



from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_lowercase						: Union[str, Any]     						=logging.get_logger(__name__)

# General docstring
_lowercase						: Union[str, Any]     						="""RegNetConfig"""

# Base docstring
_lowercase						: Union[str, Any]     						="""facebook/regnet-y-040"""
_lowercase						: Dict     						=[1, 1088, 7, 7]

# Image classification docstring
_lowercase						: List[Any]     						="""facebook/regnet-y-040"""
_lowercase						: Optional[Any]     						="""tabby, tabby cat"""

_lowercase						: str     						=[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  __lowercase = 3   ,  __lowercase = 1   ,  __lowercase = 1   ,  __lowercase = "relu"   ,  **__lowercase   ,  )   -> Dict:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									# The padding and conv has been verified in
									# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
									a__  :		List[str]					=    tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2    )
									a__  :		Tuple					=    tf.keras.layers.ConvaD(
									    filters=__lowercase   ,  kernel_size=__lowercase   ,  strides=__lowercase   ,  padding="""VALID"""   ,  groups=__lowercase   ,  use_bias=__lowercase   ,  name="""convolution"""   ,  )
									a__  :		Any					=    tf.keras.layers.BatchNormalization(epsilon=1E-5   ,  momentum=0.9   ,  name="""normalization"""    )
									a__  :		List[str]					=    ACTaFN[activation] if activation is not None else tf.identity

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Dict:
									"""simple docstring"""




									a__  :		Union[str, Any]					=    self.convolution(self.padding(__lowercase    )    )
									a__  :		Dict					=    self.normalization(__lowercase    )
									a__  :		Optional[int]					=    self.activation(__lowercase    )
									return hidden_state




class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  **__lowercase    )   -> str:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									a__  :		List[Any]					=    config.num_channels
									a__  :		Optional[int]					=    TFRegNetConvLayer(
									    out_channels=config.embedding_size   ,  kernel_size=3   ,  stride=2   ,  activation=config.hidden_act   ,  name="""embedder"""   ,  )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Optional[Any]:
									"""simple docstring"""




									a__  :		List[Any]					=    shape_list(__lowercase    )[1]
									if tf.executing_eagerly() and num_channels != self.num_channels:
												raise ValueError(
												    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""    )

									# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
									# So change the input format from `NCHW` to `NHWC`.
									# shape = (batch_size, in_height, in_width, in_channels=num_channels)
									a__  :		Union[str, Any]					=    tf.transpose(__lowercase   ,  perm=(0, 2, 3, 1)    )
									a__  :		Tuple					=    self.embedder(__lowercase    )
									return hidden_state




class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  __lowercase = 2   ,  **__lowercase    )   -> Optional[Any]:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									a__  :		List[str]					=    tf.keras.layers.ConvaD(
									    filters=__lowercase   ,  kernel_size=1   ,  strides=__lowercase   ,  use_bias=__lowercase   ,  name="""convolution"""    )
									a__  :		Dict					=    tf.keras.layers.BatchNormalization(epsilon=1E-5   ,  momentum=0.9   ,  name="""normalization"""    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase = False    )   -> tf.Tensor:
									"""simple docstring"""




									return self.normalization(self.convolution(__lowercase    )   ,  training=__lowercase    )




class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  __lowercase   ,  **__lowercase    )   -> Union[str, Any]:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									a__  :		int					=    tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase   ,  name="""pooler"""    )
									a__  :		List[str]					=    [
									    tf.keras.layers.ConvaD(filters=__lowercase   ,  kernel_size=1   ,  activation="""relu"""   ,  name="""attention.0"""    ),
									    tf.keras.layers.ConvaD(filters=__lowercase   ,  kernel_size=1   ,  activation="""sigmoid"""   ,  name="""attention.2"""    ),
									]

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Any:
									"""simple docstring"""




									a__  :		Union[str, Any]					=    self.pooler(__lowercase    )
									for layer_module in self.attention:
												a__  :		Optional[int]					=    layer_module(__lowercase    )
									a__  :		Any					=    hidden_state * pooled
									return hidden_state




class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase = 1   ,  **__lowercase    )   -> Any:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									a__  :		Optional[Any]					=    in_channels != out_channels or stride != 1
									a__  :		Dict					=    max(1   ,  out_channels // config.groups_width    )
									a__  :		Dict					=    (
									    TFRegNetShortCut(__lowercase   ,  stride=__lowercase   ,  name="""shortcut"""    )
									    if should_apply_shortcut
									    else tf.keras.layers.Activation("""linear"""   ,  name="""shortcut"""    )
									)
									# `self.layers` instead of `self.layer` because that is a reserved argument.
									a__  :		Dict					=    [
									    TFRegNetConvLayer(__lowercase   ,  kernel_size=1   ,  activation=config.hidden_act   ,  name="""layer.0"""    ),
									    TFRegNetConvLayer(
									        __lowercase   ,  stride=__lowercase   ,  groups=__lowercase   ,  activation=config.hidden_act   ,  name="""layer.1"""    ),
									    TFRegNetConvLayer(__lowercase   ,  kernel_size=1   ,  activation=__lowercase   ,  name="""layer.2"""    ),
									]
									a__  :		int					=    ACTaFN[config.hidden_act]

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Dict:
									"""simple docstring"""




									a__  :		int					=    hidden_state
									for layer_module in self.layers:
												a__  :		int					=    layer_module(__lowercase    )
									a__  :		Optional[int]					=    self.shortcut(__lowercase    )
									hidden_state += residual
									a__  :		int					=    self.activation(__lowercase    )
									return hidden_state




class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase = 1   ,  **__lowercase    )   -> int:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									a__  :		Dict					=    in_channels != out_channels or stride != 1
									a__  :		Tuple					=    max(1   ,  out_channels // config.groups_width    )
									a__  :		Dict					=    (
									    TFRegNetShortCut(__lowercase   ,  stride=__lowercase   ,  name="""shortcut"""    )
									    if should_apply_shortcut
									    else tf.keras.layers.Activation("""linear"""   ,  name="""shortcut"""    )
									)
									a__  :		Tuple					=    [
									    TFRegNetConvLayer(__lowercase   ,  kernel_size=1   ,  activation=config.hidden_act   ,  name="""layer.0"""    ),
									    TFRegNetConvLayer(
									        __lowercase   ,  stride=__lowercase   ,  groups=__lowercase   ,  activation=config.hidden_act   ,  name="""layer.1"""    ),
									    TFRegNetSELayer(__lowercase   ,  reduced_channels=int(round(in_channels / 4    )    )   ,  name="""layer.2"""    ),
									    TFRegNetConvLayer(__lowercase   ,  kernel_size=1   ,  activation=__lowercase   ,  name="""layer.3"""    ),
									]
									a__  :		str					=    ACTaFN[config.hidden_act]

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Tuple:
									"""simple docstring"""




									a__  :		int					=    hidden_state
									for layer_module in self.layers:
												a__  :		int					=    layer_module(__lowercase    )
									a__  :		int					=    self.shortcut(__lowercase    )
									hidden_state += residual
									a__  :		List[Any]					=    self.activation(__lowercase    )
									return hidden_state




class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase = 2   ,  __lowercase = 2   ,  **__lowercase    )   -> Dict:
									"""simple docstring"""




									super().__init__(**__lowercase    )

									a__  :		str					=    TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
									a__  :		Any					=    [
									    # downsampling is done in the first layer with stride of 2
									    layer(__lowercase   ,  __lowercase   ,  __lowercase   ,  stride=__lowercase   ,  name="""layers.0"""    ),
									    *[layer(__lowercase   ,  __lowercase   ,  __lowercase   ,  name=F'''layers.{i+1}'''    ) for i in range(depth - 1    )],
									]

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Optional[int]:
									"""simple docstring"""




									for layer_module in self.layers:
												a__  :		List[str]					=    layer_module(__lowercase    )
									return hidden_state




class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  **__lowercase    )   -> List[str]:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									a__  :		Tuple					=    []
									# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
									self.stages.append(
									    TFRegNetStage(
									        __lowercase   ,  config.embedding_size   ,  config.hidden_sizes[0]   ,  stride=2 if config.downsample_in_first_stage else 1   ,  depth=config.depths[0]   ,  name="""stages.0"""   ,  )    )
									a__  :		str					=    zip(config.hidden_sizes   ,  config.hidden_sizes[1:]    )
									for i, ((in_channels, out_channels), depth) in enumerate(zip(__lowercase   ,  config.depths[1:]    )    ):
												self.stages.append(TFRegNetStage(__lowercase   ,  __lowercase   ,  __lowercase   ,  depth=__lowercase   ,  name=F'''stages.{i+1}'''    )    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase = False   ,  __lowercase = True    )   -> TFBaseModelOutputWithNoAttention:
									"""simple docstring"""




									a__  :		Tuple					=    () if output_hidden_states else None

									for stage_module in self.stages:
												if output_hidden_states:
															a__  :		Tuple					=    hidden_states + (hidden_state,)

												a__  :		Optional[Any]					=    stage_module(__lowercase    )

									if output_hidden_states:
												a__  :		Optional[int]					=    hidden_states + (hidden_state,)

									if not return_dict:
												return tuple(v for v in [hidden_state, hidden_states] if v is not None    )

									return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowercase   ,  hidden_states=__lowercase    )




@keras_serializable
class   snake_case__							(tf.keras.layers.Layer						):







						"""simple docstring"""




						__lowerCAmelCase	:int         =	RegNetConfig

						def __init__(  self   ,  __lowercase   ,  **__lowercase    )   -> Optional[Any]:
									"""simple docstring"""




									super().__init__(**__lowercase    )
									a__  :		List[str]					=    config
									a__  :		List[str]					=    TFRegNetEmbeddings(__lowercase   ,  name="""embedder"""    )
									a__  :		Any					=    TFRegNetEncoder(__lowercase   ,  name="""encoder"""    )
									a__  :		Optional[int]					=    tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase   ,  name="""pooler"""    )

