



"""simple docstring"""
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


_UpperCAmelCase   =	logging.get_logger(__name__)
class     a		:



  def __init__(  self       :    Tuple				,						lowerCAmelCase       :    Dict = None				,						lowerCAmelCase       :    Optional[int] = None				,						lowerCAmelCase       :    Union[str, Any]=None				,						lowerCAmelCase       :    Optional[Any]=None						)   ->    Any:


     '''simple docstring'''




     if not conversation_id:
        SCREAMING_SNAKE_CASE_:       Optional[int]   	=uuid.uuida()
     if past_user_inputs is None:
        SCREAMING_SNAKE_CASE_:       Any   	=[]
     if generated_responses is None:
        SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=[]

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=conversation_id
     SCREAMING_SNAKE_CASE_:       Any   	=past_user_inputs
     SCREAMING_SNAKE_CASE_:       List[Any]   	=generated_responses
     SCREAMING_SNAKE_CASE_:       List[str]   	=text



  def __eq__(  self       :    Union[str, Any]				,						lowerCAmelCase       :    Optional[int]						)   ->    Tuple:


     '''simple docstring'''




     if not isinstance(lowerCamelCase_				,						lowerCamelCase_						):
        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 		lowerCamelCase__				(  self       :    List[str]				,						lowerCAmelCase       :    int				,						lowerCAmelCase       :    List[Any] = False						)   ->    str:


     '''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}".'''						)
           SCREAMING_SNAKE_CASE_:       Any   	=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:
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=text



  def 		lowerCamelCase__				(  self       :    Optional[int]						)   ->    List[str]:


     '''simple docstring'''




     if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=None



  def 		lowerCamelCase__				(  self       :    Union[str, Any]				,						lowerCAmelCase       :    Dict						)   ->    Optional[Any]:


     '''simple docstring'''




     self.generated_responses.append(lowerCamelCase_						)



  def 		lowerCamelCase__				(  self       :    int						)   ->    str:


     '''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       :    Tuple						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=f'''Conversation id: {self.uuid} \n'''
     for is_user, text in self.iter_texts():
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	="""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__				):



  def __init__(  self       :    Optional[int]				,						*lowerCAmelCase       :    int				,						**lowerCAmelCase       :    str						)   ->    Optional[Any]:


     '''simple docstring'''




     super().__init__(*lowerCamelCase_				,						**lowerCamelCase_						)
     if self.tokenizer.pad_token_id is None:
        SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.tokenizer.eos_token



  def 		lowerCamelCase__				(  self       :    List[Any]				,						lowerCAmelCase       :    int=None				,						lowerCAmelCase       :    Tuple=None				,						lowerCAmelCase       :    Optional[Any]=None				,						**lowerCAmelCase       :    Union[str, Any]						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       str   	={}
     SCREAMING_SNAKE_CASE_:       Any   	={}
     SCREAMING_SNAKE_CASE_:       Tuple   	={}

     if min_length_for_response is not None:
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=min_length_for_response
     if minimum_tokens is not None:
        SCREAMING_SNAKE_CASE_:       int   	=minimum_tokens

     if "max_length" in generate_kwargs:
        SCREAMING_SNAKE_CASE_:       Dict   	=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:
        SCREAMING_SNAKE_CASE_:       int   	=clean_up_tokenization_spaces

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



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


     '''simple docstring'''




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



  def 		lowerCamelCase__				(  self       :    int				,						lowerCAmelCase       :    Any				,						lowerCAmelCase       :    str=32						)   ->    List[str]:


     '''simple docstring'''




     if not isinstance(lowerCamelCase_				,						lowerCamelCase_						):
        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"""						):
        SCREAMING_SNAKE_CASE_:       List[str]   	=self.tokenizer._build_conversation_input_ids(lowerCamelCase_						)
     else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        SCREAMING_SNAKE_CASE_:       str   	=self._legacy_parse_and_tokenize(lowerCamelCase_						)

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



  def 		lowerCamelCase__				(  self       :    Tuple				,						lowerCAmelCase       :    List[Any]				,						lowerCAmelCase       :    Dict=10				,						**lowerCAmelCase       :    List[str]						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[str]   	=generate_kwargs.get("""max_length"""				,						self.model.config.max_length						)

     SCREAMING_SNAKE_CASE_:       List[Any]   	=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})'''						)
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=max_length - minimum_tokens
        SCREAMING_SNAKE_CASE_:       Tuple   	=model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
           SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=model_inputs["""attention_mask"""][:, -trim:]
     SCREAMING_SNAKE_CASE_:       Any   	=model_inputs.pop("""conversation"""						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=max_length
     SCREAMING_SNAKE_CASE_:       int   	=self.model.generate(**lowerCamelCase_				,						**lowerCamelCase_						)
     if self.model.config.is_encoder_decoder:
        SCREAMING_SNAKE_CASE_:       Dict   	=1
     else:
        SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



  def 		lowerCamelCase__				(  self       :    List[Any]				,						lowerCAmelCase       :    Union[str, Any]				,						lowerCAmelCase       :    Optional[Any]=True						)   ->    Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       str   	=model_outputs["""output_ids"""]
     SCREAMING_SNAKE_CASE_:       Tuple   	=self.tokenizer.decode(
         output_ids[0]				,						skip_special_tokens=lowerCamelCase_				,						clean_up_tokenization_spaces=lowerCamelCase_				,						)
     SCREAMING_SNAKE_CASE_:       Any   	=model_outputs["""conversation"""]
     conversation.mark_processed()
     conversation.append_response(lowerCamelCase_						)
     return conversation





  def 		lowerCamelCase__				(  self       :    Any				,						lowerCAmelCase       :    Dict						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.tokenizer.eos_token_id
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=[]
     for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
           input_ids.extend(self.tokenizer.encode(lowerCamelCase_				,						add_special_tokens=lowerCamelCase_						) + [eos_token_id]						)
        else:
           input_ids.extend(self.tokenizer.encode(lowerCamelCase_				,						add_special_tokens=lowerCamelCase_						)						)

     if len(lowerCamelCase_						) > self.tokenizer.model_max_length:
        SCREAMING_SNAKE_CASE_:       List[str]   	=input_ids[-self.tokenizer.model_max_length :]
     return input_ids








def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					if number > 0:
										raise ValueError("input must be a negative integer"       )
					a					     =   len(bin(A       )[3:]       )
					a					     =   bin(abs(A       ) - (1 << binary_number_length)       )[3:]
					a					     =   (
					    (
					        "1"
					        + "0" * (binary_number_length - len(A       ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()
