## Uses the huggingface text generation inference API
import os, copy, types
import json
from enum import Enum
import requests
import time
import litellm
from typing import Callable, Dict, List, Any
from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper
from typing import Optional
from .prompt_templates.factory import prompt_factory, custom_prompt

class HuggingfaceError(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

class HuggingfaceConfig(): 
    """
    Reference: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate 
    """
    best_of: Optional[int] = None
    decoder_input_details: Optional[bool] = None
    details: Optional[bool] = True # enables returning logprobs + best of
    max_new_tokens: Optional[int] = None
    repetition_penalty: Optional[float] = None
    return_full_text: Optional[bool] = False # by default don't return the input as part of the output
    seed: Optional[int] = None
    temperature: Optional[float] = None
    top_k: Optional[int] = None
    top_n_tokens: Optional[int] = None
    top_p: Optional[int] = None
    truncate: Optional[int] = None
    typical_p: Optional[float] = None
    watermark: Optional[bool] = None

    def __init__(self, 
                 best_of: Optional[int] = None, 
                 decoder_input_details: Optional[bool] = None,
                 details: Optional[bool] = None,
                 max_new_tokens: Optional[int] = None,
                 repetition_penalty: Optional[float] = None,
                 return_full_text: Optional[bool] = None,
                 seed: Optional[int] = None,
                 temperature: Optional[float] = None, 
                 top_k: Optional[int] = None,
                 top_n_tokens: Optional[int] = None, 
                 top_p: Optional[int] = None,
                 truncate: Optional[int] = None,
                 typical_p: Optional[float] = None,
                 watermark: Optional[bool] = None
                 ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != 'self' and value is not None:
                setattr(self.__class__, key, value)
   
    @classmethod
    def get_config(cls):
        return {k: v for k, v in cls.__dict__.items() 
                if not k.startswith('__') 
                and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) 
                and v is not None}

def validate_environment(api_key, headers):
    default_headers = {
        "content-type": "application/json",
    }
    if api_key and headers is None:
        default_headers["Authorization"] = f"Bearer {api_key}" # Huggingface Inference Endpoint default is to accept bearer tokens
        headers = default_headers
    elif headers:
        headers=headers
    else: 
        headers = default_headers
    return headers

def output_parser(generated_text: str): 
    """
    Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens. 

    Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763
    """
    chat_template_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "<s>", "</s>"]
    for token in chat_template_tokens: 
        if generated_text.strip().startswith(token):
            generated_text = generated_text.replace(token, "", 1)
        if generated_text.endswith(token):
            generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
    return generated_text
            


tgi_models_cache = None
conv_models_cache = None
def read_tgi_conv_models():
    try:
        global tgi_models_cache, conv_models_cache
        # Check if the cache is already populated
        # so we don't keep on reading txt file if there are 1k requests
        if (tgi_models_cache is not None) and (conv_models_cache is not None):
            return tgi_models_cache, conv_models_cache
        # If not, read the file and populate the cache
        tgi_models = set()
        script_directory = os.path.dirname(os.path.abspath(__file__))
        # Construct the file path relative to the script's directory
        file_path = os.path.join(script_directory, "huggingface_llms_metadata", "hf_text_generation_models.txt")

        with open(file_path, 'r') as file:
            for line in file:
                tgi_models.add(line.strip())
        
        # Cache the set for future use
        tgi_models_cache = tgi_models
        
        # If not, read the file and populate the cache
        file_path = os.path.join(script_directory, "huggingface_llms_metadata", "hf_conversational_models.txt")
        conv_models = set()
        with open(file_path, 'r') as file:
            for line in file:
                conv_models.add(line.strip())
        # Cache the set for future use
        conv_models_cache = conv_models        
        return tgi_models, conv_models
    except:
        return set(), set()


