemaker/jumpstart-dft-hf-llm-mistral-7b-ins-20240329-150233",
            model_id="huggingface-llm-mistral-7b-instruct-20240329-150233",
            messages=messages,
            temperature=0.2,
            max_tokens=80,
            aws_region_name=os.getenv("AWS_REGION_NAME_2"),
            aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID_2"),
            aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY_2"),
            stream=True,
        )
        # Add any assertions here to check the response
        print(response)
        complete_response = ""
        has_finish_reason = False
        # Add any assertions here to check the response
        idx = 0
        async for chunk in response:
            # print
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            complete_response += chunk
            if finished:
                break
            idx += 1
        if has_finish_reason is False:
            raise Exception("finish reason not set for last chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except Exception as e:
        pytest.fail(f"An exception occurred - {str(e)}")


# asyncio.run(test_sagemaker_streaming_async())


def test_completion_sagemaker_stream():
    try:
        response = completion(
            model="sagemaker/jumpstart-dft-hf-llm-mistral-7b-ins-20240329-150233",
            model_id="huggingface-llm-mistral-7b-instruct-20240329-150233",
            messages=messages,
            temperature=0.2,
            max_tokens=80,
            aws_region_name=os.getenv("AWS_REGION_NAME_2"),
            aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID_2"),
            aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY_2"),
            stream=True,
        )
        complete_response = ""
        has_finish_reason = False
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            if finished:
                break
            complete_response += chunk
        if has_finish_reason is False:
            raise Exception("finish reason not set for last chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


def test_completion_watsonx_stream():
    litellm.set_verbose = True
    try:
        response = completion(
            model="watsonx/ibm/granite-13b-chat-v2",
            messages=messages,
            temperature=0.5,
            max_tokens=20,
            stream=True,
        )
        complete_response = ""
        has_finish_reason = False
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            if finished:
                break
            complete_response += chunk
        if has_finish_reason is False:
            raise Exception("finish reason not set for last chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_sagemaker_stream()


# def test_maritalk_streaming():
#     messages = [{"role": "user", "content": "Hey"}]
#     try:
#         response = completion("maritalk", messages=messages, stream=True)
#         complete_response = ""
#         start_time = time.time()
#         for idx, chunk in enumerate(response):
#             chunk, finished = streaming_format_tests(idx, chunk)
#             complete_response += chunk
#             if finished:
#                 break
#         if complete_response.strip() == "":
#             raise Exception("Empty response received")
#     except:
#         pytest.fail(f"error occurred: {traceback.format_exc()}")
# test_maritalk_streaming()
# test on openai completion call


# # test on ai21 completion call
def ai21_completion_call():
    try:
        messages = [
            {
                "role": "system",
                "content": "You are an all-knowing oracle",
            },
            {"role": "user", "content": "What is the meaning of the Universe?"},
        ]
        response = completion(
            model="j2-ultra", messages=messages, stream=True, max_tokens=500
        )
        print(f"response: {response}")
        has_finished = False
        complete_response = ""
        start_time = time.time()
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finished = finished
            complete_response += chunk
            if finished:
                break
        if has_finished is False:
            raise Exception("finished reason missing from final chunk")
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except:
        pytest.fail(f"error occurred: {traceback.format_exc()}")


# ai21_completion_call()


def ai21_completion_call_bad_key():
    try:
        api_key = "bad-key"
        response = completion(
            model="j2-ultra", messages=messages, stream=True, api_key=api_key
        )
        print(f"response: {response}")
        complete_response = ""
        start_time = time.time()
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            complete_response += chunk
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"completion_response: {complete_response}")
    except:
        pytest.fail(f"error occurred: {traceback.format_exc()}")


# ai21_completion_call_bad_key()


@pytest.mark.skip(reason="flaky test")
@pytest.mark.asyncio
async def test_hf_completion_tgi_stream():
    try:
        response = await acompletion(
            model="huggingface/HuggingFaceH4/zephyr-7b-beta",
            messages=[{"content": "Hello, how are you?", "role": "user"}],
            stream=True,
        )
        # Add any assertions here to check the response
        print(f"response: {response}")
        complete_response = ""
        start_time = time.time()
        idx = 0
        async for chunk in response:
            chunk, finished = streaming_format_tests(idx, chunk)
            complete_response += chunk
            if finished:
                break
            idx += 1
        print(f"completion_response: {complete_response}")
    except litellm.ServiceUnavailableError as e:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# hf_test_completion_tgi_stream()

# def test_completion_aleph_alpha():
#     try:
#         response = completion(
#             model="luminous-base", messages=messages, stream=True
#         )
#         # Add any assertions here to check the response
#         has_finished = False
#         complete_response = ""
#         start_time = time.time()
#         for idx, chunk in enumerate(response):
#             chunk, finished = streaming_format_tests(idx, chunk)
#             has_finished = finished
#             complete_response += chunk
#             if finished:
#                 break
#         if has_finished is False:
#             raise Exception("finished reason missing from final chunk")
#         if complete_response.strip() == "":
#             raise Exception("Empty response received")
#     except Exception as e:
#         pytest.fail(f"Error occurred: {e}")

