




'''simple docstring'''



from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFConvBertForMaskedLM,
         TFConvBertForMultipleChoice,
         TFConvBertForQuestionAnswering,
         TFConvBertForSequenceClassification,
         TFConvBertForTokenClassification,
         TFConvBertModel,
     )





class      lowerCAmelCase :


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=None     ,	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	13
            __snake_case            =	7
            __snake_case            =	True
            __snake_case            =	True
            __snake_case            =	True
            __snake_case            =	True
            __snake_case            =	99
            __snake_case            =	384
            __snake_case            =	2
            __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            =	0.02
            __snake_case            =	3
            __snake_case            =	4
            __snake_case            =	128
            __snake_case            =	2
            __snake_case            =	9
            __snake_case            =	1
            __snake_case            =	None


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_input_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	None
            __snake_case            =	None
            __snake_case            =	None
            if self.use_labels:
                 __snake_case            =	ids_tensor([self.batch_size]     ,	self.type_sequence_label_size	)
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels	)
                 __snake_case            =	ids_tensor([self.batch_size]     ,	self.num_choices	)

            __snake_case            =	ConvBertConfig(
                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     ,	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     ,	initializer_range=self.initializer_range     ,	return_dict=__SCREAMING_SNAKE_CASE     ,	)

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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	TFConvBertModel(config=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

            __snake_case            =	[input_ids, input_mask]
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)

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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	TFConvBertForMaskedLM(config=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	self.num_labels
            __snake_case            =	TFConvBertForSequenceClassification(config=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	self.num_choices
            __snake_case            =	TFConvBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE     ,	1	)     ,	(1, self.num_choices, 1)	)
            __snake_case            =	tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE     ,	1	)     ,	(1, self.num_choices, 1)	)
            __snake_case            =	tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE     ,	1	)     ,	(1, self.num_choices, 1)	)
            __snake_case            =	{
                '''input_ids''': multiple_choice_inputs_ids,
                '''attention_mask''': multiple_choice_input_mask,
                '''token_type_ids''': multiple_choice_token_type_ids,
            }
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.num_labels
            __snake_case            =	TFConvBertForTokenClassification(config=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	TFConvBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            __snake_case            =	model(__SCREAMING_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    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            (
                (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     
            )            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
            return config, inputs_dict





@require_tf
class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   str										=      (
           (
               TFConvBertModel,
               TFConvBertForMaskedLM,
               TFConvBertForQuestionAnswering,
               TFConvBertForSequenceClassification,
               TFConvBertForTokenClassification,
               TFConvBertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       __lowercase	:   Optional[int]										=      (
           {
               '''feature-extraction''': TFConvBertModel,
               '''fill-mask''': TFConvBertForMaskedLM,
               '''question-answering''': TFConvBertForQuestionAnswering,
               '''text-classification''': TFConvBertForSequenceClassification,
               '''token-classification''': TFConvBertForTokenClassification,
               '''zero-shot''': TFConvBertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __lowercase	:   Optional[Any]										=      False
       __lowercase	:   List[str]										=      False
       __lowercase	:   Union[str, Any]										=      False


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	TFConvBertModelTester(self	)
            __snake_case            =	ConfigTester(self     ,	config_class=__SCREAMING_SNAKE_CASE     ,	hidden_size=37	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            self.config_tester.run_common_tests()


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE	)


       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case            =	True
            __snake_case            =	True

            if hasattr(__SCREAMING_SNAKE_CASE     ,	'''use_cache'''	):
                 __snake_case            =	True

            __snake_case            =	getattr(self.model_tester     ,	'''encoder_seq_length'''     ,	self.model_tester.seq_length	)
            __snake_case            =	getattr(self.model_tester     ,	'''key_length'''     ,	__SCREAMING_SNAKE_CASE	)

            for model_class in self.all_model_classes:
                 __snake_case            =	self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	len(model(__SCREAMING_SNAKE_CASE	)	)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(__SCREAMING_SNAKE_CASE     ,	saved_model=__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	os.path.join(__SCREAMING_SNAKE_CASE     ,	'''saved_model'''     ,	'''1'''	)
                      __snake_case            =	tf.keras.models.load_model(__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	model(__SCREAMING_SNAKE_CASE	)

                      if self.is_encoder_decoder:
                           __snake_case            =	outputs['''encoder_hidden_states''']
                           __snake_case            =	outputs['''encoder_attentions''']
                      else:
                           __snake_case            =	outputs['''hidden_states''']
                           __snake_case            =	outputs['''attentions''']

                      self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)

