

'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
				from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__ ,					snake_case__=13 ,					snake_case__=32 ,					snake_case__=3 ,					snake_case__=4 ,					snake_case__=[10, 20, 30, 40] ,					snake_case__=[2, 2, 3, 2] ,					snake_case__=True ,					snake_case__=True ,					snake_case__=37 ,					snake_case__="gelu" ,					snake_case__=10 ,					snake_case__=0.0_2 ,					snake_case__=["stage2", "stage3", "stage4"] ,					snake_case__=[2, 3, 4] ,					snake_case__=None ,					):
		lowerCAmelCase					:    Union[str, Any]      							= parent
		lowerCAmelCase					:    Optional[int]      							= batch_size
		lowerCAmelCase					:    Optional[Any]      							= image_size
		lowerCAmelCase					:    Tuple      							= num_channels
		lowerCAmelCase					:    Tuple      							= num_stages
		lowerCAmelCase					:    List[Any]      							= hidden_sizes
		lowerCAmelCase					:    Any      							= depths
		lowerCAmelCase					:    List[str]      							= is_training
		lowerCAmelCase					:    int      							= use_labels
		lowerCAmelCase					:    Union[str, Any]      							= intermediate_size
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Tuple      							= num_labels
		lowerCAmelCase					:    Optional[Any]      							= initializer_range
		lowerCAmelCase					:    Optional[Any]      							= out_features
		lowerCAmelCase					:    Union[str, Any]      							= out_indices
		lowerCAmelCase					:    Tuple      							= scope



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

		lowerCAmelCase					:    Dict      							= None
		if self.use_labels:
			lowerCAmelCase					:    Dict      							= ids_tensor([self.batch_size] ,					self.num_labels    )

		lowerCAmelCase					:    Tuple      							= self.get_config()

		return config, pixel_values, labels



	def 				lowercase     (					self    ):
		return ConvNextVaConfig(
		    num_channels=self.num_channels ,					hidden_sizes=self.hidden_sizes ,					depths=self.depths ,					num_stages=self.num_stages ,					hidden_act=self.hidden_act ,					is_decoder=snake_case__ ,					initializer_range=self.initializer_range ,					out_features=self.out_features ,					out_indices=self.out_indices ,					num_labels=self.num_labels ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Dict      							= ConvNextVaModel(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Tuple      							= model(snake_case__    )
		# expected last hidden states: B, C, H // 32, W // 32
		self.parent.assertEqual(
		    result.last_hidden_state.shape ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Any      							= ConvNextVaForImageClassification(snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    str      							= model(snake_case__ ,					labels=snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Any      							= ConvNextVaBackbone(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Tuple      							= model(snake_case__    )

		# verify hidden states
		self.parent.assertEqual(len(result.feature_maps    ) ,					len(config.out_features    )    )
		self.parent.assertListEqual(list(result.feature_maps[0].shape    ) ,					[self.batch_size, self.hidden_sizes[1], 4, 4]    )

		# verify channels
		self.parent.assertEqual(len(model.channels    ) ,					len(config.out_features    )    )
		self.parent.assertListEqual(model.channels ,					config.hidden_sizes[1:]    )

		# verify backbone works with out_features=None
		lowerCAmelCase					:    str      							= None
		lowerCAmelCase					:    List[Any]      							= ConvNextVaBackbone(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    List[Any]      							= model(snake_case__    )

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps    ) ,					1    )
		self.parent.assertListEqual(list(result.feature_maps[0].shape    ) ,					[self.batch_size, self.hidden_sizes[-1], 1, 1]    )

		# verify channels
		self.parent.assertEqual(len(model.channels    ) ,					1    )
		self.parent.assertListEqual(model.channels ,					[config.hidden_sizes[-1]]    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.prepare_config_and_inputs()
		lowerCAmelCase					:    Optional[int]      							= config_and_inputs
		lowerCAmelCase					:    List[Any]      							= {"pixel_values": pixel_values}
		return config, inputs_dict



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.prepare_config_and_inputs()
		lowerCAmelCase					:    Dict      							= config_and_inputs
		lowerCAmelCase					:    Optional[Any]      							= {"pixel_values": pixel_values, "labels": labels}
		return config, inputs_dict



@require_torch
class  lowerCAmelCase				(				_UpperCamelCase      ,				_UpperCamelCase      ,				unittest.TestCase							):
	_lowerCamelCase   :					Dict			      = (
	    (
	        ConvNextVaModel,
	        ConvNextVaForImageClassification,
	        ConvNextVaBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCamelCase   :					str			      = (
	    {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_lowerCamelCase   :					List[str]			      = False
	_lowerCamelCase   :					List[Any]			      = False
	_lowerCamelCase   :					str			      = False
	_lowerCamelCase   :					Optional[int]			      = False
	_lowerCamelCase   :					int			      = False



