

'''simple docstring'''




import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertModel,
				)



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__ ,					snake_case__=13 ,					snake_case__=7 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=99 ,					snake_case__=64 ,					snake_case__=32 ,					snake_case__=5 ,					snake_case__=4 ,					snake_case__=37 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=16 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=3 ,					snake_case__=4 ,					snake_case__=None ,					):
		lowerCAmelCase					:    str      							= parent
		lowerCAmelCase					:    Optional[int]      							= batch_size
		lowerCAmelCase					:    Optional[Any]      							= seq_length
		lowerCAmelCase					:    Optional[Any]      							= is_training
		lowerCAmelCase					:    Dict      							= use_input_mask
		lowerCAmelCase					:    Tuple      							= use_token_type_ids
		lowerCAmelCase					:    int      							= use_labels
		lowerCAmelCase					:    int      							= vocab_size
		lowerCAmelCase					:    Any      							= hidden_size
		lowerCAmelCase					:    Optional[Any]      							= embedding_size
		lowerCAmelCase					:    int      							= num_hidden_layers
		lowerCAmelCase					:    List[str]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= intermediate_size
		lowerCAmelCase					:    Dict      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    int      							= attention_probs_dropout_prob
		lowerCAmelCase					:    List[Any]      							= max_position_embeddings
		lowerCAmelCase					:    int      							= type_vocab_size
		lowerCAmelCase					:    List[str]      							= type_sequence_label_size
		lowerCAmelCase					:    Dict      							= initializer_range
		lowerCAmelCase					:    Any      							= num_labels
		lowerCAmelCase					:    str      							= num_choices
		lowerCAmelCase					:    int      							= scope



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size    )

		lowerCAmelCase					:    Union[str, Any]      							= None
		if self.use_input_mask:
			lowerCAmelCase					:    Dict      							= random_attention_mask([self.batch_size, self.seq_length]    )

		lowerCAmelCase					:    Optional[int]      							= None
		if self.use_token_type_ids:
			lowerCAmelCase					:    List[str]      							= ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size    )

		lowerCAmelCase					:    Optional[Any]      							= None
		lowerCAmelCase					:    Optional[Any]      							= None
		lowerCAmelCase					:    Dict      							= None
		if self.use_labels:
			lowerCAmelCase					:    List[Any]      							= ids_tensor([self.batch_size] ,					self.type_sequence_label_size    )
			lowerCAmelCase					:    Any      							= ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels    )
			lowerCAmelCase					:    Union[str, Any]      							= ids_tensor([self.batch_size] ,					self.num_choices    )

		lowerCAmelCase					:    Tuple      							= self.get_config()

		return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



	def 				lowercase     (					self    ):
		return MobileBertConfig(
		    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					embedding_size=self.embedding_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=snake_case__ ,					initializer_range=self.initializer_range ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= MobileBertModel(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    int      							= model(snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= model(snake_case__ ,					token_type_ids=snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= model(snake_case__    )

		self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)    )
		self.parent.assertEqual(result.pooler_output.shape ,					(self.batch_size, self.hidden_size)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    int      							= MobileBertForMaskedLM(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    str      							= model(snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					labels=snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= MobileBertForNextSentencePrediction(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    str      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					labels=snake_case__ ,					)
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, 2)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= MobileBertForPreTraining(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Tuple      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					labels=snake_case__ ,					next_sentence_label=snake_case__ ,					)
		self.parent.assertEqual(result.prediction_logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)    )
		self.parent.assertEqual(result.seq_relationship_logits.shape ,					(self.batch_size, 2)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= MobileBertForQuestionAnswering(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    List[str]      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					start_positions=snake_case__ ,					end_positions=snake_case__ ,					)
		self.parent.assertEqual(result.start_logits.shape ,					(self.batch_size, self.seq_length)    )
		self.parent.assertEqual(result.end_logits.shape ,					(self.batch_size, self.seq_length)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= self.num_labels
		lowerCAmelCase					:    List[Any]      							= MobileBertForSequenceClassification(snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Union[str, Any]      							= model(snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=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__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.num_labels
		lowerCAmelCase					:    int      							= MobileBertForTokenClassification(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Union[str, Any]      							= model(snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					labels=snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[str]      							= self.num_choices
		lowerCAmelCase					:    Any      							= MobileBertForMultipleChoice(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Dict      							= input_ids.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    int      							= token_type_ids.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    Union[str, Any]      							= input_mask.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    List[str]      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					labels=snake_case__ ,					)
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.prepare_config_and_inputs()
		(
		    lowerCAmelCase   
		)					:    Optional[Any]      							= config_and_inputs
		lowerCAmelCase					:    List[Any]      							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
		return config, inputs_dict