                      __snake_case            =	getattr(
                          self.model_tester     ,	'''expected_num_hidden_layers'''     ,	self.model_tester.num_hidden_layers + 1	)

                      self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)
                      self.assertListEqual(
                          list(output_hidden_states[0].shape[-2:]	)     ,	[self.model_tester.seq_length, self.model_tester.hidden_size]     ,	)

                      self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	self.model_tester.num_hidden_layers	)
                      self.assertListEqual(
                          list(output_attentions[0].shape[-3:]	)     ,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]     ,	)


       @slow
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''	)
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)




       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case            =	True
            __snake_case            =	getattr(self.model_tester     ,	'''decoder_seq_length'''     ,	self.model_tester.seq_length	)
            __snake_case            =	getattr(self.model_tester     ,	'''encoder_seq_length'''     ,	self.model_tester.seq_length	)
            __snake_case            =	getattr(self.model_tester     ,	'''key_length'''     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	getattr(self.model_tester     ,	'''key_length'''     ,	__SCREAMING_SNAKE_CASE	)

            def check_decoder_attentions_output(__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	len(__SCREAMING_SNAKE_CASE	)
                 self.assertEqual(out_len % 2     ,	0	)
                 __snake_case            =	outputs.decoder_attentions
                 self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	self.model_tester.num_hidden_layers	)
                 self.assertListEqual(
                     list(decoder_attentions[0].shape[-3:]	)     ,	[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]     ,	)

            def check_encoder_attentions_output(__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	[
                     t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
                 ]
                 self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	self.model_tester.num_hidden_layers	)
                 self.assertListEqual(
                     list(attentions[0].shape[-3:]	)     ,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]     ,	)

            for model_class in self.all_model_classes:
                 __snake_case            =	True
                 __snake_case            =	False
                 __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	)
                 __snake_case            =	len(__SCREAMING_SNAKE_CASE	)
                 self.assertEqual(config.output_hidden_states     ,	__SCREAMING_SNAKE_CASE	)
                 check_encoder_attentions_output(__SCREAMING_SNAKE_CASE	)

                 if self.is_encoder_decoder:
                      __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	)
                      self.assertEqual(config.output_hidden_states     ,	__SCREAMING_SNAKE_CASE	)
                      check_decoder_attentions_output(__SCREAMING_SNAKE_CASE	)

                 # Check that output attentions can also be changed via the config
                 del inputs_dict["output_attentions"]
                 __snake_case            =	True
                 __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	)
                 self.assertEqual(config.output_hidden_states     ,	__SCREAMING_SNAKE_CASE	)
                 check_encoder_attentions_output(__SCREAMING_SNAKE_CASE	)

                 # Check attention is always last and order is fine
                 __snake_case            =	True
                 __snake_case            =	True
                 __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	)

                 self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)     ,	len(__SCREAMING_SNAKE_CASE	)	)
                 self.assertEqual(model.config.output_hidden_states     ,	__SCREAMING_SNAKE_CASE	)
                 check_encoder_attentions_output(__SCREAMING_SNAKE_CASE	)





@require_tf
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''	)
            __snake_case            =	tf.constant([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	[1, 6, 768]
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tf.constant(
                [
                    [
                        [-0.03_475_493, -0.4_686_034, -0.30_638_832],
                        [0.22_637_248, -0.26_988_646, -0.7_423_424],
                        [0.10_324_868, -0.45_013_508, -0.58_280_784],
                    ]
                ]	)
            tf.debugging.assert_near(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)













import argparse
import os
import re


_lowercase		:  List[str]			       ="""src/diffusers"""

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      _re_indent.search(lowerCAmelCase__						)
		return "" if search is None else search.groups()[0]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=""							,lowerCAmelCase__=None							,lowerCAmelCase__=None						):
		lowerCamelCase_	:     Optional[Any] 						=      0
		lowerCamelCase_	:     Union[str, Any] 						=      code.split('\n'						)
		if start_prompt is not None:
				while not lines[index].startswith(lowerCAmelCase__						):
						index += 1
				lowerCamelCase_	:     Any 						=      ['\n'.join(lines[:index]						)]
		else:
				lowerCamelCase_	:     str 						=      []

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

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

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

		return blocks


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

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

		return _inner


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=None						):

