


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							(    _lowercase					,						_lowercase):
	_a  :  Optional[Any]				   =							'''resnet'''
	_a  :  int				   =							['''basic''', '''bottleneck''']




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=3		, _SCREAMING_SNAKE_CASE						:		List[Any]=64		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=[256, 512, 1024, 2048]		, _SCREAMING_SNAKE_CASE						:		Any=[3, 4, 6, 3]		, _SCREAMING_SNAKE_CASE						:		str="bottleneck"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="relu"		, _SCREAMING_SNAKE_CASE						:		Optional[int]=False		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=None		, _SCREAMING_SNAKE_CASE						:		str=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(**_SCREAMING_SNAKE_CASE		)
			if layer_type not in self.layer_types:
					raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
			lowerCAmelCase__       :      List[str]   					= num_channels
			lowerCAmelCase__       :      List[Any]   					= embedding_size
			lowerCAmelCase__       :      Tuple   					= hidden_sizes
			lowerCAmelCase__       :      Union[str, Any]   					= depths
			lowerCAmelCase__       :      Tuple   					= layer_type
			lowerCAmelCase__       :      Union[str, Any]   					= hidden_act
			lowerCAmelCase__       :      int   					= downsample_in_first_stage
			lowerCAmelCase__       :      int   					= ['''stem'''] + [F'stage{idx}' for idx in range(1		, len(_SCREAMING_SNAKE_CASE		) + 1		)]
			lowerCAmelCase__       :      Any   					= get_aligned_output_features_output_indices(
			    out_features=_SCREAMING_SNAKE_CASE		, out_indices=_SCREAMING_SNAKE_CASE		, stage_names=self.stage_names		)





class  _a							(    _lowercase):
	_a  :  Any				   =							version.parse('''1.11''')




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




	@property
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	float:
			return 1E-3








import timeit

import numpy as np

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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		def wrapper(*_a     ,       **_a	):
				lowerCAmelCase__       :      List[str]   					= timeit.default_timer()
				lowerCAmelCase__       :      List[Any]   					= func(*_a     ,       **_a	)
				lowerCAmelCase__       :      Any   					= timeit.default_timer() - starttime
				return delta

		lowerCAmelCase__       :      Any   					= func.__name__

		return wrapper






def 					lowerCamelCase_   (	_a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__       :      str   					= seq_shapes or {}
		for i in range(_a	):
				lowerCAmelCase__       :      List[str]   					= {}
				for col_id, (k, v) in enumerate(features.items()	):
						if isinstance(_a     ,       _ArrayXD	):
								lowerCAmelCase__       :      List[str]   					= np.random.rand(*v.shape	).astype(v.dtype	)
						elif isinstance(_a     ,       datasets.Value	):
								if v.dtype == "string":
										lowerCAmelCase__       :      Dict   					= '''The small grey turtle was surprisingly fast when challenged.'''
								else:
										lowerCAmelCase__       :      Any   					= np.random.randint(10     ,       size=1	).astype(v.dtype	).item()
						elif isinstance(_a     ,       datasets.Sequence	):
								while isinstance(_a     ,       datasets.Sequence	):
										lowerCAmelCase__       :      Optional[int]   					= v.feature
								lowerCAmelCase__       :      str   					= seq_shapes[k]
								lowerCAmelCase__       :      Any   					= np.random.rand(*_a	).astype(v.dtype	)
						lowerCAmelCase__       :      int   					= data

				dummy_data.append((i, example)	)

		return dummy_data






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      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:
						lowerCAmelCase__       :      Optional[int]   					= features.encode_example(_a	)
						writer.write(_a	)

				lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= 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}.'	)

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

		return dataset