@require_torch
class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[str]			      = (
	    (
	        MobileBertModel,
	        MobileBertForMaskedLM,
	        MobileBertForMultipleChoice,
	        MobileBertForNextSentencePrediction,
	        MobileBertForPreTraining,
	        MobileBertForQuestionAnswering,
	        MobileBertForSequenceClassification,
	        MobileBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCamelCase   :					Tuple			      = (
	    {
	        """feature-extraction""": MobileBertModel,
	        """fill-mask""": MobileBertForMaskedLM,
	        """question-answering""": MobileBertForQuestionAnswering,
	        """text-classification""": MobileBertForSequenceClassification,
	        """token-classification""": MobileBertForTokenClassification,
	        """zero-shot""": MobileBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCamelCase   :					str			      = True



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__=False    ):
		lowerCAmelCase					:    int      							= super()._prepare_for_class(snake_case__ ,					snake_case__ ,					return_labels=snake_case__    )

		if return_labels:
			if model_class in get_values(snake_case__    ):
				lowerCAmelCase					:    str      							= torch.zeros(
				    (self.model_tester.batch_size, self.model_tester.seq_length) ,					dtype=torch.long ,					device=snake_case__    )
				lowerCAmelCase					:    Tuple      							= torch.zeros(
				    self.model_tester.batch_size ,					dtype=torch.long ,					device=snake_case__    )
		return inputs_dict



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= MobileBertModelTester(self    )
		lowerCAmelCase					:    Dict      							= ConfigTester(self ,					config_class=snake_case__ ,					hidden_size=37    )



	def 				lowercase     (					self    ):
		self.config_tester.run_common_tests()



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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case__    )



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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case__    )


def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      Optional[int]:




	"""simple docstring"""





	return torch.tensor(
	    _A					,   dtype=torch.long					,   device=_A					,   )


_lowerCAmelCase  :		Union[str, Any]		     =	1E-3






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				unittest.TestCase							):



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= MobileBertModel.from_pretrained('google/mobilebert-uncased'    ).to(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]    )
		with torch.no_grad():
			lowerCAmelCase					:    Tuple      							= model(snake_case__    )[0]
		lowerCAmelCase					:    List[Any]      							= torch.Size((1, 9, 512)    )
		self.assertEqual(output.shape ,					snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= torch.tensor(
		    [
		        [
		            [-2.4_7_3_6_5_2_6e0_7, 8.2_6_9_1_6_5_6e0_4, 1.6_5_2_1_8_3_8e0_5],
		            [-5.7_5_4_1_7_0_4e-0_1, 3.9_0_5_6_0_2_2e0_0, 4.4_0_1_1_5_0_7e0_0],
		            [2.6_0_4_7_3_5_9e0_0, 1.5_6_7_7_6_5_2e0_0, -1.7_3_2_4_1_8_8e-0_1],
		        ]
		    ] ,					device=snake_case__ ,					)

		# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
		# ~1 difference, it's therefore not a good idea to measure using addition.
		# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
		# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
		lowerCAmelCase					:    List[str]      							= torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE    )
		lowerCAmelCase					:    Dict      							= torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE    )

		self.assertTrue(lower_bound and upper_bound    )




'''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)