		# If no key is provided, we use a noop.
		def noop(lowerCAmelCase__						):
				return x

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

		lowerCamelCase_	:     Optional[Any] 						=      ignore_underscore(lowerCAmelCase__						)
		return sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		# This inner function sort imports between [ ].
		def _replace(lowerCAmelCase__						):
				lowerCamelCase_	:     Dict 						=      match.groups()[0]
				if "," not in imports:
						return F"[{imports}]"
				lowerCamelCase_	:     Optional[int] 						=      [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_	:     str 						=      keys[:-1]
				return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase__						)]						) + "]"

		lowerCamelCase_	:     Tuple 						=      import_statement.split('\n'						)
		if len(lowerCAmelCase__						) > 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_	:     int 						=      2 if lines[1].strip() == '[' else 1
				lowerCamelCase_	:     Any 						=      [(i, _re_strip_line.search(lowerCAmelCase__						).groups()[0]) for i, line in enumerate(lines[idx:-idx]						)]
				lowerCamelCase_	:     str 						=      sort_objects(lowerCAmelCase__							,key=lambda lowerCAmelCase__						: x[1]						)
				lowerCamelCase_	:     Any 						=      [lines[x[0] + idx] for x in sorted_indices]
				return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]						)
		elif len(lowerCAmelCase__						) == 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_	:     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_	:     List[Any] 						=      keys[:-1]
						lowerCamelCase_	:     Optional[Any] 						=      get_indent(lines[1]						) + ', '.join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase__						)]						)
				return "\n".join(lowerCAmelCase__						)
		else:
				# Finally we have to deal with imports fitting on one line
				lowerCamelCase_	:     Any 						=      _re_bracket_content.sub(_replace							,lowerCAmelCase__						)
				return import_statement


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=True						):
		with open(lowerCAmelCase__							,'r'						) as f:
				lowerCamelCase_	:     Any 						=      f.read()

		if "_import_structure" not in code:
				return

		# Blocks of indent level 0
		lowerCamelCase_	:     int 						=      split_code_in_indented_blocks(
		    lowerCAmelCase__							,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(lowerCAmelCase__						) - 1						):
				# Check if the block contains some `_import_structure`s thingy to sort.
				lowerCamelCase_	:     Any 						=      main_blocks[block_idx]
				lowerCamelCase_	:     Tuple 						=      block.split('\n'						)

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

				# Ignore beginning and last line: they don't contain anything.
				lowerCamelCase_	:     Tuple 						=      '\n'.join(block_lines[line_idx:-1]						)
				lowerCamelCase_	:     Dict 						=      get_indent(block_lines[1]						)
				# Slit the internal block into blocks of indent level 1.
				lowerCamelCase_	:     Dict 						=      split_code_in_indented_blocks(lowerCAmelCase__							,indent_level=lowerCAmelCase__						)
				# We have two categories of import key: list or _import_structure[key].append/extend
				lowerCamelCase_	:     List[str] 						=      _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_	:     Tuple 						=      [(pattern.search(lowerCAmelCase__						).groups()[0] if pattern.search(lowerCAmelCase__						) is not None else None) for b in internal_blocks]
				# We only sort the lines with a key.
				lowerCamelCase_	:     Any 						=      [(i, key) for i, key in enumerate(lowerCAmelCase__						) if key is not None]
				lowerCamelCase_	:     Optional[Any] 						=      [x[0] for x in sorted(lowerCAmelCase__							,key=lambda lowerCAmelCase__						: 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(lowerCAmelCase__						)						):
						if keys[i] is None:
								reordered_blocks.append(internal_blocks[i]						)
						else:
								lowerCamelCase_	:     Tuple 						=      sort_objects_in_import(internal_blocks[sorted_indices[count]]						)
								reordered_blocks.append(lowerCAmelCase__						)
								count += 1

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

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__=True						):
		lowerCamelCase_	:     Dict 						=      []
		for root, _, files in os.walk(lowerCAmelCase__						):
				if "__init__.py" in files:
						lowerCamelCase_	:     Optional[int] 						=      sort_imports(os.path.join(lowerCAmelCase__							,'__init__.py'						)							,check_only=lowerCAmelCase__						)
						if result:
								lowerCamelCase_	:     Dict 						=      [os.path.join(lowerCAmelCase__							,'__init__.py'						)]
		if len(lowerCAmelCase__						) > 0:
				raise ValueError(F"Would overwrite {len(lowerCAmelCase__						)} files, run `make style`."						)


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

		sort_imports_in_all_inits(check_only=args.check_only)
