


'''simple docstring'''



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


__lowercase      :		str			=      logging.get_logger(__name__)

__lowercase      :		Union[str, Any]			=      {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}





class 				__UpperCamelCase							(						__A						, __A   ):
	A_           =       "bit"
	A_           =       ["preactivation", "bottleneck"]
	A_           =       ["SAME", "VALID"]




	def __init__(  self		,							__a=3		,							__a=64		,							__a=[256, 512, 1024, 2048]		,							__a=[3, 4, 6, 3]		,							__a="preactivation"		,							__a="relu"		,							__a=None		,							__a=32		,							__a=0.0		,							__a=False		,							__a=32		,							__a=1		,							__a=None		,							__a=None		,							**__a		,							):

						'''simple docstring'''





						super().__init__(**lowerCAmelCase_				)
						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:
																__a     :			Optional[int]          =    global_padding.upper()
											else:
																raise ValueError(f"""Padding strategy {global_padding} not supported"""				)
						__a     :			Optional[Any]          =    num_channels
						__a     :			Optional[int]          =    embedding_size
						__a     :			Optional[Any]          =    hidden_sizes
						__a     :			Dict          =    depths
						__a     :			Dict          =    layer_type
						__a     :			Any          =    hidden_act
						__a     :			Optional[Any]          =    global_padding
						__a     :			List[str]          =    num_groups
						__a     :			Any          =    drop_path_rate
						__a     :			Optional[Any]          =    embedding_dynamic_padding
						__a     :			str          =    output_stride
						__a     :			List[str]          =    width_factor

						__a     :			Tuple          =    ["stem"] + [f"""stage{idx}""" for idx in range(1		,							len(lowerCAmelCase_				) + 1				)]
						__a     :			Union[str, Any]          =    get_aligned_output_features_output_indices(
						    out_features=lowerCAmelCase_		,							out_indices=lowerCAmelCase_		,							stage_names=self.stage_names				)











"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