# # test_completion_aleph_alpha()

# def test_completion_aleph_alpha_bad_key():
#     try:
#         api_key = "bad-key"
#         response = completion(
#             model="luminous-base", messages=messages, stream=True, api_key=api_key
#         )
#         # Add any assertions here to check the response
#         has_finished = False
#         complete_response = ""
#         start_time = time.time()
#         for idx, chunk in enumerate(response):
#             chunk, finished = streaming_format_tests(idx, chunk)
#             has_finished = finished
#             complete_response += chunk
#             if finished:
#                 break
#         if has_finished is False:
#             raise Exception("finished reason missing from final chunk")
#         if complete_response.strip() == "":
#             raise Exception("Empty response received")
#     except InvalidRequestError as e:
#         pass
#     except Exception as e:
#         pytest.fail(f"Error occurred: {e}")

# test_completion_aleph_alpha_bad_key()


# test on openai completion call
def test_openai_chat_completion_call():
    try:
        litellm.set_verbose = False
        print(f"making openai chat completion call")
        response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
        complete_response = ""
        start_time = time.time()
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            print(f"outside chunk: {chunk}")
            if finished:
                break
            complete_response += chunk
            # print(f'complete_chunk: {complete_response}')
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"complete response: {complete_response}")
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass


# test_openai_chat_completion_call()


def test_openai_chat_completion_complete_response_call():
    try:
        complete_response = completion(
            model="gpt-3.5-turbo",
            messages=messages,
            stream=True,
            complete_response=True,
        )
        print(f"complete response: {complete_response}")
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass


# test_openai_chat_completion_complete_response_call()
def test_openai_stream_options_call():
    litellm.set_verbose = False
    response = litellm.completion(
        model="gpt-3.5-turbo",
        messages=[{"role": "system", "content": "say GM - we're going to make it "}],
        stream=True,
        stream_options={"include_usage": True},
        max_tokens=10,
    )
    usage = None
    chunks = []
    for chunk in response:
        print("chunk: ", chunk)
        chunks.append(chunk)

    last_chunk = chunks[-1]
    print("last chunk: ", last_chunk)

    """
    Assert that:
    - Last Chunk includes Usage
    - All chunks prior to last chunk have usage=None
    """

    assert last_chunk.usage is not None
    assert last_chunk.usage.total_tokens > 0
    assert last_chunk.usage.prompt_tokens > 0
    assert last_chunk.usage.completion_tokens > 0

    # assert all non last chunks have usage=None
    assert all(chunk.usage is None for chunk in chunks[:-1])


def test_openai_stream_options_call_text_completion():
    litellm.set_verbose = False
    response = litellm.text_completion(
        model="gpt-3.5-turbo-instruct",
        prompt="say GM - we're going to make it ",
        stream=True,
        stream_options={"include_usage": True},
        max_tokens=10,
    )
    usage = None
    chunks = []
    for chunk in response:
        print("chunk: ", chunk)
        chunks.append(chunk)

    last_chunk = chunks[-1]
    print("last chunk: ", last_chunk)

    """
    Assert that:
    - Last Chunk includes Usage
    - All chunks prior to last chunk have usage=None
    """

    assert last_chunk.usage is not None
    assert last_chunk.usage.total_tokens > 0
    assert last_chunk.usage.prompt_tokens > 0
    assert last_chunk.usage.completion_tokens > 0

    # assert all non last chunks have usage=None
    assert all(chunk.usage is None for chunk in chunks[:-1])


def test_openai_text_completion_call():
    try:
        litellm.set_verbose = True
        response = completion(
            model="gpt-3.5-turbo-instruct", messages=messages, stream=True
        )
        complete_response = ""
        start_time = time.time()
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            print(f"chunk: {chunk}")
            complete_response += chunk
            if finished:
                break
            # print(f'complete_chunk: {complete_response}')
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"complete response: {complete_response}")
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass


# test_openai_text_completion_call()


# # test on together ai completion call - starcoder
def test_together_ai_completion_call_mistral():
    try:
        litellm.set_verbose = False
        start_time = time.time()
        response = completion(
            model="together_ai/mistralai/Mistral-7B-Instruct-v0.2",
            messages=messages,
            logger_fn=logger_fn,
            stream=True,
        )
        complete_response = ""
        print(f"returned response object: {response}")
        has_finish_reason = False
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            if finished:
                break
            complete_response += chunk
        if has_finish_reason is False:
            raise Exception("Finish reason not set for last chunk")
        if complete_response == "":
            raise Exception("Empty response received")
        print(f"complete response: {complete_response}")
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass


def test_together_ai_completion_call_starcoder_bad_key():
    try:
        api_key = "bad-key"
        start_time = time.time()
        response = completion(
            model="together_ai/bigcode/starcoder",
            messages=messages,
            stream=True,
            api_key=api_key,
        )
        complete_response = ""
        has_finish_reason = False
        for idx, chunk in enumerate(response):
            chunk, finished = streaming_format_tests(idx, chunk)
            has_finish_reason = finished
            if finished:
                break
            complete_response += chunk
        if has_finish_reason is False:
            raise Exception("Finish reason not set for last chunk")
        if complete_response == "":
            raise Exception("Empty response received")
        print(f"complete response: {complete_response}")
    except BadRequestError as e:
        pass
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass


