



import math




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase = 0 ,    _lowerCamelCase = 0    ):
			A							:					Tuple              =						end or len(_lowerCamelCase    )
			for i in range(_lowerCamelCase ,    _lowerCamelCase    ):
						A							:					Optional[Any]              =						i
						A							:					List[Any]              =						array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									A							:					List[Any]              =						array[temp_index - 1]
									temp_index -= 1
						A							:					Union[str, Any]              =						temp_index_value
			return array




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):  # Max Heap
			A							:					Optional[int]              =						index
			A							:					Optional[int]              =						2 * index + 1  # Left Node
			A							:					List[Any]              =						2 * index + 2  # Right Node

			if left_index < heap_size and array[largest] < array[left_index]:
						A							:					int              =						left_index

			if right_index < heap_size and array[largest] < array[right_index]:
						A							:					Tuple              =						right_index

			if largest != index:
						A       , A							:					str              =						array[largest], array[index]
						heapify(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					int              =						len(_lowerCamelCase    )

			for i in range(n // 2 ,    -1 ,    -1    ):
						heapify(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

			for i in range(n - 1 ,    0 ,    -1    ):
						A       , A							:					Optional[Any]              =						array[0], array[i]
						heapify(_lowerCamelCase ,    0 ,    _lowerCamelCase    )

			return array




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			if (array[first_index] > array[middle_index]) != (
			    array[first_index] > array[last_index]
			):
						return array[first_index]
			elif (array[middle_index] > array[first_index]) != (
			    array[middle_index] > array[last_index]
			):
						return array[middle_index]
			else:
						return array[last_index]




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[int]              =						low
			A							:					Tuple              =						high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						A       , A							:					Any              =						array[j], array[i]
						i += 1




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			if len(_lowerCamelCase    ) == 0:
						return array
			A							:					int              =						2 * math.ceil(math.loga(len(_lowerCamelCase    )    )    )
			A							:					Any              =						16
			return intro_sort(_lowerCamelCase ,    0 ,    len(_lowerCamelCase    ) ,    _lowerCamelCase ,    _lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(_lowerCamelCase    )
						max_depth -= 1
						A							:					List[str]              =						median_of_a(_lowerCamelCase ,    _lowerCamelCase ,    start + ((end - start) // 2) + 1 ,    end - 1    )
						A							:					Union[str, Any]              =						partition(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
						intro_sort(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
						A							:					str              =						p
			return insertion_sort(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__SCREAMING_SNAKE_CASE           =  input("""Enter numbers separated by a comma : """).strip()
							__SCREAMING_SNAKE_CASE           =  [float(item) for item in user_input.split(""",""")]
							print(sort(unsorted))




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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Optional[int]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Optional[Any] )      ->				bool:
									raise NotImplementedError("StoppingCriteria needs to be subclassed" )







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[int] = None )      ->				List[Any]:
									A							:					str              =						max_length
									A							:					Optional[int]              =						max_position_embeddings







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			List[str]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Any )      ->				bool:
									A							:					List[Any]              =						input_ids.shape[-1]
									A							:					Any              =						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 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			int )      ->				List[Any]:
									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."	,	__lowerCamelCase	,	)
									A							:					str              =						start_length
									A							:					Optional[Any]              =						max_new_tokens
									A							:					Dict              =						start_length + max_new_tokens







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			int	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Tuple )      ->				bool:
									return input_ids.shape[-1] >= self.max_length







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Optional[int]	,	__lowerCamelCase      :			float	,	__lowerCamelCase      :			Optional[float] = None )      ->				List[Any]:
									A							:					str              =						max_time
									A							:					Dict              =						time.time() if initial_timestamp is None else initial_timestamp







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Any	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Tuple )      ->				bool:
									return time.time() - self.initial_timestamp > self.max_time







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			int )      ->				bool:
									return any(criteria(__lowerCamelCase	,	__lowerCamelCase ) for criteria in self )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									for stopping_criterium in self:
												if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															return stopping_criterium.max_length
												elif isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															return stopping_criterium.max_length
									return None







def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[int]              =						stopping_criteria.max_length
			A							:					Any              =						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