





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 SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	):
										if not conversation_id:
															lowerCamelCase__       =       uuid.uuida()
										if past_user_inputs is None:
															lowerCamelCase__       =       []
										if generated_responses is None:
															lowerCamelCase__       =       []

										lowerCamelCase__       =       conversation_id
										lowerCamelCase__       =       past_user_inputs
										lowerCamelCase__       =       generated_responses
										lowerCamelCase__       =       text





					def __eq__( self	,  _lowerCAmelCase	):
										if not isinstance(UpperCamelCase__	,  UpperCamelCase__	):
															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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = False	):
										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}\"."	)
																				lowerCamelCase__       =       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:
															lowerCamelCase__       =       text





					def 					__magic_name__ ( self	):
										if self.new_user_input:
															self.past_user_inputs.append(self.new_user_input	)
										lowerCamelCase__       =       None





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										self.generated_responses.append(UpperCamelCase__	)





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

@add_end_docstrings(
    SCREAMING_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 SCREAMING_SNAKE_CASE_ (					SCREAMING_SNAKE_CASE_   ):





					"""simple docstring"""


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





					def 					__magic_name__ ( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       {}
										lowerCamelCase__       =       {}
										lowerCamelCase__       =       {}

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

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

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





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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=32	):
										if not isinstance(UpperCamelCase__	,  UpperCamelCase__	):
															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"	):
															lowerCamelCase__       =       self.tokenizer._build_conversation_input_ids(UpperCamelCase__	)
										else:
															# If the tokenizer cannot handle conversations, we default to only the old version
															lowerCamelCase__       =       self._legacy_parse_and_tokenize(UpperCamelCase__	)

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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=10	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       generate_kwargs.get("max_length"	,  self.model.config.max_length	)

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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=True	):
										lowerCamelCase__       =       model_outputs['''output_ids''']
										lowerCamelCase__       =       self.tokenizer.decode(
										    output_ids[0]	,  skip_special_tokens=UpperCamelCase__	,  clean_up_tokenization_spaces=UpperCamelCase__	,  )
										lowerCamelCase__       =       model_outputs['''conversation''']
										conversation.mark_processed()
										conversation.append_response(UpperCamelCase__	)
										return conversation






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

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








A_												=     {str(digit): digit**5 for digit in range(1_0)}





def    __UpperCamelCase      (    a) ->int:
					return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a))





def    __UpperCamelCase      (    ) ->int:
					return sum(
					    number
					    for number in range(1000,     1000000)
					    if number == digits_fifth_powers_sum(a))


if __name__ == "__main__":
						print(solution())

