"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase									=			logging.get_logger(__name__)

__UpperCamelCase									=			{
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "levit"







       def __init__( self,							lowerCAmelCase__=224,							lowerCAmelCase__=3,							lowerCAmelCase__=3,							lowerCAmelCase__=2,							lowerCAmelCase__=1,							lowerCAmelCase__=16,							lowerCAmelCase__=[128, 256, 384],							lowerCAmelCase__=[4, 8, 12],							lowerCAmelCase__=[4, 4, 4],							lowerCAmelCase__=[16, 16, 16],							lowerCAmelCase__=0,							lowerCAmelCase__=[2, 2, 2],							lowerCAmelCase__=[2, 2, 2],							lowerCAmelCase__=0.02,							**lowerCAmelCase__,							)      ->      Optional[Any]:
            super().__init__(**lowerCAmelCase__)
            snake_case_					       =					image_size
            snake_case_					       =					num_channels
            snake_case_					       =					kernel_size
            snake_case_					       =					stride
            snake_case_					       =					padding
            snake_case_					       =					hidden_sizes
            snake_case_					       =					num_attention_heads
            snake_case_					       =					depths
            snake_case_					       =					key_dim
            snake_case_					       =					drop_path_rate
            snake_case_					       =					patch_size
            snake_case_					       =					attention_ratio
            snake_case_					       =					mlp_ratio
            snake_case_					       =					initializer_range
            snake_case_					       =					[
                ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
            ]




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      version.parse("1.11"   )







       @property
       def 		a_						( self)      ->      Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ])







       @property
       def 		a_						( self)      ->      float:
            return 1e-4



import random






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= [], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase_		)
										elif element > pivot:
															greater.append(UpperCamelCase_		)
										else:
															equal.append(UpperCamelCase_		)
					return less, equal, greater






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					if index >= len(UpperCamelCase_		) or index < 0:
										return None

					SCREAMING_SNAKE_CASE__													= items[random.randint(0	,	len(UpperCamelCase_		) - 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= _partition(UpperCamelCase_	,	UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase_	,	UpperCamelCase_		)
					# must be in larger
					else:
										return quick_select(UpperCamelCase_	,	index - (m + count)		)