# test_together_ai_completion_call_starcoder_bad_key()
#### Test Function calling + streaming ####


def test_completion_openai_with_functions():
    function1 = [
        {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        }
    ]
    try:
        litellm.set_verbose = False
        response = completion(
            model="gpt-3.5-turbo-1106",
            messages=[{"role": "user", "content": "what's the weather in SF"}],
            functions=function1,
            stream=True,
        )
        # Add any assertions here to check the response
        print(response)
        for chunk in response:
            print(chunk)
            if chunk["choices"][0]["finish_reason"] == "stop":
                break
            print(chunk["choices"][0]["finish_reason"])
            print(chunk["choices"][0]["delta"]["content"])
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


# test_completion_openai_with_functions()
#### Test Async streaming ####


# # test on ai21 completion call
async def ai21_async_completion_call():
    try:
        response = completion(
            model="j2-ultra", messages=messages, stream=True, logger_fn=logger_fn
        )
        print(f"response: {response}")
        complete_response = ""
        start_time = time.time()
        # Change for loop to async for loop
        idx = 0
        async for chunk in response:
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            complete_response += chunk
            idx += 1
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"complete response: {complete_response}")
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass


# asyncio.run(ai21_async_completion_call())


async def completion_call():
    try:
        response = completion(
            model="gpt-3.5-turbo",
            messages=messages,
            stream=True,
            logger_fn=logger_fn,
            max_tokens=10,
        )
        print(f"response: {response}")
        complete_response = ""
        start_time = time.time()
        # Change for loop to async for loop
        idx = 0
        async for chunk in response:
            chunk, finished = streaming_format_tests(idx, chunk)
            if finished:
                break
            complete_response += chunk
            idx += 1
        if complete_response.strip() == "":
            raise Exception("Empty response received")
        print(f"complete response: {complete_response}")
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass


# asyncio.run(completion_call())

#### Test Function Calling + Streaming ####

final_openai_function_call_example = {
    "id": "chatcmpl-7zVNA4sXUftpIg6W8WlntCyeBj2JY",
    "object": "chat.completion",
    "created": 1694892960,
    "model": "gpt-3.5-turbo-0613",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": None,
                "function_call": {
                    "name": "get_current_weather",
                    "arguments": '{\n  "location": "Boston, MA"\n}',
                },
            },
            "finish_reason": "function_call",
        }
    ],
    "usage": {"prompt_tokens": 82, "completion_tokens": 18, "total_tokens": 100},
}

function_calling_output_structure = {
    "id": str,
    "object": str,
    "created": int,
    "model": str,
    "choices": [
        {
            "index": int,
            "message": {
                "role": str,
                "content": (type(None), str),
                "function_call": {"name": str, "arguments": str},
            },
            "finish_reason": str,
        }
    ],
    "usage": {"prompt_tokens": int, "completion_tokens": int, "total_tokens": int},
}


def validate_final_structure(item, structure=function_calling_output_structure):
    if isinstance(item, list):
        if not all(validate_final_structure(i, structure[0]) for i in item):
            return Exception(
                "Function calling final output doesn't match expected output format"
            )
    elif isinstance(item, dict):
        if not all(
            k in item and validate_final_structure(item[k], v)
            for k, v in structure.items()
        ):
            return Exception(
                "Function calling final output doesn't match expected output format"
            )
    else:
        if not isinstance(item, structure):
            return Exception(
                "Function calling final output doesn't match expected output format"
            )
    return True


first_openai_function_call_example = {
    "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0",
    "object": "chat.completion.chunk",
    "created": 1694893248,
    "model": "gpt-3.5-turbo-0613",
    "choices": [
        {
            "index": 0,
            "delta": {
                "role": "assistant",
                "content": None,
                "function_call": {"name": "get_current_weather", "arguments": ""},
            },
            "finish_reason": None,
        }
    ],
}


def validate_first_function_call_chunk_structure(item):
    if not (isinstance(item, dict) or isinstance(item, litellm.ModelResponse)):
        raise Exception(f"Incorrect format, type of item: {type(item)}")

    required_keys = {"id", "object", "created", "model", "choices"}
    for key in required_keys:
        if key not in item:
            raise Exception("Incorrect format")

    if not isinstance(item["choices"], list) or not item["choices"]:
        raise Exception("Incorrect format")

