





'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase_   :   Union[str, Any]       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Dict       =					{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Dict								=						"perceiver"







	def __init__(				self			:   List[str] , lowerCamelCase			:   List[str]=2_5_6 , lowerCamelCase			:   List[Any]=1_2_8_0 , lowerCamelCase			:   int=7_6_8 , lowerCamelCase			:   str=1 , lowerCamelCase			:   List[Any]=2_6 , lowerCamelCase			:   int=8 , lowerCamelCase			:   List[Any]=8 , lowerCamelCase			:   Union[str, Any]=None , lowerCamelCase			:   str=None , lowerCamelCase			:   Union[str, Any]="kv" , lowerCamelCase			:   Dict=1 , lowerCamelCase			:   Any=1 , lowerCamelCase			:   List[Any]="gelu" , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   List[Any]=0.02 , lowerCamelCase			:   List[Any]=1e-12 , lowerCamelCase			:   Tuple=True , lowerCamelCase			:   int=2_6_2 , lowerCamelCase			:   int=2_0_4_8 , lowerCamelCase			:   Optional[Any]=5_6 , lowerCamelCase			:   Tuple=[3_6_8, 4_9_6] , lowerCamelCase			:   Optional[Any]=1_6 , lowerCamelCase			:   Optional[int]=1_9_2_0 , lowerCamelCase			:   Optional[int]=1_6 , lowerCamelCase			:   List[str]=[1, 1_6, 2_2_4, 2_2_4] , **lowerCamelCase			:   Tuple , ):




				'''simple docstring'''
				super().__init__(**lowerCamelCase		)

				a__     = num_latents
				a__     = d_latents
				a__     = d_model
				a__     = num_blocks
				a__     = num_self_attends_per_block
				a__     = num_self_attention_heads
				a__     = num_cross_attention_heads
				a__     = qk_channels
				a__     = v_channels
				a__     = cross_attention_shape_for_attention
				a__     = self_attention_widening_factor
				a__     = cross_attention_widening_factor
				a__     = hidden_act
				a__     = attention_probs_dropout_prob
				a__     = initializer_range
				a__     = layer_norm_eps
				a__     = use_query_residual
				# masked language modeling attributes
				a__     = vocab_size
				a__     = max_position_embeddings
				# image classification attributes
				a__     = image_size
				# flow attributes
				a__     = train_size
				# multimodal autoencoding attributes
				a__     = num_frames
				a__     = audio_samples_per_frame
				a__     = samples_per_patch
				a__     = output_shape






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				if self.task == "multiple-choice":
							a__     = {0: "batch", 1: "choice", 2: "sequence"}
				else:
							a__     = {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("inputs", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				    ]		)







	@property
	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				return 1e-4







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCamelCase			:   int = -1 , lowerCamelCase			:   int = -1 , lowerCamelCase			:   int = -1 , lowerCamelCase			:   bool = False , lowerCamelCase			:   Optional[TensorType] = None , lowerCamelCase			:   int = 3 , lowerCamelCase			:   int = 4_0 , lowerCamelCase			:   int = 4_0 , ):




				'''simple docstring'''
				# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

				if isinstance(lowerCamelCase , lowerCamelCase		):
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							a__     = compute_effective_axis_dimension(
							    lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0		)
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							a__     = preprocessor.num_special_tokens_to_add(lowerCamelCase		)
							a__     = compute_effective_axis_dimension(
							    lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCamelCase		)
							# Generate dummy inputs according to compute batch and sequence
							a__     = [" ".join(["a"]		) * seq_length] * batch_size
							a__     = dict(preprocessor(lowerCamelCase , return_tensors=lowerCamelCase		)		)
							a__     = inputs.pop("input_ids"		)
							return inputs
				elif isinstance(lowerCamelCase , lowerCamelCase		) and preprocessor.model_input_names[0] == "pixel_values":
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							a__     = compute_effective_axis_dimension(lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_batch		)
							a__     = self._generate_dummy_images(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase		)
							a__     = dict(preprocessor(images=lowerCamelCase , return_tensors=lowerCamelCase		)		)
							a__     = inputs.pop("pixel_values"		)
							return inputs
				else:
							raise ValueError(
							    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."		)










'''simple docstring'''


import math







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int = 0					,  __lowerCamelCase  :     int = 0     )   ->    list:
			a__     = end or len(__lowerCamelCase     )
			for i in range(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = i
						a__     = array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									a__     = array[temp_index - 1]
									temp_index -= 1
						a__     = temp_index_value
			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    None:  # Max Heap
			a__     = index
			a__     = 2 * index + 1  # Left Node
			a__     = 2 * index + 2  # Right Node

			if left_index < heap_size and array[largest] < array[left_index]:
						a__     = left_index

			if right_index < heap_size and array[largest] < array[right_index]:
						a__     = right_index

			if largest != index:
						a__   ,				a__     = array[largest], array[index]
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			a__     = len(__lowerCamelCase     )

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

			for i in range(n - 1					,  0					,  -1     ):
						a__   ,				a__     = array[0], array[i]
						heapify(__lowerCamelCase					,  0					,  __lowerCamelCase     )

			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			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    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			a__     = low
			a__     = high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						a__   ,				a__     = array[j], array[i]
						i += 1







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			if len(__lowerCamelCase     ) == 0:
						return array
			a__     = 2 * math.ceil(math.loga(len(__lowerCamelCase     )     )     )
			a__     = 16
			return intro_sort(__lowerCamelCase					,  0					,  len(__lowerCamelCase     )					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    list:
			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(__lowerCamelCase     )
						max_depth -= 1
						a__     = median_of_a(__lowerCamelCase					,  __lowerCamelCase					,  start + ((end - start) // 2) + 1					,  end - 1     )
						a__     = partition(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						intro_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						a__     = p
			return insertion_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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