						@unpack_inputs
						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = False   ,  )   -> TFBaseModelOutputWithPoolingAndNoAttention:
									"""simple docstring"""




									a__  :		int					=    (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									a__  :		Optional[Any]					=    return_dict if return_dict is not None else self.config.use_return_dict

									a__  :		Union[str, Any]					=    self.embedder(__lowercase   ,  training=__lowercase    )

									a__  :		List[Any]					=    self.encoder(
									    __lowercase   ,  output_hidden_states=__lowercase   ,  return_dict=__lowercase   ,  training=__lowercase    )

									a__  :		List[str]					=    encoder_outputs[0]
									a__  :		List[str]					=    self.pooler(__lowercase    )

									# Change to NCHW output format have uniformity in the modules
									a__  :		Any					=    tf.transpose(__lowercase   ,  perm=(0, 3, 1, 2)    )
									a__  :		Dict					=    tf.transpose(__lowercase   ,  perm=(0, 3, 1, 2)    )

									# Change the other hidden state outputs to NCHW as well
									if output_hidden_states:
												a__  :		Optional[int]					=    tuple([tf.transpose(__lowercase   ,  perm=(0, 3, 1, 2)    ) for h in encoder_outputs[1]]    )

									if not return_dict:
												return (last_hidden_state, pooled_output) + encoder_outputs[1:]

									return TFBaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=__lowercase   ,  pooler_output=__lowercase   ,  hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states   ,  )




class   snake_case__							(_UpperCamelCase						):







						"""simple docstring"""




						__lowerCAmelCase	:Optional[int]         =	RegNetConfig
						__lowerCAmelCase	:str         =	"""regnet"""
						__lowerCAmelCase	:Tuple         =	"""pixel_values"""

						@property
						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Any:
									"""simple docstring"""




									return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)   ,  dtype=tf.floataa    )}




_lowercase						: Optional[Any]     						=R"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

_lowercase						: str     						=R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."   ,      _UpperCamelCase   ,      )
class   snake_case__							(_UpperCamelCase						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  *__lowercase   ,  **__lowercase    )   -> Any:
									"""simple docstring"""




									super().__init__(__lowercase   ,  *__lowercase   ,  **__lowercase    )
									a__  :		int					=    TFRegNetMainLayer(__lowercase   ,  name="""regnet"""    )

						@unpack_inputs
						@add_start_docstrings_to_model_forward(__lowercase    )
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC   ,  output_type=__lowercase   ,  config_class=_CONFIG_FOR_DOC   ,  modality="""vision"""   ,  expected_output=_EXPECTED_OUTPUT_SHAPE   ,  )
						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase=False   ,  )   -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
									"""simple docstring"""




									a__  :		Optional[int]					=    (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									a__  :		Optional[int]					=    return_dict if return_dict is not None else self.config.use_return_dict

									a__  :		List[Any]					=    self.regnet(
									    pixel_values=__lowercase   ,  output_hidden_states=__lowercase   ,  return_dict=__lowercase   ,  training=__lowercase   ,  )
									if not return_dict:
												return (outputs[0],) + outputs[1:]

									return TFBaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=outputs.last_hidden_state   ,  pooler_output=outputs.pooler_output   ,  hidden_states=outputs.hidden_states   ,  )




@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "   ,      _UpperCamelCase   ,      )
class   snake_case__							(_UpperCamelCase   ,      _UpperCamelCase						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  *__lowercase   ,  **__lowercase    )   -> Optional[int]:
									"""simple docstring"""




									super().__init__(__lowercase   ,  *__lowercase   ,  **__lowercase    )
									a__  :		str					=    config.num_labels
									a__  :		Dict					=    TFRegNetMainLayer(__lowercase   ,  name="""regnet"""    )
									# classification head
									a__  :		Optional[int]					=    [
									    tf.keras.layers.Flatten(),
									    tf.keras.layers.Dense(config.num_labels   ,  name="""classifier.1"""    ) if config.num_labels > 0 else tf.identity,
									]

						@unpack_inputs
						@add_start_docstrings_to_model_forward(__lowercase    )
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,  output_type=__lowercase   ,  config_class=_CONFIG_FOR_DOC   ,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,  )
						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase=False   ,  )   -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
									"""simple docstring"""




									a__  :		Tuple					=    (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									a__  :		Dict					=    return_dict if return_dict is not None else self.config.use_return_dict

									a__  :		List[Any]					=    self.regnet(
									    __lowercase   ,  output_hidden_states=__lowercase   ,  return_dict=__lowercase   ,  training=__lowercase    )

									a__  :		List[str]					=    outputs.pooler_output if return_dict else outputs[1]

									a__  :		Tuple					=    self.classifier[0](__lowercase    )
									a__  :		Optional[Any]					=    self.classifier[1](__lowercase    )

									a__  :		int					=    None if labels is None else self.hf_compute_loss(labels=__lowercase   ,  logits=__lowercase    )

									if not return_dict:
												a__  :		Tuple					=    (logits,) + outputs[2:]
												return ((loss,) + output) if loss is not None else output

									return TFSequenceClassifierOutput(loss=__lowercase   ,  logits=__lowercase   ,  hidden_states=outputs.hidden_states    )











import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