    required_keys_in_choices_array = {"index", "delta", "finish_reason"}
    for choice in item["choices"]:
        if not (
            isinstance(choice, dict)
            or isinstance(choice, litellm.utils.StreamingChoices)
        ):
            raise Exception(f"Incorrect format, type of choice: {type(choice)}")
        for key in required_keys_in_choices_array:
            if key not in choice:
                raise Exception("Incorrect format")

        if not (
            isinstance(choice["delta"], dict)
            or isinstance(choice["delta"], litellm.utils.Delta)
        ):
            raise Exception(
                f"Incorrect format, type of choice: {type(choice['delta'])}"
            )

        required_keys_in_delta = {"role", "content", "function_call"}
        for key in required_keys_in_delta:
            if key not in choice["delta"]:
                raise Exception("Incorrect format")

        if not (
            isinstance(choice["delta"]["function_call"], dict)
            or isinstance(choice["delta"]["function_call"], BaseModel)
        ):
            raise Exception(
                f"Incorrect format, type of function call: {type(choice['delta']['function_call'])}"
            )

        required_keys_in_function_call = {"name", "arguments"}
        for key in required_keys_in_function_call:
            if not hasattr(choice["delta"]["function_call"], key):
                raise Exception(
                    f"Incorrect format, expected key={key};  actual keys: {choice['delta']['function_call']}, eval: {hasattr(choice['delta']['function_call'], key)}"
                )

    return True


second_function_call_chunk_format = {
    "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0",
    "object": "chat.completion.chunk",
    "created": 1694893248,
    "model": "gpt-3.5-turbo-0613",
    "choices": [
        {
            "index": 0,
            "delta": {"function_call": {"arguments": "{\n"}},
            "finish_reason": None,
        }
    ],
}


def validate_second_function_call_chunk_structure(data):
    if not isinstance(data, dict):
        raise Exception("Incorrect format")

    required_keys = {"id", "object", "created", "model", "choices"}
    for key in required_keys:
        if key not in data:
            raise Exception("Incorrect format")

    if not isinstance(data["choices"], list) or not data["choices"]:
        raise Exception("Incorrect format")

    required_keys_in_choices_array = {"index", "delta", "finish_reason"}
    for choice in data["choices"]:
        if not isinstance(choice, dict):
            raise Exception("Incorrect format")
        for key in required_keys_in_choices_array:
            if key not in choice:
                raise Exception("Incorrect format")

        if (
            "function_call" not in choice["delta"]
            or "arguments" not in choice["delta"]["function_call"]
        ):
            raise Exception("Incorrect format")

    return True


final_function_call_chunk_example = {
    "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0",
    "object": "chat.completion.chunk",
    "created": 1694893248,
    "model": "gpt-3.5-turbo-0613",
    "choices": [{"index": 0, "delta": {}, "finish_reason": "function_call"}],
}


def validate_final_function_call_chunk_structure(data):
    if not (isinstance(data, dict) or isinstance(data, litellm.ModelResponse)):
        raise Exception("Incorrect format")

    required_keys = {"id", "object", "created", "model", "choices"}
    for key in required_keys:
        if key not in data:
            raise Exception("Incorrect format")

    if not isinstance(data["choices"], list) or not data["choices"]:
        raise Exception("Incorrect format")

    required_keys_in_choices_array = {"index", "delta", "finish_reason"}
    for choice in data["choices"]:
        if not (
            isinstance(choice, dict) or isinstance(choice["delta"], litellm.utils.Delta)
        ):
            raise Exception("Incorrect format")
        for key in required_keys_in_choices_array:
            if key not in choice:
                raise Exception("Incorrect format")

    return True


def streaming_and_function_calling_format_tests(idx, chunk):
    extracted_chunk = ""
    finished = False
    print(f"idx: {idx}")
    print(f"chunk: {chunk}")
    decision = False
    if idx == 0:  # ensure role assistant is set
        decision = validate_first_function_call_chunk_structure(chunk)
        role = chunk["choices"][0]["delta"]["role"]
        assert role == "assistant"
    elif idx != 0:  # second chunk
        try:
            decision = validate_second_function_call_chunk_structure(data=chunk)
        except:  # check if it's the last chunk (returns an empty delta {} )
            decision = validate_final_function_call_chunk_structure(data=chunk)
            finished = True
    if "content" in chunk["choices"][0]["delta"]:
        extracted_chunk = chunk["choices"][0]["delta"]["content"]
    if decision == False:
        raise Exception("incorrect format")
    return extracted_chunk, finished


def test_openai_streaming_and_function_calling():
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]
    messages = [{"role": "user", "content": "What is the weather like in Boston?"}]
    try:
        response = completion(
            model="gpt-3.5-turbo",
            tools=tools,
            messages=messages,
            stream=True,
        )
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            if idx == 0:
                assert (
                    chunk.choices[0].delta.tool_calls[0].function.arguments is not None
                )
                assert isinstance(
                    chunk.choices[0].delta.tool_calls[0].function.arguments, str
                )
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")
        raise e


# test_azure_streaming_and_function_calling()


def test_success_callback_streaming():
    def success_callback(kwargs, completion_response, start_time, end_time):
        print(
            {
                "success": True,
                "input": kwargs,
                "output": completion_response,
                "start_time": start_time,
                "end_time": end_time,
            }
        )

    litellm.success_callback = [success_callback]

    messages = [{"role": "user", "content": "hello"}]
    print("TESTING LITELLM COMPLETION CALL")
    response = litellm.completion(
        model="gpt-3.5-turbo",
        messages=messages,
        stream=True,
        max_tokens=5,
    )
    print(response)

    for chunk in response:
        print(chunk["choices"][0])


# test_success_callback_streaming()

#### STREAMING + FUNCTION CALLING ###
from pydantic import BaseModel
from typing import List, Optional


class Function(BaseModel):
    name: str
    arguments: str


class ToolCalls(BaseModel):
    index: int
    id: str
    type: str
    function: Function


class Delta(BaseModel):
    role: str
    content: Optional[str] = None
    tool_calls: List[ToolCalls]


class Choices(BaseModel):
    index: int
    delta: Delta
    logprobs: Optional[str] = None
    finish_reason: Optional[str] = None


class Chunk(BaseModel):
    id: str
    object: str
    created: int
    model: str
    # system_fingerprint: str
    choices: List[Choices]


def validate_first_streaming_function_calling_chunk(chunk: ModelResponse):
    chunk_instance = Chunk(**chunk.model_dump())


### Chunk 1


# {
#     "id": "chatcmpl-8vdVjtzxc0JqGjq93NxC79dMp6Qcs",
#     "object": "chat.completion.chunk",
#     "created": 1708747267,
#     "model": "gpt-3.5-turbo-0125",
#     "system_fingerprint": "fp_86156a94a0",
#     "choices": [
#         {
#             "index": 0,
#             "delta": {
#                 "role": "assistant",
#                 "content": null,
#                 "tool_calls": [
#                     {
#                         "index": 0,
#                         "id": "call_oN10vaaC9iA8GLFRIFwjCsN7",
#                         "type": "function",
#                         "function": {
#                             "name": "get_current_weather",
#                             "arguments": ""
#                         }
#                     }
#                 ]
#             },
#             "logprobs": null,
#             "finish_reason": null
#         }
#     ]
# }
class Function2(BaseModel):
    arguments: str


class ToolCalls2(BaseModel):
    index: int
    function: Optional[Function2] = None


class Delta2(BaseModel):
    tool_calls: List[ToolCalls2]


class Choices2(BaseModel):
    index: int
    delta: Delta2
    logprobs: Optional[str] = None
    finish_reason: Optional[str] = None


class Chunk2(BaseModel):
    id: str
    object: str
    created: int
    model: str
    system_fingerprint: str
    choices: List[Choices2]


## Chunk 2

# {
#     "id": "chatcmpl-8vdVjtzxc0JqGjq93NxC79dMp6Qcs",
#     "object": "chat.completion.chunk",
#     "created": 1708747267,
#     "model": "gpt-3.5-turbo-0125",
#     "system_fingerprint": "fp_86156a94a0",
#     "choices": [
#         {
#             "index": 0,
#             "delta": {
#                 "tool_calls": [
#                     {
#                         "index": 0,
#                         "function": {
#                             "arguments": "{\""
#                         }
#                     }
#                 ]
#             },
#             "logprobs": null,
#             "finish_reason": null
#         }
#     ]
# }


def validate_second_streaming_function_calling_chunk(chunk: ModelResponse):
    chunk_instance = Chunk2(**chunk.model_dump())


class Delta3(BaseModel):
    content: Optional[str] = None
    role: Optional[str] = None
    function_call: Optional[dict] = None
    tool_calls: Optional[List] = None


class Choices3(BaseModel):
    index: int
    delta: Delta3
    logprobs: Optional[str] = None
    finish_reason: str


class Chunk3(BaseModel):
    id: str
    object: str
    created: int
    model: str
    # system_fingerprint: str
    choices: List[Choices3]


def validate_final_streaming_function_calling_chunk(chunk: ModelResponse):
    chunk_instance = Chunk3(**chunk.model_dump())


def test_azure_streaming_and_function_calling():
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]
    messages = [{"role": "user", "content": "What is the weather like in Boston?"}]
    try:
        response = completion(
            model="azure/gpt-4-nov-release",
            tools=tools,
            tool_choice="auto",
            messages=messages,
            stream=True,
            api_base=os.getenv("AZURE_FRANCE_API_BASE"),
            api_key=os.getenv("AZURE_FRANCE_API_KEY"),
            api_version="2024-02-15-preview",
        )
        # Add any assertions here to check the response
        for idx, chunk in enumerate(response):
            print(f"chunk: {chunk}")
            if idx == 0:
                assert (
                    chunk.choices[0].delta.tool_calls[0].function.arguments is not None
                )
                assert isinstance(
                    chunk.choices[0].delta.tool_calls[0].function.arguments, str
                )
                validate_first_streaming_function_calling_chunk(chunk=chunk)
            elif idx == 1:
                validate_second_streaming_function_calling_chunk(chunk=chunk)
            elif chunk.choices[0].finish_reason is not None:  # last chunk
                validate_final_streaming_function_calling_chunk(chunk=chunk)

    except Exception as e:
        pytest.fail(f"Error occurred: {e}")
        raise e


