# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

from typing import *  # noqa: F403
import json

from botocore.client import BaseClient
from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat

from llama_models.llama3.api.tokenizer import Tokenizer

from llama_stack.providers.utils.inference.model_registry import (
    build_model_alias,
    ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
    OpenAICompatCompletionChoice,
    OpenAICompatCompletionResponse,
    process_chat_completion_response,
    process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
    chat_completion_request_to_prompt,
    content_has_media,
    interleaved_content_as_str,
)

from llama_stack.apis.inference import *  # noqa: F403

from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
from llama_stack.providers.utils.bedrock.client import create_bedrock_client


MODEL_ALIASES = [
    build_model_alias(
        "meta.llama3-1-8b-instruct-v1:0",
        CoreModelId.llama3_1_8b_instruct.value,
    ),
    build_model_alias(
        "meta.llama3-1-70b-instruct-v1:0",
        CoreModelId.llama3_1_70b_instruct.value,
    ),
    build_model_alias(
        "meta.llama3-1-405b-instruct-v1:0",
        CoreModelId.llama3_1_405b_instruct.value,
    ),
]


class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
    def __init__(self, config: BedrockConfig) -> None:
        ModelRegistryHelper.__init__(self, MODEL_ALIASES)
        self._config = config

        self._client = create_bedrock_client(config)
        self.formatter = ChatFormat(Tokenizer.get_instance())

    @property
    def client(self) -> BaseClient:
        return self._client

    async def initialize(self) -> None:
        pass

    async def shutdown(self) -> None:
        self.client.close()

    async def completion(
        self,
        model_id: str,
        content: InterleavedContent,
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        response_format: Optional[ResponseFormat] = None,
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
    ) -> AsyncGenerator:
        raise NotImplementedError()

    async def chat_completion(
        self,
        model_id: str,
        messages: List[Message],
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        response_format: Optional[ResponseFormat] = None,
        tools: Optional[List[ToolDefinition]] = None,
        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
    ) -> Union[
        ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
    ]:
        model = await self.model_store.get_model(model_id)
        request = ChatCompletionRequest(
            model=model.provider_resource_id,
            messages=messages,
            sampling_params=sampling_params,
            tools=tools or [],
            tool_choice=tool_choice,
            tool_prompt_format=tool_prompt_format,
            response_format=response_format,
            stream=stream,
            logprobs=logprobs,
        )

        if stream:
            return self._stream_chat_completion(request)
        else:
            return await self._nonstream_chat_completion(request)

    async def _nonstream_chat_completion(
        self, request: ChatCompletionRequest
    ) -> ChatCompletionResponse:
        params = await self._get_params_for_chat_completion(request)
        res = self.client.invoke_model(**params)
        chunk = next(res["body"])
        result = json.loads(chunk.decode("utf-8"))

        choice = OpenAICompatCompletionChoice(
            finish_reason=result["stop_reason"],
            text=result["generation"],
        )

        response = OpenAICompatCompletionResponse(choices=[choice])
        return process_chat_completion_response(response, self.formatter)

    async def _stream_chat_completion(
        self, request: ChatCompletionRequest
    ) -> AsyncGenerator:
        params = await self._get_params_for_chat_completion(request)
        res = self.client.invoke_model_with_response_stream(**params)
        event_stream = res["body"]

        async def _generate_and_convert_to_openai_compat():
            for chunk in event_stream:
                chunk = chunk["chunk"]["bytes"]
                result = json.loads(chunk.decode("utf-8"))
                choice = OpenAICompatCompletionChoice(
                    finish_reason=result["stop_reason"],
                    text=result["generation"],
                )
                yield OpenAICompatCompletionResponse(choices=[choice])

        stream = _generate_and_convert_to_openai_compat()
        async for chunk in process_chat_completion_stream_response(
            stream, self.formatter
        ):
            yield chunk

    async def _get_params_for_chat_completion(
        self, request: ChatCompletionRequest
    ) -> Dict:
        bedrock_model = request.model

        inference_config = {}
        param_mapping = {
            "max_tokens": "max_gen_len",
            "temperature": "temperature",
            "top_p": "top_p",
        }

        for k, v in param_mapping.items():
            if getattr(request.sampling_params, k):
                inference_config[v] = getattr(request.sampling_params, k)

        prompt = await chat_completion_request_to_prompt(
            request, self.get_llama_model(request.model), self.formatter
        )
        return {
            "modelId": bedrock_model,
            "body": json.dumps(
                {
                    "prompt": prompt,
                    **inference_config,
                }
            ),
        }

    async def embeddings(
        self,
        model_id: str,
        contents: List[InterleavedContent],
    ) -> EmbeddingsResponse:
        model = await self.model_store.get_model(model_id)
        embeddings = []
        for content in contents:
            assert not content_has_media(
                content
            ), "Bedrock does not support media for embeddings"
            input_text = interleaved_content_as_str(content)
            input_body = {"inputText": input_text}
            body = json.dumps(input_body)
            response = self.client.invoke_model(
                body=body,
                modelId=model.provider_resource_id,
                accept="application/json",
                contentType="application/json",
            )
            response_body = json.loads(response.get("body").read())
            embeddings.append(response_body.get("embedding"))
        return EmbeddingsResponse(embeddings=embeddings)