'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

_lowercase		:				Optional[Any]	       =		{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}





class 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase):
			UpperCamelCase__       = '''bit'''
			UpperCamelCase__       = ['''preactivation''', '''bottleneck''']
			UpperCamelCase__       = ['''SAME''', '''VALID''']







			def __init__(			self       :						List[str]       ,    lowercase_       :						Union[str, Any]=3       ,    lowercase_       :						Tuple=64       ,    lowercase_       :						List[str]=[256, 512, 1024, 2048]       ,    lowercase_       :						List[str]=[3, 4, 6, 3]       ,    lowercase_       :						Any="preactivation"       ,    lowercase_       :						List[Any]="relu"       ,    lowercase_       :						Dict=None       ,    lowercase_       :						List[Any]=32       ,    lowercase_       :						int=0.0       ,    lowercase_       :						Optional[Any]=False       ,    lowercase_       :						Any=32       ,    lowercase_       :						Optional[Any]=1       ,    lowercase_       :						str=None       ,    lowercase_       :						Dict=None       ,    **lowercase_       :						List[Any]       ,    ):
										super().__init__(**lowercase_					)
										if layer_type not in self.layer_types:
																	raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types					)}'''					)
										if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																								lowercase_      :    Union[str, Any]       		= global_padding.upper()
																	else:
																								raise ValueError(f'''Padding strategy {global_padding} not supported'''					)
										lowercase_      :    Union[str, Any]       		= num_channels
										lowercase_      :    str       		= embedding_size
										lowercase_      :    Tuple       		= hidden_sizes
										lowercase_      :    Tuple       		= depths
										lowercase_      :    Tuple       		= layer_type
										lowercase_      :    List[str]       		= hidden_act
										lowercase_      :    str       		= global_padding
										lowercase_      :    List[Any]       		= num_groups
										lowercase_      :    Tuple       		= drop_path_rate
										lowercase_      :    Optional[Any]       		= embedding_dynamic_padding
										lowercase_      :    Optional[Any]       		= output_stride
										lowercase_      :    Union[str, Any]       		= width_factor

										lowercase_      :    int       		= ["""stem"""] + [f'''stage{idx}''' for idx in range(1       ,    len(lowercase_					) + 1					)]
										lowercase_		,		lowercase_      :    Dict       		= get_aligned_output_features_output_indices(
										    out_features=lowercase_       ,    out_indices=lowercase_       ,    stage_names=self.stage_names					)






'''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__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							if input_in_memory_max_size != "default":
														monkeypatch.setattr(datasets.config   ,   """IN_MEMORY_MAX_SIZE"""   ,   UpperCAmelCase__				)
							lowercase_      :    List[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:
														lowercase_      :    str       		= dataset_size < in_memory_max_size
							else:
														lowercase_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