@pytest.mark.asyncio
async def test_azure_astreaming_and_function_calling():
    import uuid

    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]
    messages = [
        {
            "role": "user",
            "content": f"What is the weather like in Boston? {uuid.uuid4()}",
        }
    ]
    from litellm.caching import Cache

    litellm.cache = Cache(
        type="redis",
        host=os.environ["REDIS_HOST"],
        port=os.environ["REDIS_PORT"],
        password=os.environ["REDIS_PASSWORD"],
    )
    try:
        response = await litellm.acompletion(
            model="azure/gpt-4-nov-release",
            tools=tools,
            tool_choice="auto",
            messages=messages,
            stream=True,
            api_base=os.getenv("AZURE_FRANCE_API_BASE"),
            api_key=os.getenv("AZURE_FRANCE_API_KEY"),
            api_version="2024-02-15-preview",
            caching=True,
        )
        # Add any assertions here to check the response
        idx = 0
        async for chunk in response:
            print(f"chunk: {chunk}")
            if idx == 0:
                assert (
                    chunk.choices[0].delta.tool_calls[0].function.arguments is not None
                )
                assert isinstance(
                    chunk.choices[0].delta.tool_calls[0].function.arguments, str
                )
                validate_first_streaming_function_calling_chunk(chunk=chunk)
            elif idx == 1:
                validate_second_streaming_function_calling_chunk(chunk=chunk)
            elif chunk.choices[0].finish_reason is not None:  # last chunk
                validate_final_streaming_function_calling_chunk(chunk=chunk)
            idx += 1

        ## CACHING TEST
        print("\n\nCACHING TESTS\n\n")
        response = await litellm.acompletion(
            model="azure/gpt-4-nov-release",
            tools=tools,
            tool_choice="auto",
            messages=messages,
            stream=True,
            api_base=os.getenv("AZURE_FRANCE_API_BASE"),
            api_key=os.getenv("AZURE_FRANCE_API_KEY"),
            api_version="2024-02-15-preview",
            caching=True,
        )
        # Add any assertions here to check the response
        idx = 0
        async for chunk in response:
            print(f"chunk: {chunk}")
            if idx == 0:
                assert (
                    chunk.choices[0].delta.tool_calls[0].function.arguments is not None
                )
                assert isinstance(
                    chunk.choices[0].delta.tool_calls[0].function.arguments, str
                )
                validate_first_streaming_function_calling_chunk(chunk=chunk)
            elif idx == 1 and chunk.choices[0].finish_reason is None:
                validate_second_streaming_function_calling_chunk(chunk=chunk)
            elif chunk.choices[0].finish_reason is not None:  # last chunk
                validate_final_streaming_function_calling_chunk(chunk=chunk)
            idx += 1
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")
        raise e


def test_completion_claude_3_function_call_with_streaming():
    litellm.set_verbose = True
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]
    messages = [
        {
            "role": "user",
            "content": "What's the weather like in Boston today in fahrenheit?",
        }
    ]
    try:
        # test without max tokens
        response = completion(
            model="claude-3-opus-20240229",
            messages=messages,
            tools=tools,
            tool_choice="auto",
            stream=True,
        )
        idx = 0
        for chunk in response:
            print(f"chunk in response: {chunk}")
            if idx == 0:
                assert (
                    chunk.choices[0].delta.tool_calls[0].function.arguments is not None
                )
                assert isinstance(
                    chunk.choices[0].delta.tool_calls[0].function.arguments, str
                )
                validate_first_streaming_function_calling_chunk(chunk=chunk)
            elif idx == 1 and chunk.choices[0].finish_reason is None:
                validate_second_streaming_function_calling_chunk(chunk=chunk)
            elif chunk.choices[0].finish_reason is not None:  # last chunk
                validate_final_streaming_function_calling_chunk(chunk=chunk)
            idx += 1
        # raise Exception("it worked!")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


