
"""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__ (			_lowerCamelCase ,  _lowerCamelCase):
		A_		: Any            =			'resnet'
		A_		: Optional[int]            =			['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__(**_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    :		int		    =   num_channels
			__lowerCAmelCase    :		Optional[Any]		    =   embedding_size
			__lowerCAmelCase    :		int		    =   hidden_sizes
			__lowerCAmelCase    :		Optional[int]		    =   depths
			__lowerCAmelCase    :		Optional[int]		    =   layer_type
			__lowerCAmelCase    :		int		    =   hidden_act
			__lowerCAmelCase    :		List[str]		    =   downsample_in_first_stage
			__lowerCAmelCase    :		Union[str, Any]		    =   ['stem'] + [f"stage{idx}" for idx in range(1 ,	len(_SCREAMING_SNAKE_CASE				) + 1				)]
			__lowerCAmelCase    :		str		    =   get_aligned_output_features_output_indices(
			    out_features=_SCREAMING_SNAKE_CASE ,	out_indices=_SCREAMING_SNAKE_CASE ,	stage_names=self.stage_names				)




class 				A__ (			_lowerCamelCase):
		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"""







from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip