





"""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


__magic_name__       :					Any						=			logging.get_logger(__name__)

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






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[str]										=  """resnet"""
			__lowerCAmelCase     :				List[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						,   ):






									'''simple docstring'''







									super().__init__(**_A    )
									if layer_type not in self.layer_types:
															raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types    )}"""    )
									UpperCamelCase				:		List[Any]				   =			num_channels
									UpperCamelCase				:		str				   =			embedding_size
									UpperCamelCase				:		List[Any]				   =			hidden_sizes
									UpperCamelCase				:		str				   =			depths
									UpperCamelCase				:		Tuple				   =			layer_type
									UpperCamelCase				:		Tuple				   =			hidden_act
									UpperCamelCase				:		str				   =			downsample_in_first_stage
									UpperCamelCase				:		str				   =			["""stem"""] + [f"""stage{idx}""" for idx in range(1						,   len(_A    ) + 1    )]
									UpperCamelCase   ,						UpperCamelCase				:		Union[str, Any]				   =			get_aligned_output_features_output_indices(
									    out_features=_A						,   out_indices=_A						,   stage_names=self.stage_names    )






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  version.parse("""1.11"""	)







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]    )







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return 1e-3










"""simple docstring"""


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = None						,   _A = None						,   _A = True						,   _A = None						,   _A = False						,   _A = None						,   _A = True						,   _A = "arrow"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    split=_A						,   features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   streaming=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[Any]				   =			load_from_cache_file
									UpperCamelCase				:		List[str]				   =			file_format
									UpperCamelCase				:		Optional[Any]				   =			Spark(
									    df=_A						,   features=_A						,   cache_dir=_A						,   working_dir=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split    )
									UpperCamelCase				:		Dict				   =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_A						,   file_format=self._file_format						,   )
									return self.builder.as_dataset(split=self.split    )



