"""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


_UpperCamelCase   :						List[Any]      =    logging.get_logger(__name__)

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


class      UpperCAmelCase_	(    _a ,							_a):
      lowerCamelCase__		:    Any   =  "resnet"
      lowerCamelCase__		:    Union[str, Any]   =  ["basic", "bottleneck"]


      def __init__(   self		,			a=3		,			a=6_4		,			a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]		,			a=[3, 4, 6, 3]		,			a="bottleneck"		,			a="relu"		,			a=False		,			a=None		,			a=None		,			**a		,			)       ->					Optional[Any]:
             super().__init__(**_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							)}"""							)
             lowercase__			:		int       =	num_channels
             lowercase__			:		str       =	embedding_size
             lowercase__			:		Union[str, Any]       =	hidden_sizes
             lowercase__			:		List[Any]       =	depths
             lowercase__			:		int       =	layer_type
             lowercase__			:		Union[str, Any]       =	hidden_act
             lowercase__			:		Dict       =	downsample_in_first_stage
             lowercase__			:		int       =	['stem'] + [f"""stage{idx}""" for idx in range(1		,			len(_snake_case							) + 1							)]
             lowercase__    ,      lowercase__			:		Any       =	get_aligned_output_features_output_indices(
                 out_features=_snake_case		,			out_indices=_snake_case		,			stage_names=self.stage_names							)





class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    int   =  version.parse("1.11")


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


      @property
      def 		_UpperCAmelCase       (   self							)       ->					float:
             return 1e-3



from typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)