
"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







class 							__magic_name__     :





					def __init__(				self   :							int							,						snake_case_   :							str = None							,						snake_case_   :							uuid.UUID = None							,						snake_case_   :							List[Any]=None							,						snake_case_   :							int=None    ):
												if not conversation_id:
																			__snake_case     			=       uuid.uuida()
												if past_user_inputs is None:
																			__snake_case     			=       []
												if generated_responses is None:
																			__snake_case     			=       []

												__snake_case     			=       conversation_id
												__snake_case     			=       past_user_inputs
												__snake_case     			=       generated_responses
												__snake_case     			=       text





					def __eq__(				self   :							List[str]							,						snake_case_   :							Dict    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			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 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							str							,						snake_case_   :							bool = 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}".'''    )
																										__snake_case     			=       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:
																			__snake_case     			=       text





					def 							lowerCAmelCase      (				self   :							str    ):
												if self.new_user_input:
																			self.past_user_inputs.append(self.new_user_input    )
												__snake_case     			=       None





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str    ):
												self.generated_responses.append(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												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   :							int    ):
												__snake_case     			=       F'''Conversation id: {self.uuid} \n'''
												for is_user, text in self.iter_texts():
																			__snake_case     			=       "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 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Dict							,						*snake_case_   :							Any							,						**snake_case_   :							int    ):
												super().__init__(*snake_case_							,						**snake_case_    )
												if self.tokenizer.pad_token_id is None:
																			__snake_case     			=       self.tokenizer.eos_token





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Optional[int]=None							,						snake_case_   :							Dict=None							,						snake_case_   :							Any=None							,						**snake_case_   :							Union[str, Any]    ):
												__snake_case     			=       {}
												__snake_case     			=       {}
												__snake_case     			=       {}

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

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

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





					def __call__(				self   :							str							,						snake_case_   :							Union[Conversation, List[Conversation]]							,						snake_case_   :							Optional[int]=0							,						**snake_case_   :							Optional[int]    ):
												__snake_case     			=       super().__call__(snake_case_							,						num_workers=snake_case_							,						**snake_case_    )
												if isinstance(snake_case_							,						snake_case_    ) and len(snake_case_    ) == 1:
																			return outputs[0]
												return outputs





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Conversation							,						snake_case_   :							List[str]=32    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			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"    ):
																			__snake_case     			=       self.tokenizer._build_conversation_input_ids(snake_case_    )
												else:
																			# If the tokenizer cannot handle conversations, we default to only the old version
																			__snake_case     			=       self._legacy_parse_and_tokenize(snake_case_    )

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





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Tuple							,						snake_case_   :							Tuple=10							,						**snake_case_   :							Optional[int]    ):
												__snake_case     			=       generate_kwargs.get("max_length"							,						self.model.config.max_length    )

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





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Any							,						snake_case_   :							Union[str, Any]=True    ):
												__snake_case     			=       model_outputs["output_ids"]
												__snake_case     			=       self.tokenizer.decode(
												    output_ids[0]							,						skip_special_tokens=snake_case_							,						clean_up_tokenization_spaces=snake_case_							,						)
												__snake_case     			=       model_outputs["conversation"]
												conversation.mark_processed()
												conversation.append_response(snake_case_    )
												return conversation





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Conversation    ):
												__snake_case     			=       self.tokenizer.eos_token_id
												__snake_case     			=       []
												for is_user, text in conversation.iter_texts():
																			if eos_token_id is not None:
																										input_ids.extend(self.tokenizer.encode(snake_case_							,						add_special_tokens=snake_case_    ) + [eos_token_id]    )
																			else:
																										input_ids.extend(self.tokenizer.encode(snake_case_							,						add_special_tokens=snake_case_    )    )

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


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = 10							,    SCREAMING_SNAKE_CASE = 10_00							,    SCREAMING_SNAKE_CASE = True )  ->     int:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError("Invalid value for min_val or max_val (min_value < max_value)" )
							return min_val if option else max_val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							return int((number_a + number_a) / 2 )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     None:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError("argument value for lower and higher must be(lower > higher)" )

							if not lower < to_guess < higher:
														raise ValueError(
														    "guess value must be within the range of lower and higher value" )

							def answer(SCREAMING_SNAKE_CASE ) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print("started..." )

							__snake_case     			=       lower
							__snake_case     			=       higher

							__snake_case     			=       []

							while True:
														__snake_case     			=       get_avg(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														last_numbers.append(SCREAMING_SNAKE_CASE )

														if answer(SCREAMING_SNAKE_CASE ) == "low":
																					__snake_case     			=       number
														elif answer(SCREAMING_SNAKE_CASE ) == "high":
																					__snake_case     			=       number
														else:
																					break

							print(F'''guess the number : {last_numbers[-1]}''' )
							print(F'''details : {last_numbers!s}''' )
def   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							__snake_case     			=       int(input("Enter lower value : " ).strip() )
							__snake_case     			=       int(input("Enter high value : " ).strip() )
							__snake_case     			=       int(input("Enter value to guess : " ).strip() )
							guess_the_number(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()