def get_hf_task_for_model(model):
    # read text file, cast it to set 
    # read the file called "huggingface_llms_metadata/hf_text_generation_models.txt"
    tgi_models, conversational_models = read_tgi_conv_models()
    if model in tgi_models:
        return "text-generation-inference"
    elif model in conversational_models:
        return "conversational"
    elif "roneneldan/TinyStories" in model:
        return None
    else:
        return "text-generation-inference" # default to tgi

def completion(
    model: str,
    messages: list,
    api_base: Optional[str],
    headers: Optional[dict],
    model_response: ModelResponse,
    print_verbose: Callable,
    encoding,
    api_key,
    logging_obj,
    custom_prompt_dict={},
    optional_params=None,
    litellm_params=None,
    logger_fn=None,
):
    exception_mapping_worked = False
    try:
        headers = validate_environment(api_key, headers)
        task = get_hf_task_for_model(model)
        print_verbose(f"{model}, {task}")
        completion_url = ""
        input_text = None
        if "https" in model:
            completion_url = model
        elif api_base:
            completion_url = api_base
        elif "HF_API_BASE" in os.environ:
            completion_url = os.getenv("HF_API_BASE", "")
        elif "HUGGINGFACE_API_BASE" in os.environ:
            completion_url = os.getenv("HUGGINGFACE_API_BASE", "")
        else:
            completion_url = f"https://api-inference.huggingface.co/models/{model}"

        ## Load Config
        config=litellm.HuggingfaceConfig.get_config()
        for k, v in config.items():
            if k not in optional_params: # completion(top_k=3) > huggingfaceConfig(top_k=3) <- allows for dynamic variables to be passed in
                optional_params[k] = v

        ### MAP INPUT PARAMS
        if task == "conversational":
            inference_params = copy.deepcopy(optional_params)
            inference_params.pop("details")
            inference_params.pop("return_full_text")
            past_user_inputs = []
            generated_responses = []
            text = ""
            for message in messages:
                if message["role"] == "user":
                    if text != "":
                        past_user_inputs.append(text)
                    text = message["content"]
                elif message["role"] == "assistant" or message["role"] == "system":
                    generated_responses.append(message["content"])
            data = {
                "inputs": {
                    "text": text, 
                    "past_user_inputs": past_user_inputs, 
                    "generated_responses": generated_responses
                },
                "parameters": inference_params
            }
            input_text = "".join(message["content"] for message in messages)
        elif task == "text-generation-inference":
            # always send "details" and "return_full_text" as params
            if model in custom_prompt_dict:
                # check if the model has a registered custom prompt
                model_prompt_details = custom_prompt_dict[model]
                prompt = custom_prompt(
                    role_dict=model_prompt_details.get("roles", None), 
                    initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),  
                    final_prompt_value=model_prompt_details.get("final_prompt_value", ""), 
                    messages=messages
                )
            else:
                prompt = prompt_factory(model=model, messages=messages)
            data = {
                "inputs": prompt,
                "parameters": optional_params,
                "stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
            }
            input_text = prompt
        else:
            # Non TGI and Conversational llms
            # We need this branch, it removes 'details' and 'return_full_text' from params 
            if model in custom_prompt_dict:
                # check if the model has a registered custom prompt
                model_prompt_details = custom_prompt_dict[model]
                prompt = custom_prompt(
                    role_dict=model_prompt_details.get("roles", {}), 
                    initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),  
                    final_prompt_value=model_prompt_details.get("final_prompt_value", ""), 
                    bos_token=model_prompt_details.get("bos_token", ""),
                    eos_token=model_prompt_details.get("eos_token", ""),
                    messages=messages,
                )
            else:
                prompt = prompt_factory(model=model, messages=messages)
            inference_params = copy.deepcopy(optional_params)
            inference_params.pop("details")
            inference_params.pop("return_full_text")
            data = {
                "inputs": prompt,
                "parameters": inference_params,
                "stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
            }
            input_text = prompt
        ## LOGGING
        logging_obj.pre_call(
                input=input_text,
                api_key=api_key,
                additional_args={"complete_input_dict": data, "task": task, "headers": headers},
            )
        ## COMPLETION CALL
        if "stream" in optional_params and optional_params["stream"] == True:
            response = requests.post(
                completion_url, 
                headers=headers, 
                data=json.dumps(data), 
                stream=optional_params["stream"]
            )
            return response.iter_lines()
        else:
            response = requests.post(
                completion_url, 
                headers=headers, 
                data=json.dumps(data)
            )