@pytest.mark.asyncio
async def test_acompletion_claude_3_function_call_with_streaming():
    litellm.set_verbose = True
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]
    messages = [
        {
            "role": "user",
            "content": "What's the weather like in Boston today in fahrenheit?",
        }
    ]
    try:
        # test without max tokens
        response = await acompletion(
            model="claude-3-opus-20240229",
            messages=messages,
            tools=tools,
            tool_choice="auto",
            stream=True,
        )
        idx = 0
        print(f"response: {response}")
        async for chunk in response:
            # print(f"chunk: {chunk}")
            if idx == 0:
                assert (
                    chunk.choices[0].delta.tool_calls[0].function.arguments is not None
                )
                assert isinstance(
                    chunk.choices[0].delta.tool_calls[0].function.arguments, str
                )
                validate_first_streaming_function_calling_chunk(chunk=chunk)
            elif idx == 1 and chunk.choices[0].finish_reason is None:
                validate_second_streaming_function_calling_chunk(chunk=chunk)
            elif chunk.choices[0].finish_reason is not None:  # last chunk
                validate_final_streaming_function_calling_chunk(chunk=chunk)
            idx += 1
        # raise Exception("it worked! ")
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")


class ModelResponseIterator:
    def __init__(self, model_response):
        self.model_response = model_response
        self.is_done = False

    # Sync iterator
    def __iter__(self):
        return self

    def __next__(self):
        if self.is_done:
            raise StopIteration
        self.is_done = True
        return self.model_response

    # Async iterator
    def __aiter__(self):
        return self

    async def __anext__(self):
        if self.is_done:
            raise StopAsyncIteration
        self.is_done = True
        return self.model_response


class ModelResponseListIterator:
    def __init__(self, model_responses):
        self.model_responses = model_responses
        self.index = 0

    # Sync iterator
    def __iter__(self):
        return self

    def __next__(self):
        if self.index >= len(self.model_responses):
            raise StopIteration
        model_response = self.model_responses[self.index]
        self.index += 1
        return model_response

    # Async iterator
    def __aiter__(self):
        return self

    async def __anext__(self):
        if self.index >= len(self.model_responses):
            raise StopAsyncIteration
        model_response = self.model_responses[self.index]
        self.index += 1
        return model_response


def test_unit_test_custom_stream_wrapper():
    """
    Test if last streaming chunk ends with '?', if the message repeats itself.
    """
    litellm.set_verbose = False
    chunk = {
        "id": "chatcmpl-123",
        "object": "chat.completion.chunk",
        "created": 1694268190,
        "model": "gpt-3.5-turbo-0125",
        "system_fingerprint": "fp_44709d6fcb",
        "choices": [
            {"index": 0, "delta": {"content": "How are you?"}, "finish_reason": "stop"}
        ],
    }
    chunk = litellm.ModelResponse(**chunk, stream=True)

    completion_stream = ModelResponseIterator(model_response=chunk)

    response = litellm.CustomStreamWrapper(
        completion_stream=completion_stream,
        model="gpt-3.5-turbo",
        custom_llm_provider="cached_response",
        logging_obj=litellm.Logging(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": "Hey"}],
            stream=True,
            call_type="completion",
            start_time=time.time(),
            litellm_call_id="12345",
            function_id="1245",
        ),
    )

    freq = 0
    for chunk in response:
        if chunk.choices[0].delta.content is not None:
            if "How are you?" in chunk.choices[0].delta.content:
                freq += 1
    assert freq == 1


def test_aamazing_unit_test_custom_stream_wrapper_n():
    """
    Test if the translated output maps exactly to the received openai input

