


import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


UpperCamelCase			    =     logging.get_logger(__name__)


UpperCamelCase			    =     r"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"



class 				__UpperCAmelCase		(a_   ):







			@add_start_docstrings(UpperCAmelCase_					)
			def __call__(     self:  Dict				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  Any				,     **UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									raise NotImplementedError("""StoppingCriteria needs to be subclassed"""					)



class 				__UpperCAmelCase		(a_   ):







			def __init__(     self:  Tuple				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  Union[str, Any] = None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  max_length
									_SCREAMING_SNAKE_CASE     				=  max_position_embeddings







			@add_start_docstrings(UpperCAmelCase_					)
			def __call__(     self:  Optional[Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Any				,     **UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  input_ids.shape[-1]
									_SCREAMING_SNAKE_CASE     				=  cur_len >= self.max_length
									if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
															logger.warning_once(
															    """This is a friendly reminder - the current text generation call will exceed the model's predefined """
															    F'maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '
															    """exceptions, performance degradation, or nothing at all."""					)
									return is_done



class 				__UpperCAmelCase		(a_   ):







			def __init__(     self:  Optional[Any]				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									warnings.warn(
									    """The class `MaxNewTokensCriteria` is deprecated. """
									    F'Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '
									    """with `max_length = start_length + max_new_tokens` instead."""				,     UpperCAmelCase_				,     )
									_SCREAMING_SNAKE_CASE     				=  start_length
									_SCREAMING_SNAKE_CASE     				=  max_new_tokens
									_SCREAMING_SNAKE_CASE     				=  start_length + max_new_tokens







			@add_start_docstrings(UpperCAmelCase_					)
			def __call__(     self:  List[str]				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  List[str]				,     **UpperCAmelCase_:  List[Any]					):







									'''simple docstring'''




									return input_ids.shape[-1] >= self.max_length



class 				__UpperCAmelCase		(a_   ):







			def __init__(     self:  Optional[int]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  int = None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  max_time
									_SCREAMING_SNAKE_CASE     				=  time.time() if initial_timestamp is None else initial_timestamp







			@add_start_docstrings(UpperCAmelCase_					)
			def __call__(     self:  Dict				,     UpperCAmelCase_:  Tuple				,     UpperCAmelCase_:  Optional[Any]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									return time.time() - self.initial_timestamp > self.max_time



class 				__UpperCAmelCase		(a_   ):







			@add_start_docstrings(UpperCAmelCase_					)
			def __call__(     self:  Union[str, Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									return any(criteria(UpperCAmelCase_				,     UpperCAmelCase_					) for criteria in self					)







			@property
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for stopping_criterium in self:
															if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																					return stopping_criterium.max_length
															elif isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																					return stopping_criterium.max_length
									return None

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  stopping_criteria.max_length
						_SCREAMING_SNAKE_CASE     				=  deepcopy(lowerCamelCase__		)
						if stopping_max_length is not None and stopping_max_length != max_length:
												warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter"""	,lowerCamelCase__		)
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=lowerCamelCase__		)		)
						return new_stopping_criteria









def 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		)
						for i in range(length - 1		):
												_SCREAMING_SNAKE_CASE     				=  i
												for k in range(i + 1	,snake_case__		):
																		if collection[k] < collection[least]:
																								_SCREAMING_SNAKE_CASE     				=  k
												if least != i:
																		_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  (collection[i], collection[least])
						return collection


if __name__ == "__main__":
		UpperCamelCase			    =     input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase			    =     [int(item) for item in user_input.split(''',''')]
		print(selection_sort(unsorted))