            ## Some servers might return streaming responses even though stream was not set to true. (e.g. Baseten)
            is_streamed = False 
            if response.__dict__['headers'].get("Content-Type", "") == "text/event-stream":
                is_streamed = True
            
            # iterate over the complete streamed response, and return the final answer
            if is_streamed:
                streamed_response = CustomStreamWrapper(completion_stream=response.iter_lines(), model=model, custom_llm_provider="huggingface", logging_obj=logging_obj)
                content = ""
                for chunk in streamed_response: 
                    content += chunk["choices"][0]["delta"]["content"]
                completion_response: List[Dict[str, Any]] = [{"generated_text": content}]
                ## LOGGING
                logging_obj.post_call(
                    input=input_text,
                    api_key=api_key,
                    original_response=completion_response,
                    additional_args={"complete_input_dict": data, "task": task},
                )
            else: 
                ## LOGGING
                logging_obj.post_call(
                    input=input_text,
                    api_key=api_key,
                    original_response=response.text,
                    additional_args={"complete_input_dict": data, "task": task},
                )
                ## RESPONSE OBJECT
                try:
                    completion_response = response.json()
                except:
                    import traceback
                    raise HuggingfaceError(
                        message=f"Original Response received: {response.text}; Stacktrace: {traceback.format_exc()}", status_code=response.status_code
                    )
            print_verbose(f"response: {completion_response}")
            if isinstance(completion_response, dict) and "error" in completion_response:
                print_verbose(f"completion error: {completion_response['error']}")
                print_verbose(f"response.status_code: {response.status_code}")
                raise HuggingfaceError(
                    message=completion_response["error"],
                    status_code=response.status_code,
                )
            else:
                if task == "conversational": 
                    if len(completion_response["generated_text"]) > 0: # type: ignore
                        model_response["choices"][0]["message"][
                            "content"
                        ] = completion_response["generated_text"] # type: ignore
                elif task == "text-generation-inference": 
                    if len(completion_response[0]["generated_text"]) > 0: 
                        model_response["choices"][0]["message"][
                            "content"
                        ] = output_parser(completion_response[0]["generated_text"])
                    ## GETTING LOGPROBS + FINISH REASON 
                    if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]:
                        model_response.choices[0].finish_reason = completion_response[0]["details"]["finish_reason"]
                        sum_logprob = 0
                        for token in completion_response[0]["details"]["tokens"]:
                            if token["logprob"] != None:
                                sum_logprob += token["logprob"]
                        model_response["choices"][0]["message"]._logprob = sum_logprob
                    if "best_of" in optional_params and optional_params["best_of"] > 1: 
                        if "details" in completion_response[0] and "best_of_sequences" in completion_response[0]["details"]:
                            choices_list = []
                            for idx, item in enumerate(completion_response[0]["details"]["best_of_sequences"]):
                                sum_logprob = 0
                                for token in item["tokens"]:
                                    if token["logprob"] != None:
                                        sum_logprob += token["logprob"]
                                if len(item["generated_text"]) > 0: 
                                    message_obj = Message(content=output_parser(item["generated_text"]), logprobs=sum_logprob)
                                else: 
                                    message_obj = Message(content=None)
                                choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj)
                                choices_list.append(choice_obj)
                            model_response["choices"].extend(choices_list)
                else:
                    if len(completion_response[0]["generated_text"]) > 0: 
                        model_response["choices"][0]["message"][
                            "content"
                        ] = output_parser(completion_response[0]["generated_text"])
            ## CALCULATING USAGE
            prompt_tokens = 0
            try:
                prompt_tokens = len(
                    encoding.encode(input_text)
                )  ##[TODO] use the llama2 tokenizer here
            except:
                # this should remain non blocking we should not block a response returning if calculating usage fails
                pass
            print_verbose(f'output: {model_response["choices"][0]["message"]}')
            output_text = model_response["choices"][0]["message"].get("content", "")
            if output_text is not None and len(output_text) > 0:
                completion_tokens = 0
                try:
                    completion_tokens = len(
                        encoding.encode(model_response["choices"][0]["message"].get("content", ""))
                    )  ##[TODO] use the llama2 tokenizer here
                except:
                    # this should remain non blocking we should not block a response returning if calculating usage fails
                    pass
            else: 
                completion_tokens = 0