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= ConvNextVaModelTester(self    )
		lowerCAmelCase					:    Optional[Any]      							= ConfigTester(self ,					config_class=snake_case__ ,					has_text_modality=snake_case__ ,					hidden_size=37    )



	def 				lowercase     (					self    ):
		self.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()



	def 				lowercase     (					self    ):
		return



	@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'    )
	def 				lowercase     (					self    ):
		pass



	@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'    )
	def 				lowercase     (					self    ):
		pass



	@unittest.skip(reason='ConvNextV2 does not use feedforward chunking'    )
	def 				lowercase     (					self    ):
		pass



	def 				lowercase     (					self    ):
		if not self.model_tester.is_training:
			return

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    List[Any]      							= self.model_tester.prepare_config_and_inputs_with_labels()
			lowerCAmelCase					:    List[str]      							= True

			if model_class.__name__ in [
			    *get_values(snake_case__    ),
			    *get_values(snake_case__    ),
			]:
				continue

			lowerCAmelCase					:    List[str]      							= model_class(snake_case__    )
			model.to(snake_case__    )
			model.train()
			lowerCAmelCase					:    Optional[int]      							= self._prepare_for_class(snake_case__ ,					snake_case__ ,					return_labels=snake_case__    )
			lowerCAmelCase					:    Union[str, Any]      							= model(**snake_case__    ).loss
			loss.backward()



	def 				lowercase     (					self    ):
		if not self.model_tester.is_training:
			return

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Tuple      							= self.model_tester.prepare_config_and_inputs_with_labels()
			lowerCAmelCase					:    Optional[Any]      							= False
			lowerCAmelCase					:    Dict      							= True

			if (
			    model_class.__name__
			    in [*get_values(snake_case__    ), *get_values(snake_case__    )]
			    or not model_class.supports_gradient_checkpointing
			):
				continue

			lowerCAmelCase					:    Union[str, Any]      							= model_class(snake_case__    )
			model.to(snake_case__    )
			model.gradient_checkpointing_enable()
			model.train()
			lowerCAmelCase					:    str      							= self._prepare_for_class(snake_case__ ,					snake_case__ ,					return_labels=snake_case__    )
			lowerCAmelCase					:    str      							= model(**snake_case__    ).loss
			loss.backward()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Union[str, Any]      							= model_class(snake_case__    )
			lowerCAmelCase					:    int      							= inspect.signature(model.forward    )
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			lowerCAmelCase					:    str      							= [*signature.parameters.keys()]

			lowerCAmelCase					:    Optional[int]      							= ["pixel_values"]
			self.assertListEqual(arg_names[:1] ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case__    )



	def 				lowercase     (					self    ):
		def check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    Any      							= model_class(snake_case__    )
			model.to(snake_case__    )
			model.eval()

			with torch.no_grad():
				lowerCAmelCase					:    Tuple      							= model(**self._prepare_for_class(snake_case__ ,					snake_case__    )    )

			lowerCAmelCase					:    Optional[Any]      							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			lowerCAmelCase					:    Dict      							= self.model_tester.num_stages
			self.assertEqual(len(snake_case__    ) ,					expected_num_stages + 1    )

			# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]    ) ,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,					)

		lowerCAmelCase					:    Optional[int]      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Union[str, Any]      							= True
			check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			lowerCAmelCase					:    Optional[Any]      							= True

			check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowerCAmelCase					:    List[str]      							= ConvNextVaModel.from_pretrained(snake_case__    )
			self.assertIsNotNone(snake_case__    )


def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image






@require_torch
@require_vision
class  lowerCAmelCase				(				unittest.TestCase							):



	@cached_property
	def 				lowercase     (					self    ):
		return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'    ) if is_vision_available() else None



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'    ).to(snake_case__    )

		lowerCAmelCase					:    Dict      							= self.default_image_processor
		lowerCAmelCase					:    int      							= prepare_img()
		lowerCAmelCase					:    Optional[Any]      							= preprocessor(images=snake_case__ ,					return_tensors='pt'    ).to(snake_case__    )

		# forward pass
		with torch.no_grad():
			lowerCAmelCase					:    Tuple      							= model(**snake_case__    )

		# verify the logits
		lowerCAmelCase					:    Optional[int]      							= torch.Size((1, 1000)    )
		self.assertEqual(outputs.logits.shape ,					snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]    ).to(snake_case__    )
		self.assertTrue(torch.allclose(outputs.logits[0, :3] ,					snake_case__ ,					atol=1e-4    )    )




