

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






class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       List[Any]   =						name
												_a      :       List[str]   =						value
												_a      :       List[str]   =						weight


							def __repr__(     self			)       ->       Optional[int]:
												return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""


							def 							__lowercase							(     self			)       ->       List[Any]:
												return self.value


							def 							__lowercase							(     self			)       ->       int:
												return self.name


							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.weight




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return self.value / self.weight



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Tuple						,__a :     List[str]      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[int]   =						[]
					for i in range(len(__a      )      ):
										menu.append(Things(name[i]						,value[i]						,weight[i]      )      )
					return menu



def   __UpperCAmelCase					(      __a :     int						,__a :     Union[str, Any]						,__a :     int      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						sorted(__a						,key=__a						,reverse=__a      )
					_a      :       Any   =						[]
					_a ,   _a      :       Optional[int]   =						0.0, 0.0
					for i in range(len(__a      )      ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]      )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def   __UpperCAmelCase					(      )     ->	int:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



