





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      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None		):



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

							A__							=       conversation_id
							A__							=       past_user_inputs
							A__							=       generated_responses
							A__							=       text



	def __eq__(       self			,	UpperCAmelCase__		):



							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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = 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}\"."""		)
																			A__							=       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__							=       text



	def 			__A							(       self		):



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



	def 			__A							(       self			,	UpperCAmelCase__		):
							self.generated_responses.append(__lowerCAmelCase		)



	def 			__A							(       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		):
							A__							=       F"""Conversation id: {self.uuid} \n"""
							for is_user, text in self.iter_texts():
													A__							=       "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      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(*__lowerCAmelCase			,	**__lowerCAmelCase		)
							if self.tokenizer.pad_token_id is None:
													A__							=       self.tokenizer.eos_token



	def 			__A							(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	**UpperCAmelCase__		):
							A__							=       {}
							A__							=       {}
							A__							=       {}

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

							if "max_length" in generate_kwargs:
													A__							=       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__							=       clean_up_tokenization_spaces

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



	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0			,	**UpperCAmelCase__		):
							A__							=       super().__call__(__lowerCAmelCase			,	num_workers=__lowerCAmelCase			,	**__lowerCAmelCase		)
							if isinstance(__lowerCAmelCase			,	__lowerCAmelCase		) and len(__lowerCAmelCase		) == 1:
													return outputs[0]
							return outputs



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=32		):



							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"		):
													A__							=       self.tokenizer._build_conversation_input_ids(__lowerCAmelCase		)
							else:
													# If the tokenizer cannot handle conversations, we default to only the old version
													A__							=       self._legacy_parse_and_tokenize(__lowerCAmelCase		)

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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=10			,	**UpperCAmelCase__		):
							A__							=       generate_kwargs.get("max_length"			,	self.model.config.max_length		)

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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=True		):
							A__							=       model_outputs["output_ids"]
							A__							=       self.tokenizer.decode(
							    output_ids[0]			,	skip_special_tokens=__lowerCAmelCase			,	clean_up_tokenization_spaces=__lowerCAmelCase			,	)
							A__							=       model_outputs["conversation"]
							conversation.mark_processed()
							conversation.append_response(__lowerCAmelCase		)
							return conversation



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.tokenizer.eos_token_id
							A__							=       []
							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:
													A__							=       input_ids[-self.tokenizer.model_max_length :]
							return input_ids










import math
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * power_factor
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





