

import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch


lowercase_       		=  logging.get_logger(__name__)


class  A						:







    """simple docstring"""





    def __init__(      self :       Optional[int],lowercase_ :       Tuple = None,lowercase_ :       List[str] = None,lowercase_ :       Any=None,lowercase_ :       str=None		)->    Any:





        '''simple docstring'''







        if not conversation_id:
            A__          =   uuid.uuida()
        if past_user_inputs is None:
            A__          =   []
        if generated_responses is None:
            A__          =   []

        A__          =   conversation_id
        A__          =   past_user_inputs
        A__          =   generated_responses
        A__          =   text


    def __eq__(      self :       Any,lowercase_ :       Dict		)->    List[Any]:





        '''simple docstring'''







        if not isinstance(lowercase_,lowercase_		):
            return False
        if self.uuid == other.uuid:
            return True
        return (
            self.new_user_input == other.new_user_input
            and self.past_user_inputs == other.past_user_inputs
            and self.generated_responses == other.generated_responses
        )


    def 						snake_case__     (      self :       str,lowercase_ :       str,lowercase_ :       Tuple = False		)->    List[Any]:





        '''simple docstring'''







        if self.new_user_input:
            if overwrite:
                logger.warning(
                    F'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
                    F'with: "{text}".'		)
                A__          =   text
            else:
                logger.warning(
                    F'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
                    F'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'		)
        else:
            A__          =   text


    def 						snake_case__     (      self :       int		)->    Tuple:





        '''simple docstring'''







        if self.new_user_input:
            self.past_user_inputs.append(self.new_user_input		)
        A__          =   None


    def 						snake_case__     (      self :       List[Any],lowercase_ :       str		)->    Optional[Any]:





        '''simple docstring'''







        self.generated_responses.append(lowercase_		)


    def 						snake_case__     (      self :       Any		)->    Tuple:





        '''simple docstring'''







        for user_input, generated_response in zip(self.past_user_inputs,self.generated_responses		):
            yield True, user_input
            yield False, generated_response
        if self.new_user_input:
            yield True, self.new_user_input


    def __repr__(      self :       List[str]		)->    Any:





        '''simple docstring'''







        A__          =   F'Conversation id: {self.uuid} \n'
        for is_user, text in self.iter_texts():
            A__          =   '''user''' if is_user else '''bot'''
            output += F'{name} >> {text} \n'
        return output




@add_end_docstrings(
    _UpperCAmelCase    ,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '    ,							)
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def __init__(      self :       Any,*lowercase_ :       List[str],**lowercase_ :       str		)->    str:





        '''simple docstring'''







        super().__init__(*lowercase_,**lowercase_		)
        if self.tokenizer.pad_token_id is None:
            A__          =   self.tokenizer.eos_token


    def 						snake_case__     (      self :       Any,lowercase_ :       Tuple=None,lowercase_ :       Optional[Any]=None,lowercase_ :       Optional[int]=None,**lowercase_ :       Optional[int]		)->    Tuple:





        '''simple docstring'''







        A__          =   {}
        A__          =   {}
        A__          =   {}

        if min_length_for_response is not None:
            A__          =   min_length_for_response
        if minimum_tokens is not None:
            A__          =   minimum_tokens

        if "max_length" in generate_kwargs:
            A__          =   generate_kwargs['''max_length''']
            # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
        if clean_up_tokenization_spaces is not None:
            A__          =   clean_up_tokenization_spaces

        if generate_kwargs:
            forward_params.update(lowercase_		)
        return preprocess_params, forward_params, postprocess_params


    def __call__(      self :       Optional[Any],lowercase_ :       List[str],lowercase_ :       Optional[Any]=0,**lowercase_ :       List[Any]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   super().__call__(lowercase_,num_workers=lowercase_,**lowercase_		)
        if isinstance(lowercase_,lowercase_		) and len(lowercase_		) == 1:
            return outputs[0]
        return outputs


    def 						snake_case__     (      self :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       Tuple=3_2		)->    Dict[str, Any]:





        '''simple docstring'''







        if not isinstance(lowercase_,lowercase_		):
            raise ValueError('ConversationalPipeline, expects Conversation as inputs'		)
        if conversation.new_user_input is None:
            raise ValueError(
                F'Conversation with UUID {type(conversation.uuid		)} does not contain new user input to process. '
                'Add user inputs with the conversation\'s `add_user_input` method'		)
        if hasattr(self.tokenizer,'_build_conversation_input_ids'		):
            A__          =   self.tokenizer._build_conversation_input_ids(lowercase_		)
        else:
            # If the tokenizer cannot handle conversations, we default to only the old version
            A__          =   self._legacy_parse_and_tokenize(lowercase_		)

        if self.framework == "pt":
            A__          =   torch.LongTensor([input_ids]		)
        elif self.framework == "tf":
            A__          =   tf.constant([input_ids]		)
        return {"input_ids": input_ids, "conversation": conversation}


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       str,lowercase_ :       List[str]=1_0,**lowercase_ :       Any		)->    int:





        '''simple docstring'''







        A__          =   generate_kwargs.get('max_length',self.model.config.max_length		)

        A__          =   model_inputs['''input_ids'''].shape[1]
        if max_length - minimum_tokens < n:
            logger.warning(F'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'		)
            A__          =   max_length - minimum_tokens
            A__          =   model_inputs['''input_ids'''][:, -trim:]
            if "attention_mask" in model_inputs:
                A__          =   model_inputs['''attention_mask'''][:, -trim:]
        A__          =   model_inputs.pop('conversation'		)
        A__          =   max_length
        A__          =   self.model.generate(**lowercase_,**lowercase_		)
        if self.model.config.is_encoder_decoder:
            A__          =   1
        else:
            A__          =   n
        return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, Any],lowercase_ :       Any=True		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   model_outputs['''output_ids''']
        A__          =   self.tokenizer.decode(
            output_ids[0],skip_special_tokens=lowercase_,clean_up_tokenization_spaces=lowercase_,)
        A__          =   model_outputs['''conversation''']
        conversation.mark_processed()
        conversation.append_response(lowercase_		)
        return conversation


    def 						snake_case__     (      self :       Tuple,lowercase_ :       List[str]		)->    Dict:





        '''simple docstring'''







        A__          =   self.tokenizer.eos_token_id
        A__          =   []
        for is_user, text in conversation.iter_texts():
            if eos_token_id is not None:
                input_ids.extend(self.tokenizer.encode(lowercase_,add_special_tokens=lowercase_		) + [eos_token_id]		)
            else:
                input_ids.extend(self.tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)		)

        if len(lowercase_		) > self.tokenizer.model_max_length:
            A__          =   input_ids[-self.tokenizer.model_max_length :]
        return input_ids






"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int							):
    if a < 0 or b < 0:
        raise ValueError('''the value of both inputs must be positive'''							)
    _UpperCAmelCase							:  List[str]											=    str(bin(UpperCamelCase__							)							)[2:]  # remove the leading "0b"
    _UpperCAmelCase							:  str											=    str(bin(UpperCamelCase__							)							)[2:]
    _UpperCAmelCase							:  List[str]											=    max(len(UpperCamelCase__							)     ,       len(UpperCamelCase__							)							)
    return "0b" + "".join(
        str(int('''1''' in (char_a, char_b)							)							)
        for char_a, char_b in zip(a_binary.zfill(UpperCamelCase__							)     ,       b_binary.zfill(UpperCamelCase__							)							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


