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

class HuggingfaceError(Exception):
    def __init__(self, status_code, message, request: Optional[httpx.Request]=None, response: Optional[httpx.Response]=None):
        self.status_code = status_code
        self.message = message
        if request is not None:
            self.request = request
        else: 
            self.request = httpx.Request(method="POST", url="https://api-inference.huggingface.co/models")
        if response is not None:
            self.response = response
        else: 
            self.response = httpx.Response(status_code=status_code, request=self.request)
        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 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

class Huggingface(BaseLLM): 
    _client_session: Optional[httpx.Client] = None
    _aclient_session: Optional[httpx.AsyncClient] = None

    def __init__(self) -> None:
        super().__init__()

    def validate_environment(self, 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 convert_to_model_response_object(self, 
                                         completion_response, 
                                         model_response, 
                                         task, 
                                         optional_params, 
                                         encoding, 
                                         input_text,
                                         model):
        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
        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"] = int(time.time())
        model_response["model"] = model
        usage = Usage(
            prompt_tokens=prompt_tokens,
            completion_tokens=completion_tokens,
            total_tokens=prompt_tokens + completion_tokens
        )
        model_response.usage = usage
        model_response._hidden_params["original_response"] = completion_response
        return model_response

    def completion(self,
        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={},
        acompletion: bool = False,
        optional_params=None,
        litellm_params=None,
        logger_fn=None,
    ):
        super().completion()
        exception_mapping_worked = False
        try:
            headers = self.validate_environment(api_key, headers)
            task = get_hf_task_for_model(model)
            print_verbose(f"{model}, {task}")
            completion_url = ""
            input_text = ""
            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, "api_base": completion_url, "acompletion": acompletion},
                )
            ## COMPLETION CALL
            if acompletion is True: 
                ### ASYNC STREAMING 
                if optional_params.get("stream", False):
                    return self.async_streaming(logging_obj=logging_obj, api_base=completion_url, data=data, headers=headers, model_response=model_response, model=model) # type: ignore
                else:
                    ### ASYNC COMPLETION
                    return self.acompletion(api_base=completion_url, data=data, headers=headers, model_response=model_response, task=task, encoding=encoding, input_text=input_text, model=model, optional_params=optional_params) # type: ignore
            ### SYNC STREAMING
            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()
            ### SYNC COMPLETION
            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()
                        if isinstance(completion_response, dict): 
                            completion_response = [completion_response]
                    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,
                    )
                return self.convert_to_model_response_object(
                    completion_response=completion_response,
                    model_response=model_response,
                    task=task,
                    optional_params=optional_params,
                    encoding=encoding,
                    input_text=input_text,
                    model=model
                )
        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())

    async def acompletion(self, 
                          api_base: str, 
                          data: dict, 
                          headers: dict, 
                          model_response: ModelResponse,
                          task: str,
                          encoding: Any,
                          input_text: str,
                          model: str,
                          optional_params: dict): 
       response = None
       try:
            async with httpx.AsyncClient() as client:
                response = await client.post(url=api_base, json=data, headers=headers, timeout=None) 
                response_json = response.json()
                if response.status_code != 200:
                    raise HuggingfaceError(status_code=response.status_code, message=response.text, request=response.request, response=response)
                
                ## RESPONSE OBJECT
                return self.convert_to_model_response_object(completion_response=response_json, 
                                                            model_response=model_response,
                                                            task=task,
                                                            encoding=encoding,
                                                            input_text=input_text,
                                                            model=model,
                                                            optional_params=optional_params)
       except Exception as e: 
           if isinstance(e,httpx.TimeoutException):
                raise HuggingfaceError(status_code=500, message="Request Timeout Error")
           elif response is not None and hasattr(response, "text"):
                raise HuggingfaceError(status_code=500, message=f"{str(e)}\n\nOriginal Response: {response.text}")
           else: 
                raise HuggingfaceError(status_code=500, message=f"{str(e)}")

    async def async_streaming(self, 
                          logging_obj,
                          api_base: str, 
                          data: dict, 
                          headers: dict, 
                          model_response: ModelResponse, 
                          model: str):
        async with httpx.AsyncClient() as client:
            response = client.stream(
                "POST",
                url=f"{api_base}",
                json=data,
                headers=headers
            ) 
            async with response as r: 
                if r.status_code != 200:
                    raise HuggingfaceError(status_code=r.status_code, message="An error occurred while streaming")
                
                streamwrapper = CustomStreamWrapper(completion_stream=r.aiter_lines(), model=model, custom_llm_provider="huggingface",logging_obj=logging_obj)
                async for transformed_chunk in streamwrapper:
                    yield transformed_chunk

    def embedding(self,
        model: str,
        input: list,
        api_key: Optional[str] = None,
        api_base: Optional[str] = None,
        logging_obj=None,
        model_response=None,
        encoding=None,
    ):
        super().embedding()
        headers = self.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