

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 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a = None			,				_a = None			,				_a=None			,				_a=None			)       ->       Union[str, Any]:
												if not conversation_id:
																	_a      :       Dict   =						uuid.uuida()
												if past_user_inputs is None:
																	_a      :       Any   =						[]
												if generated_responses is None:
																	_a      :       int   =						[]

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


							def __eq__(     self			,				_a			)       ->       Any:
												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 							__lowercase							(     self			,				_a			,				_a = False			)       ->       List[str]:
												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      :       Dict   =						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      :       int   =						text


							def 							__lowercase							(     self			)       ->       Optional[int]:
												if self.new_user_input:
																	self.past_user_inputs.append(self.new_user_input			)
												_a      :       List[Any]   =						None


							def 							__lowercase							(     self			,				_a			)       ->       Optional[Any]:
												self.generated_responses.append(_a			)


							def 							__lowercase							(     self			)       ->       str:
												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			)       ->       Optional[int]:
												_a      :       List[Any]   =						F"""Conversation id: {self.uuid} \n"""
												for is_user, text in self.iter_texts():
																	_a      :       int   =						'''user''' if is_user else '''bot'''
																	output += F"""{name} >> {text} \n"""
												return output



@add_end_docstrings(
    __lowercase							,			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 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def __init__(     self			,				*_a			,				**_a			)       ->       List[str]:
												super().__init__(*_a			,				**_a			)
												if self.tokenizer.pad_token_id is None:
																	_a      :       str   =						self.tokenizer.eos_token


							def 							__lowercase							(     self			,				_a=None			,				_a=None			,				_a=None			,				**_a			)       ->       Union[str, Any]:
												_a      :       int   =						{}
												_a      :       str   =						{}
												_a      :       List[Any]   =						{}

												if min_length_for_response is not None:
																	_a      :       int   =						min_length_for_response
												if minimum_tokens is not None:
																	_a      :       str   =						minimum_tokens

												if "max_length" in generate_kwargs:
																	_a      :       Optional[int]   =						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      :       Optional[int]   =						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			)       ->       int:
												_a      :       Optional[int]   =						super().__call__(_a			,				num_workers=_a			,				**_a			)
												if isinstance(_a			,				_a			) and len(_a			) == 1:
																	return outputs[0]
												return outputs


							def 							__lowercase							(     self			,				_a			,				_a=3_2			)       ->       Dict[str, Any]:
												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      :       List[Any]   =						self.tokenizer._build_conversation_input_ids(_a			)
												else:
																	# If the tokenizer cannot handle conversations, we default to only the old version
																	_a      :       str   =						self._legacy_parse_and_tokenize(_a			)

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


							def 							__lowercase							(     self			,				_a			,				_a=1_0			,				**_a			)       ->       Union[str, Any]:
												_a      :       int   =						generate_kwargs.get('''max_length'''			,				self.model.config.max_length			)

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


							def 							__lowercase							(     self			,				_a			,				_a=True			)       ->       Union[str, Any]:
												_a      :       Optional[int]   =						model_outputs['''output_ids''']
												_a      :       List[Any]   =						self.tokenizer.decode(
												    output_ids[0]			,				skip_special_tokens=_a			,				clean_up_tokenization_spaces=_a			,				)
												_a      :       str   =						model_outputs['''conversation''']
												conversation.mark_processed()
												conversation.append_response(_a			)
												return conversation




							def 							__lowercase							(     self			,				_a			)       ->       Dict:
												_a      :       Union[str, Any]   =						self.tokenizer.eos_token_id
												_a      :       Union[str, Any]   =						[]
												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      :       Optional[Any]   =						input_ids[-self.tokenizer.model_max_length :]
												return input_ids





'''simple docstring'''





from math import factorial

SCREAMING_SNAKE_CASE     =       {str(digit): factorial(digit) for digit in range(1_0)}







def snake_case_     (					lowercase__		):
  if not isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("Parameter number must be int"		)

  if number < 0:
    raise ValueError("Parameter number must be greater than or equal to 0"		)

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(lowercase__		)		)







def snake_case_     (					lowercase__ = 6_0   ,       lowercase__ = 1_0_0_0_0_0_0		):

  if not isinstance(lowercase__   ,       lowercase__		) or not isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("Parameters chain_length and number_limit must be int"		)

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        "Parameters chain_length and number_limit must be greater than 0"		)

  # the counter for the chains with the exact desired length
  UpperCAmelCase__      :					Union[str, Any]				       =							0
  # the cached sizes of the previous chains
  UpperCAmelCase__      :					dict[int, int]				       =							{}

  for start_chain_element in range(1   ,       lowercase__		):
    # The temporary set will contain the elements of the chain
    UpperCAmelCase__      :					Any				       =							set()
    UpperCAmelCase__      :					int				       =							0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    UpperCAmelCase__      :					List[str]				       =							start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(lowercase__		)
      chain_set_length += 1
      UpperCAmelCase__      :					List[str]				       =							digit_factorial_sum(lowercase__		)

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    UpperCAmelCase__      :					List[str]				       =							chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(F'{solution()}')