'''simple docstring'''




import argparse
import os
import re


_lowerCAmelCase  :		Dict		     =	'src/diffusers'

# Pattern that looks at the indentation in a line.
_lowerCAmelCase  :		str		     =	re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
_lowerCAmelCase  :		Any		     =	re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_lowerCAmelCase  :		List[Any]		     =	re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
_lowerCAmelCase  :		int		     =	re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_lowerCAmelCase  :		Optional[Any]		     =	re.compile(r'\[([^\]]+)\]')

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    Any      							= _re_indent.search(_A				)
	return "" if search is None else search.groups()[0]

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Any=""					,   _A    :				List[str]=None					,   _A    :				Any=None				)    ->      Tuple:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= 0
	lowerCAmelCase					:    Tuple      							= code.split('\n'				)
	if start_prompt is not None:
		while not lines[index].startswith(_A				):
			index += 1
		lowerCAmelCase					:    Optional[int]      							= ['\n'.join(lines[:index]				)]
	else:
		lowerCAmelCase					:    int      							= []

	# We split into blocks until we get to the `end_prompt` (or the end of the block).
	lowerCAmelCase					:    Tuple      							= [lines[index]]
	index += 1
	while index < len(_A				) and (end_prompt is None or not lines[index].startswith(_A				)):
		if len(lines[index]				) > 0 and get_indent(lines[index]				) == indent_level:
			if len(_A				) > 0 and get_indent(current_block[-1]				).startswith(indent_level + ' '				):
				current_block.append(lines[index]				)
				blocks.append('\n'.join(_A				)				)
				if index < len(_A				) - 1:
					lowerCAmelCase					:    List[Any]      							= [lines[index + 1]]
					index += 1
				else:
					lowerCAmelCase					:    int      							= []
			else:
				blocks.append('\n'.join(_A				)				)
				lowerCAmelCase					:    Any      							= [lines[index]]
		else:
			current_block.append(lines[index]				)
		index += 1

	# Adds current block if it's nonempty.
	if len(_A				) > 0:
		blocks.append('\n'.join(_A				)				)

	# Add final block after end_prompt if provided.
	if end_prompt is not None and index < len(_A				):
		blocks.append('\n'.join(lines[index:]				)				)

	return blocks

def 			__UpperCamelCase    (    _A    :				Dict				)    ->      List[Any]:




	"""simple docstring"""





	def _inner(_A    :				Tuple				):
		return key(_A				).lower().replace('_'					,   ''				)

	return _inner

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				Any=None				)    ->      Optional[Any]:




	"""simple docstring"""





	def noop(_A    :				Any				):
		return x

	if key is None:
		lowerCAmelCase					:    List[str]      							= noop
	# Constants are all uppercase, they go first.
	lowerCAmelCase					:    str      							= [obj for obj in objects if key(_A				).isupper()]
	# Classes are not all uppercase but start with a capital, they go second.
	lowerCAmelCase					:    List[str]      							= [obj for obj in objects if key(_A				)[0].isupper() and not key(_A				).isupper()]
	# Functions begin with a lowercase, they go last.
	lowerCAmelCase					:    Optional[Any]      							= [obj for obj in objects if not key(_A				)[0].isupper()]

	lowerCAmelCase					:    Tuple      							= ignore_underscore(_A				)
	return sorted(_A					,   key=_A				) + sorted(_A					,   key=_A				) + sorted(_A					,   key=_A				)

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      int:




	"""simple docstring"""





	def _replace(_A    :				List[Any]				):
		lowerCAmelCase					:    List[Any]      							= match.groups()[0]
		if "," not in imports:
			return F"[{imports}]"
		lowerCAmelCase					:    Dict      							= [part.strip().replace('"'					,   ''				) for part in imports.split(','				)]
		# We will have a final empty element if the line finished with a comma.
		if len(keys[-1]				) == 0:
			lowerCAmelCase					:    List[str]      							= keys[:-1]
		return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(_A				)]				) + "]"

	lowerCAmelCase					:    Optional[int]      							= import_statement.split('\n'				)
	if len(_A				) > 3:
		# Here we have to sort internal imports that are on several lines (one per name):
		# key: [
		#     "object1",
		#     "object2",
		#     ...
		# ]

