



'''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


lowercase_		       =  logging.get_logger(__name__)






class __A			:



 '''simple docstring'''







 def __init__(self ,   A = None ,   A = None ,   A=None ,   A=None       )    ->     str:


      """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 ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      if not isinstance(A ,   A       ):
           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 					a__  (self ,   A ,   A = False       )    ->     List[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}".'''       )
                _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 					a__  (self       )    ->     Any:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      self.generated_responses.append(A       )



 def 					a__  (self       )    ->     Union[str, Any]:


      """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"""



      _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(
    A   ,   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			(  A						):



 '''simple docstring'''







 def __init__(self ,   *A ,   **A       )    ->     Optional[Any]:


      """simple docstring"""



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



 def 					a__  (self ,   A=None ,   A=None ,   A=None ,   **A       )    ->     int:


      """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(A       )
      return preprocess_params, forward_params, postprocess_params



 def __call__(self ,   A ,   A=0 ,   **A       )    ->     List[str]:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A=32       )    ->     Dict[str, Any]:


      """simple docstring"""



      if not isinstance(A ,   A       ):
           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(A       )
      else:
           # If the tokenizer cannot handle conversations, we default to only the old version
           _a      =  self._legacy_parse_and_tokenize(A       )

      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 					a__  (self ,   A ,   A=10 ,   **A       )    ->     str:


      """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(**A ,   **A       )
      if self.model.config.is_encoder_decoder:
           _a      =  1
      else:
           _a      =  n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



 def 					a__  (self ,   A ,   A=True       )    ->     Optional[int]:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     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(A ,   add_special_tokens=A       ) + [eos_token_id]       )
           else:
                input_ids.extend(self.tokenizer.encode(A ,   add_special_tokens=A       )       )

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








'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



