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


__magic_name__								=logging.get_logger(__name__)



class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None	)		->      List[Any]:





       '''simple docstring'''





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

       UpperCamelCase__										=      conversation_id
       UpperCamelCase__										=      past_user_inputs
       UpperCamelCase__										=      generated_responses
       UpperCamelCase__										=      text






  def __eq__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = False	)		->      Union[str, 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}\"."	)
                 UpperCamelCase__										=      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:
            UpperCamelCase__										=      text






  def 		_a					(self	)		->      str:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       self.generated_responses.append(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[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	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      F"Conversation id: {self.uuid} \n"
       for is_user, text in self.iter_texts():
            UpperCamelCase__										=      '''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   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      {}
       UpperCamelCase__										=      {}
       UpperCamelCase__										=      {}

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

       if "max_length" in generate_kwargs:
            UpperCamelCase__										=      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:
            UpperCamelCase__										=      clean_up_tokenization_spaces

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






  def __call__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=0   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=32	)		->      Dict[str, Any]:





       '''simple docstring'''





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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=10   ,				**SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=True	)		->      Optional[int]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





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

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




from __future__ import annotations







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      get_failure_array(A							)

     # 2) Step through text searching for pattern
     UpperCamelCase__						,       UpperCamelCase__										=      0, 0  # index into text, pattern
     while i < len(A							):
          if pattern[j] == text[i]:
               if j == (len(A							) - 1):
                    return True
               j += 1

          # if this is a prefix in our pattern
          # just go back far enough to continue
          elif j > 0:
               UpperCamelCase__										=      failure[j - 1]
               continue
          i += 1
     return False







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      [0]
     UpperCamelCase__										=      0
     UpperCamelCase__										=      1
     while j < len(A							):
          if pattern[i] == pattern[j]:
               i += 1
          elif i > 0:
               UpperCamelCase__										=      failure[i - 1]
               continue
          j += 1
          failure.append(A							)
     return failure


if __name__ == "__main__":
     # Test 1)
     __magic_name__								='''abc1abc12'''
     __magic_name__								='''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     __magic_name__								='''alskfjaldsk23adsfabcabc'''
     assert kmp(pattern, texta) and not kmp(pattern, texta)

     # Test 2)
     __magic_name__								='''ABABX'''
     __magic_name__								='''ABABZABABYABABX'''
     assert kmp(pattern, text)

     # Test 3)
     __magic_name__								='''AAAB'''
     __magic_name__								='''ABAAAAAB'''
     assert kmp(pattern, text)

     # Test 4)
     __magic_name__								='''abcdabcy'''
     __magic_name__								='''abcxabcdabxabcdabcdabcy'''
     assert kmp(pattern, text)

     # Test 5)
     __magic_name__								='''aabaabaaa'''
     assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]