            model_response["created"] = time.time()
            model_response["model"] = model
            model_response.usage.completion_tokens = completion_tokens
            model_response.usage.prompt_tokens = prompt_tokens
            model_response.usage.total_tokens = prompt_tokens + completion_tokens
            model_response._hidden_params["original_response"] = completion_response
            return model_response
    except HuggingfaceError as e: 
        exception_mapping_worked = True
        raise e
    except Exception as e: 
        if exception_mapping_worked: 
            raise e
        else: 
            import traceback
            raise HuggingfaceError(status_code=500, message=traceback.format_exc())


def embedding(
    model: str,
    input: list,
    api_key: Optional[str] = None,
    api_base: Optional[str] = None,
    logging_obj=None,
    model_response=None,
    encoding=None,
):
    headers = validate_environment(api_key, headers=None)
    # print_verbose(f"{model}, {task}")
    embed_url = ""
    if "https" in model:
        embed_url = model
    elif api_base:
        embed_url = api_base
    elif "HF_API_BASE" in os.environ:
        embed_url = os.getenv("HF_API_BASE", "")
    elif "HUGGINGFACE_API_BASE" in os.environ:
        embed_url = os.getenv("HUGGINGFACE_API_BASE", "")
    else:
        embed_url = f"https://api-inference.huggingface.co/models/{model}"
    
    if "sentence-transformers" in model: 
        if len(input) == 0: 
            raise HuggingfaceError(status_code=400, message="sentence transformers requires 2+ sentences")
        data = {
            "inputs": {
                "source_sentence": input[0], 
                "sentences": [ "That is a happy dog", "That is a very happy person", "Today is a sunny day" ]
            }
        }
    else:
        data = {
            "inputs": input # type: ignore
        }
    
    ## LOGGING
    logging_obj.pre_call(
            input=input,
            api_key=api_key,
            additional_args={"complete_input_dict": data},
        )
    ## COMPLETION CALL
    response = requests.post(
        embed_url, headers=headers, data=json.dumps(data)
    )

  
    ## LOGGING
    logging_obj.post_call(
            input=input,
            api_key=api_key,
            additional_args={"complete_input_dict": data},
            original_response=response,
        )


    embeddings = response.json()

    if "error" in embeddings: 
        raise HuggingfaceError(status_code=500, message=embeddings['error'])
    
    output_data = []
    if "similarities" in embeddings: 
        for idx, embedding in embeddings["similarities"]:
            output_data.append(
            {
                "object": "embedding",
                "index": idx,
                "embedding": embedding # flatten list returned from hf
            }
        )
    else: 
        for idx, embedding in enumerate(embeddings):
            if isinstance(embedding, float): 
                output_data.append(
                    {
                        "object": "embedding",
                        "index": idx,
                        "embedding": embedding # flatten list returned from hf
                    }
                )
            else: 
                output_data.append(
                    {
                        "object": "embedding",
                        "index": idx,
                        "embedding": embedding[0][0] # flatten list returned from hf
                    }
                )
    model_response["object"] = "list"
    model_response["data"] = output_data
    model_response["model"] = model
    input_tokens = 0
    for text in input:
        input_tokens+=len(encoding.encode(text)) 

    model_response["usage"] = { 
        "prompt_tokens": input_tokens, 
        "total_tokens": input_tokens,
    }
    return model_response


    