    Relevant issue: https://github.com/BerriAI/litellm/issues/3276
    """
    chunks = [
        {
            "id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
            "object": "chat.completion.chunk",
            "created": 1714075272,
            "model": "gpt-4-0613",
            "system_fingerprint": None,
            "choices": [
                {
                    "index": 0,
                    "delta": {"content": "It"},
                    "logprobs": {
                        "content": [
                            {
                                "token": "It",
                                "logprob": -1.5952516,
                                "bytes": [73, 116],
                                "top_logprobs": [
                                    {
                                        "token": "Brown",
                                        "logprob": -0.7358765,
                                        "bytes": [66, 114, 111, 119, 110],
                                    }
                                ],
                            }
                        ]
                    },
                    "finish_reason": None,
                }
            ],
        },
        {
            "id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
            "object": "chat.completion.chunk",
            "created": 1714075272,
            "model": "gpt-4-0613",
            "system_fingerprint": None,
            "choices": [
                {
                    "index": 1,
                    "delta": {"content": "Brown"},
                    "logprobs": {
                        "content": [
                            {
                                "token": "Brown",
                                "logprob": -0.7358765,
                                "bytes": [66, 114, 111, 119, 110],
                                "top_logprobs": [
                                    {
                                        "token": "Brown",
                                        "logprob": -0.7358765,
                                        "bytes": [66, 114, 111, 119, 110],
                                    }
                                ],
                            }
                        ]
                    },
                    "finish_reason": None,
                }
            ],
        },
        {
            "id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
            "object": "chat.completion.chunk",
            "created": 1714075272,
            "model": "gpt-4-0613",
            "system_fingerprint": None,
            "choices": [
                {
                    "index": 0,
                    "delta": {"content": "'s"},
                    "logprobs": {
                        "content": [
                            {
                                "token": "'s",
                                "logprob": -0.006786893,
                                "bytes": [39, 115],
                                "top_logprobs": [
                                    {
                                        "token": "'s",
                                        "logprob": -0.006786893,
                                        "bytes": [39, 115],
                                    }
                                ],
                            }
                        ]
                    },
                    "finish_reason": None,
                }
            ],
        },
        {
            "id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
            "object": "chat.completion.chunk",
            "created": 1714075272,
            "model": "gpt-4-0613",
            "system_fingerprint": None,
            "choices": [
                {
                    "index": 0,
                    "delta": {"content": " impossible"},
                    "logprobs": {
                        "content": [
                            {
                                "token": " impossible",
                                "logprob": -0.06528423,
                                "bytes": [
                                    32,
                                    105,
                                    109,
                                    112,
                                    111,
                                    115,
                                    115,
                                    105,
                                    98,
                                    108,
                                    101,
                                ],
                                "top_logprobs": [
                                    {
                                        "token": " impossible",
                                        "logprob": -0.06528423,
                                        "bytes": [
                                            32,
                                            105,
                                            109,
                                            112,
                                            111,
                                            115,
                                            115,
                                            105,
                                            98,
                                            108,
                                            101,
                                        ],
                                    }
                                ],
                            }
                        ]
                    },
                    "finish_reason": None,
                }
            ],
        },
        {
            "id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
            "object": "chat.completion.chunk",
            "created": 1714075272,
            "model": "gpt-4-0613",
            "system_fingerprint": None,
            "choices": [
                {
                    "index": 0,
                    "delta": {"content": "—even"},
                    "logprobs": {
                        "content": [
                            {
                                "token": "—even",
                                "logprob": -9999.0,
                                "bytes": [226, 128, 148, 101, 118, 101, 110],
                                "top_logprobs": [
                                    {
                                        "token": " to",
                                        "logprob": -0.12302828,
                                        "bytes": [32, 116, 111],
                                    }
                                ],
                            }
                        ]
                    },
                    "finish_reason": None,
                }
            ],
        },
        {
            "id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
            "object": "chat.completion.chunk",
            "created": 1714075272,
            "model": "gpt-4-0613",
            "system_fingerprint": None,
            "choices": [
                {"index": 0, "delta": {}, "logprobs": None, "finish_reason": "length"}
            ],
        },
        {
            "id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
            "object": "chat.completion.chunk",
            "created": 1714075272,
            "model": "gpt-4-0613",
            "system_fingerprint": None,
            "choices": [
                {"index": 1, "delta": {}, "logprobs": None, "finish_reason": "stop"}
            ],
        },
    ]

    litellm.set_verbose = True

    chunk_list = []
    for chunk in chunks:
        new_chunk = litellm.ModelResponse(stream=True, id=chunk["id"])
        if "choices" in chunk and isinstance(chunk["choices"], list):
            print("INSIDE CHUNK CHOICES!")
            new_choices = []
            for choice in chunk["choices"]:
                if isinstance(choice, litellm.utils.StreamingChoices):
                    _new_choice = choice
                elif isinstance(choice, dict):
                    _new_choice = litellm.utils.StreamingChoices(**choice)
                new_choices.append(_new_choice)
            new_chunk.choices = new_choices
        chunk_list.append(new_chunk)

    completion_stream = ModelResponseListIterator(model_responses=chunk_list)

    response = litellm.CustomStreamWrapper(
        completion_stream=completion_stream,
        model="gpt-4-0613",
        custom_llm_provider="cached_response",
        logging_obj=litellm.Logging(
            model="gpt-4-0613",
            messages=[{"role": "user", "content": "Hey"}],
            stream=True,
            call_type="completion",
            start_time=time.time(),
            litellm_call_id="12345",
            function_id="1245",
        ),
    )

    for idx, chunk in enumerate(response):
        chunk_dict = {}
        try:
            chunk_dict = chunk.model_dump(exclude_none=True)
        except:
            chunk_dict = chunk.dict(exclude_none=True)

        chunk_dict.pop("created")
        chunks[idx].pop("created")
        if chunks[idx]["system_fingerprint"] is None:
            chunks[idx].pop("system_fingerprint", None)
        if idx == 0:
            for choice in chunk_dict["choices"]:
                if "role" in choice["delta"]:
                    choice["delta"].pop("role")

        for choice in chunks[idx]["choices"]:
            # ignore finish reason None - since our pydantic object is set to exclude_none = true
            if "finish_reason" in choice and choice["finish_reason"] is None:
                choice.pop("finish_reason")
            if "logprobs" in choice and choice["logprobs"] is None:
                choice.pop("logprobs")

        assert (
            chunk_dict == chunks[idx]
        ), f"idx={idx} translated chunk = {chunk_dict} != openai chunk = {chunks[idx]}"