import os
import json
from enum import Enum
import requests
import time
from typing import Callable
from litellm.utils import ModelResponse

class NLPCloudError(Exception):
    def __init__(self, status_code, message):
        self.status_code = status_code
        self.message = message
        super().__init__(
            self.message
        )  # Call the base class constructor with the parameters it needs

def validate_environment(api_key):
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
    }
    if api_key:
        headers["Authorization"] = f"Token {api_key}"
    return headers

def completion(
    model: str,
    messages: list,
    model_response: ModelResponse,
    print_verbose: Callable,
    encoding,
    api_key,
    logging_obj,
    optional_params=None,
    litellm_params=None,
    logger_fn=None,
    default_max_tokens_to_sample=None,
):
    headers = validate_environment(api_key)
    completion_url_fragment_1 = "https://api.nlpcloud.io/v1/gpu/"
    completion_url_fragment_2 = "/generation"
    model = model
    text = " ".join(message["content"] for message in messages)

    data = {
        "text": text,
        **optional_params,
    }

    completion_url = completion_url_fragment_1 + model + completion_url_fragment_2
    ## LOGGING
    logging_obj.pre_call(
            input=text,
            api_key=api_key,
            additional_args={"complete_input_dict": data},
        )
    ## COMPLETION CALL
    response = requests.post(
        completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
    )
    if "stream" in optional_params and optional_params["stream"] == True:
        return response.iter_lines()
    else:
        ## LOGGING
        logging_obj.post_call(
                input=text,
                api_key=api_key,
                original_response=response.text,
                additional_args={"complete_input_dict": data},
            )
        print_verbose(f"raw model_response: {response.text}")
        ## RESPONSE OBJECT
        try:
            completion_response = response.json()
        except:
            raise NLPCloudError(message=response.text, status_code=response.status_code)
        if "error" in completion_response:
            raise NLPCloudError(
                message=completion_response["error"],
                status_code=response.status_code,
            )
        else:
            try:
                model_response["choices"][0]["message"]["content"] = completion_response["generated_text"]
            except:
                raise NLPCloudError(message=json.dumps(completion_response), status_code=response.status_code)

        ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. 
        prompt_tokens = completion_response["nb_input_tokens"]
        completion_tokens = completion_response["nb_generated_tokens"]

        model_response["created"] = time.time()
        model_response["model"] = model
        model_response["usage"] = {
            "prompt_tokens": prompt_tokens,
            "completion_tokens": completion_tokens,
            "total_tokens": prompt_tokens + completion_tokens,
        }
        return model_response

def embedding():
    # logic for parsing in - calling - parsing out model embedding calls
    pass