





'''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


a        =						logging.get_logger(__name__)







class __a :





   def __init__(					self				:			int				,lowerCamelCase				:			str = None				,lowerCamelCase				:			uuid.UUID = None				,lowerCamelCase				:			Optional[int]=None				,lowerCamelCase				:			Union[str, Any]=None    ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       conversation_id
      __SCREAMING_SNAKE_CASE      							=       past_user_inputs
      __SCREAMING_SNAKE_CASE      							=       generated_responses
      __SCREAMING_SNAKE_CASE      							=       text





   def __eq__(					self				:			str				,lowerCamelCase				:			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      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			str				,lowerCamelCase				:			bool = False    ):

      '''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      							=       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      							=       text





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[int]				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      self.generated_responses.append(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''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				:			Any    ):

      '''simple docstring'''

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







@add_end_docstrings(
    _snake_case,   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 (   _snake_case			):





   def __init__(					self				:			str				,*lowerCamelCase				:			Tuple				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			List[str]				,lowerCamelCase				:			Union[str, Any]=None				,lowerCamelCase				:			Tuple=None				,lowerCamelCase				:			Union[str, Any]=None				,**lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       {}
      __SCREAMING_SNAKE_CASE      							=       {}
      __SCREAMING_SNAKE_CASE      							=       {}

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

      if "max_length" in generate_kwargs:
         __SCREAMING_SNAKE_CASE      							=       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      							=       clean_up_tokenization_spaces

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





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Union[Conversation, List[Conversation]]				,lowerCamelCase				:			Tuple=0				,**lowerCamelCase				:			List[Any]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Conversation				,lowerCamelCase				:			Optional[int]=32    ):

      '''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      							=       self.tokenizer._build_conversation_input_ids(lowerCamelCase    )
      else:
         # If the tokenizer cannot handle conversations, we default to only the old version
         __SCREAMING_SNAKE_CASE      							=       self._legacy_parse_and_tokenize(lowerCamelCase    )

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





   def      UpperCAmelCase__     (					self				:			int				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Tuple=10				,**lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       generate_kwargs.get("""max_length"""				,self.model.config.max_length    )

      __SCREAMING_SNAKE_CASE      							=       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      							=       max_length - minimum_tokens
         __SCREAMING_SNAKE_CASE      							=       model_inputs["""input_ids"""][:, -trim:]
         if "attention_mask" in model_inputs:
            __SCREAMING_SNAKE_CASE      							=       model_inputs["""attention_mask"""][:, -trim:]
      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""conversation"""    )
      __SCREAMING_SNAKE_CASE      							=       max_length
      __SCREAMING_SNAKE_CASE      							=       self.model.generate(**lowerCamelCase				,**lowerCamelCase    )
      if self.model.config.is_encoder_decoder:
         __SCREAMING_SNAKE_CASE      							=       1
      else:
         __SCREAMING_SNAKE_CASE      							=       n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			str				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_outputs["""output_ids"""]
      __SCREAMING_SNAKE_CASE      							=       self.tokenizer.decode(
          output_ids[0]				,skip_special_tokens=lowerCamelCase				,clean_up_tokenization_spaces=lowerCamelCase				,)
      __SCREAMING_SNAKE_CASE      							=       model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(lowerCamelCase    )
      return conversation





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			Conversation    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.tokenizer.eos_token_id
      __SCREAMING_SNAKE_CASE      							=       []
      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      							=       input_ids[-self.tokenizer.model_max_length :]
      return input_ids







'''simple docstring'''

def 						__magic_name__       (							__UpperCAmelCase = 1							,	__UpperCAmelCase = 1000      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       1
   __SCREAMING_SNAKE_CASE      							=       0

   for divide_by_number in range(__UpperCAmelCase							,	digit + 1      ):
      __SCREAMING_SNAKE_CASE      							=       []
      __SCREAMING_SNAKE_CASE      							=       numerator
      for _ in range(1							,	digit + 1      ):
         if now_divide in has_been_divided:
            if longest_list_length < len(__UpperCAmelCase      ):
               __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
               __SCREAMING_SNAKE_CASE      							=       divide_by_number
         else:
            has_been_divided.append(__UpperCAmelCase      )
            __SCREAMING_SNAKE_CASE      							=       now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





