
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 			A__	(     UpperCamelCase__	,					UpperCamelCase__):
		A_						:	Optional[Any]           =     """resnet"""
		A_						:	Tuple           =     ["""basic""", """bottleneck"""]

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=3	,		_SCREAMING_SNAKE_CASE=64	,		_SCREAMING_SNAKE_CASE=[2_56, 5_12, 10_24, 20_48]	,		_SCREAMING_SNAKE_CASE=[3, 4, 6, 3]	,		_SCREAMING_SNAKE_CASE="bottleneck"	,		_SCREAMING_SNAKE_CASE="relu"	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(**__a			)
			if layer_type not in self.layer_types:
				raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
			__lowerCAmelCase      :						Tuple						      =				num_channels
			__lowerCAmelCase      :						List[Any]						      =				embedding_size
			__lowerCAmelCase      :						int						      =				hidden_sizes
			__lowerCAmelCase      :						Any						      =				depths
			__lowerCAmelCase      :						List[str]						      =				layer_type
			__lowerCAmelCase      :						Any						      =				hidden_act
			__lowerCAmelCase      :						Optional[Any]						      =				downsample_in_first_stage
			__lowerCAmelCase      :						Dict						      =				['stem'] + [f"stage{idx}" for idx in range(1	,		len(__a			) + 1			)]
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Dict						      =				get_aligned_output_features_output_indices(
			    out_features=__a	,		out_indices=__a	,		stage_names=self.stage_names			)




class 			A__	(     UpperCamelCase__):
		A_						:	int           =     version.parse('1.11')

		@property
		def 							__lowerCamelCase							(				self			):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ]			)







		@property
		def 							__lowerCamelCase							(				self			):
			return 1E-3

"""simple docstring"""







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size'	,  [None, 400 * 2**20, 600 * 2**20]		)
@pytest.mark.parametrize('input_in_memory_max_size'	,  ['default', 0, 100 * 2**20, 900 * 2**20]		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	if input_in_memory_max_size != "default":
		monkeypatch.setattr(datasets.config	,  'IN_MEMORY_MAX_SIZE'	,  _UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				datasets.config.IN_MEMORY_MAX_SIZE
	if input_in_memory_max_size == "default":
		assert in_memory_max_size == 0
	else:
		assert in_memory_max_size == input_in_memory_max_size
	if dataset_size and in_memory_max_size:
		__lowerCAmelCase      :						Tuple						      =				dataset_size < in_memory_max_size
	else:
		__lowerCAmelCase      :						str						      =				False
	__lowerCAmelCase      :						Optional[int]						      =				is_small_dataset(_UpperCamelCase		)
	assert result == expected