


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       :       Any						       =				logging.get_logger(__name__)

class       __magic_name__	:


     def __init__(      self							:     Optional[int]							,	lowerCamelCase__							:     str = None							,	lowerCamelCase__							:     uuid.UUID = None							,	lowerCamelCase__							:     str=None							,	lowerCamelCase__							:     str=None			)  ->      List[Any]:





      '''simple docstring'''






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

      UpperCamelCase__					:    uuid.UUID          =   conversation_id
      UpperCamelCase__					:    List[str]          =   past_user_inputs
      UpperCamelCase__					:    List[str]          =   generated_responses
      UpperCamelCase__					:    Optional[str]          =   text


     def __eq__(      self							:     Tuple							,	lowerCamelCase__							:     Union[str, Any]			)  ->      Union[str, Any]:





      '''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							:     Union[str, Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     bool = 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__					:    Optional[int]          =   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__					:    str          =   text


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





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     str			)  ->      List[Any]:





      '''simple docstring'''






      self.generated_responses.append(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      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							:     str			)  ->      int:





      '''simple docstring'''






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



@add_end_docstrings(
    __lowerCAmelCase					,				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       __magic_name__	(     __lowerCAmelCase):


     def __init__(      self							:     Dict							,	*lowerCamelCase__							:     Any							,	**lowerCamelCase__							:     int			)  ->      int:





      '''simple docstring'''






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


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





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   {}
      UpperCamelCase__					:    Optional[int]          =   {}
      UpperCamelCase__					:    List[str]          =   {}

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

      if "max_length" in generate_kwargs:
       UpperCamelCase__					:    Union[str, Any]          =   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__					:    Dict          =   clean_up_tokenization_spaces

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


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





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     Conversation							,	lowerCamelCase__							:     Any=32			)  ->      Dict[str, Any]:





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

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


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   generate_kwargs.get('''max_length'''							,	self.model.config.max_length			)

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


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





      '''simple docstring'''






      UpperCamelCase__					:    str          =   model_outputs['''output_ids''']
      UpperCamelCase__					:    List[str]          =   self.tokenizer.decode(
          output_ids[0]							,	skip_special_tokens=lowerCamelCase__							,	clean_up_tokenization_spaces=lowerCamelCase__							,	)
      UpperCamelCase__					:    Optional[int]          =   model_outputs['''conversation''']
      conversation.mark_processed()
      conversation.append_response(lowerCamelCase__			)
      return conversation


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Conversation			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.tokenizer.eos_token_id
      UpperCamelCase__					:    str          =   []
      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:
       UpperCamelCase__					:    str          =   input_ids[-self.tokenizer.model_max_length :]
      return input_ids






__UpperCamelCase       :       List[Any]						       =				256
# Modulus to hash a string
__UpperCamelCase       :       Union[str, Any]						       =				100_0003

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )
 if p_len > t_len:
  return False

 UpperCamelCase__					:    Any          =   0
 UpperCamelCase__					:    str          =   0
 UpperCamelCase__					:    List[Any]          =   1

 # Calculating the hash of pattern and substring of text
 for i in range(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Any          =   (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
  UpperCamelCase__					:    List[str]          =   (ord(text[i]     ) + text_hash * alphabet_size) % modulus
  if i == p_len - 1:
   continue
  UpperCamelCase__					:    Dict          =   (modulus_power * alphabet_size) % modulus

 for i in range(0    , t_len - p_len + 1     ):
  if text_hash == p_hash and text[i : i + p_len] == pattern:
   return True
  if i == t_len - p_len:
   continue
  # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
  UpperCamelCase__					:    Optional[int]          =   (
      (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
      + ord(text[i + p_len]     )
  ) % modulus
 return False

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   '''abc1abc12'''
 UpperCamelCase__					:    Dict          =   '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 UpperCamelCase__					:    List[str]          =   '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ) and not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 2)
 UpperCamelCase__					:    Optional[int]          =   '''ABABX'''
 UpperCamelCase__					:    int          =   '''ABABZABABYABABX'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 3)
 UpperCamelCase__					:    int          =   '''AAAB'''
 UpperCamelCase__					:    str          =   '''ABAAAAAB'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 4)
 UpperCamelCase__					:    Union[str, Any]          =   '''abcdabcy'''
 UpperCamelCase__					:    List[str]          =   '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 5)
 UpperCamelCase__					:    Tuple          =   '''Lü'''
 UpperCamelCase__					:    Any          =   '''Lüsai'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''Lue'''
 assert not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 print('''Success.'''     )


if __name__ == "__main__":
       test_rabin_karp()