		# We may have to ignore one or two lines on each side.
		lowerCAmelCase					:    Optional[Any]      							= 2 if lines[1].strip() == '[' else 1
		lowerCAmelCase					:    List[str]      							= [(i, _re_strip_line.search(_A				).groups()[0]) for i, line in enumerate(lines[idx:-idx]				)]
		lowerCAmelCase					:    Optional[Any]      							= sort_objects(_A					,   key=lambda _A				: x[1]				)
		lowerCAmelCase					:    Dict      							= [lines[x[0] + idx] for x in sorted_indices]
		return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]				)
	elif len(_A				) == 3:
		# Here we have to sort internal imports that are on one separate line:
		# key: [
		#     "object1", "object2", ...
		# ]
		if _re_bracket_content.search(lines[1]				) is not None:
			lowerCAmelCase					:    Optional[int]      							= _re_bracket_content.sub(_replace					,   lines[1]				)
		else:
			lowerCAmelCase					:    List[Any]      							= [part.strip().replace('"'					,   ''				) for part in lines[1].split(','				)]
			# We will have a final empty element if the line finished with a comma.
			if len(keys[-1]				) == 0:
				lowerCAmelCase					:    int      							= keys[:-1]
			lowerCAmelCase					:    Tuple      							= get_indent(lines[1]				) + ', '.join([F"\"{k}\"" for k in sort_objects(_A				)]				)
		return "\n".join(_A				)
	else:
		# Finally we have to deal with imports fitting on one line
		lowerCAmelCase					:    Union[str, Any]      							= _re_bracket_content.sub(_replace					,   _A				)
		return import_statement

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Tuple=True				)    ->      Optional[Any]:




	"""simple docstring"""





	with open(_A					,   'r'				) as f:
		lowerCAmelCase					:    Optional[int]      							= f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	lowerCAmelCase					:    List[Any]      							= split_code_in_indented_blocks(
	    _A					,   start_prompt='_import_structure = {'					,   end_prompt='if TYPE_CHECKING:'				)

	# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
	for block_idx in range(1					,   len(_A				) - 1				):
		# Check if the block contains some `_import_structure`s thingy to sort.
		lowerCAmelCase					:    List[str]      							= main_blocks[block_idx]
		lowerCAmelCase					:    Union[str, Any]      							= block.split('\n'				)

		# Get to the start of the imports.
		lowerCAmelCase					:    Optional[Any]      							= 0
		while line_idx < len(_A				) and "_import_structure" not in block_lines[line_idx]:
			# Skip dummy import blocks
			if "import dummy" in block_lines[line_idx]:
				lowerCAmelCase					:    Optional[Any]      							= len(_A				)
			else:
				line_idx += 1
		if line_idx >= len(_A				):
			continue

		# Ignore beginning and last line: they don't contain anything.
		lowerCAmelCase					:    str      							= '\n'.join(block_lines[line_idx:-1]				)
		lowerCAmelCase					:    str      							= get_indent(block_lines[1]				)
		# Slit the internal block into blocks of indent level 1.
		lowerCAmelCase					:    Optional[Any]      							= split_code_in_indented_blocks(_A					,   indent_level=_A				)
		# We have two categories of import key: list or _import_structure[key].append/extend
		lowerCAmelCase					:    Union[str, Any]      							= _re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
		# Grab the keys, but there is a trap: some lines are empty or just comments.
		lowerCAmelCase					:    int      							= [(pattern.search(_A				).groups()[0] if pattern.search(_A				) is not None else None) for b in internal_blocks]
		# We only sort the lines with a key.
		lowerCAmelCase					:    Dict      							= [(i, key) for i, key in enumerate(_A				) if key is not None]
		lowerCAmelCase					:    List[Any]      							= [x[0] for x in sorted(_A					,   key=lambda _A				: x[1]				)]

		# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
		lowerCAmelCase					:    int      							= 0
		lowerCAmelCase					:    Dict      							= []
		for i in range(len(_A				)				):
			if keys[i] is None:
				reordered_blocks.append(internal_blocks[i]				)
			else:
				lowerCAmelCase					:    str      							= sort_objects_in_import(internal_blocks[sorted_indices[count]]				)
				reordered_blocks.append(_A				)
				count += 1

        # And we put our main block back together with its first and last line.
		lowerCAmelCase					:    str      							= '\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]				)

	if code != "\n".join(_A				):
		if check_only:
			return True
		else:
			print(F"Overwriting {file}."				)
			with open(_A					,   'w'				) as f:
				f.write('\n'.join(_A				)				)

def 			__UpperCamelCase    (    _A    :				Tuple=True				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	for root, _, files in os.walk(_A				):
		if "__init__.py" in files:
			lowerCAmelCase					:    Any      							= sort_imports(os.path.join(_A					,   '__init__.py'				)					,   check_only=_A				)
			if result:
				lowerCAmelCase					:    Optional[Any]      							= [os.path.join(_A					,   '__init__.py'				)]
	if len(_A				) > 0:
		raise ValueError(F"Would overwrite {len(_A				)} files, run `make style`."				)


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

				sort_imports_in_all_inits(check_only=args.check_only)
