





'''simple docstring'''




import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

  from transformers import MaskFormerSwinBackbone
  from transformers.models.maskformer import MaskFormerSwinModel


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=32		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=16		,      _UpperCAmelCase=[1, 2, 1]		,      _UpperCAmelCase=[2, 2, 4]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=2.0		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-5		,      _UpperCAmelCase=True		,      _UpperCAmelCase=None		,      _UpperCAmelCase=True		,      _UpperCAmelCase=10		,      _UpperCAmelCase=8		,      _UpperCAmelCase=["stage1", "stage2", "stage3"]		,      _UpperCAmelCase=[1, 2, 3]		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			parent
          __A		:					Dict						=			batch_size
          __A		:					Any						=			image_size
          __A		:					List[str]						=			patch_size
          __A		:					List[str]						=			num_channels
          __A		:					Any						=			embed_dim
          __A		:					Dict						=			depths
          __A		:					List[Any]						=			num_heads
          __A		:					str						=			window_size
          __A		:					Union[str, Any]						=			mlp_ratio
          __A		:					str						=			qkv_bias
          __A		:					Dict						=			hidden_dropout_prob
          __A		:					Tuple						=			attention_probs_dropout_prob
          __A		:					int						=			drop_path_rate
          __A		:					str						=			hidden_act
          __A		:					str						=			use_absolute_embeddings
          __A		:					str						=			patch_norm
          __A		:					Dict						=			layer_norm_eps
          __A		:					List[str]						=			initializer_range
          __A		:					str						=			is_training
          __A		:					Union[str, Any]						=			scope
          __A		:					int						=			use_labels
          __A		:					Any						=			type_sequence_label_size
          __A		:					List[str]						=			encoder_stride
          __A		:					str						=			out_features
          __A		:					int						=			out_indices


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          __A		:					Optional[int]						=			None
          if self.use_labels:
               __A		:					Optional[int]						=			ids_tensor([self.batch_size]		,      self.type_sequence_label_size)

          __A		:					Union[str, Any]						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return MaskFormerSwinConfig(
              image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      embed_dim=self.embed_dim		,      depths=self.depths		,      num_heads=self.num_heads		,      window_size=self.window_size		,      mlp_ratio=self.mlp_ratio		,      qkv_bias=self.qkv_bias		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      drop_path_rate=self.drop_path_rate		,      hidden_act=self.hidden_act		,      use_absolute_embeddings=self.use_absolute_embeddings		,      path_norm=self.patch_norm		,      layer_norm_eps=self.layer_norm_eps		,      initializer_range=self.initializer_range		,      encoder_stride=self.encoder_stride		,      out_features=self.out_features		,      out_indices=self.out_indices		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			MaskFormerSwinModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Any						=			model(_UpperCAmelCase)

          __A		:					Optional[int]						=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
          __A		:					List[str]						=			int(config.embed_dim * 2 ** (len(config.depths) - 1))

          self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, expected_seq_len, expected_dim))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			MaskFormerSwinBackbone(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					List[Any]						=			model(_UpperCAmelCase)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)		,      len(config.out_features))
          self.parent.assertListEqual(list(result.feature_maps[0].shape)		,      [13, 16, 16, 16])

          # verify channels
          self.parent.assertEqual(len(model.channels)		,      len(config.out_features))
          self.parent.assertListEqual(model.channels		,      [16, 32, 64])

          # verify ValueError
          with self.parent.assertRaises(_UpperCAmelCase):
               __A		:					Optional[int]						=			['stem']
               __A		:					Dict						=			MaskFormerSwinBackbone(config=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.prepare_config_and_inputs()
          __A					,__A					,__A		:					int						=			config_and_inputs
          __A		:					Tuple						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     lowerCAmelCase						     =			{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			MaskFormerSwinModelTester(self)
          __A		:					Union[str, Any]						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      embed_dim=37)


     @require_torch_multi_gpu
     @unittest.skip(
         reason=(
             '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
             ' `nn.DataParallel`'
         ))
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*_UpperCAmelCase)


     @unittest.skip('Swin does not use inputs_embeds')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     @unittest.skip('Swin does not support feedforward chunking')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					str						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					str						=			model_class(_UpperCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module))
               __A		:					Any						=			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase		,      nn.Linear))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

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

               __A		:					Tuple						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			model_class(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          with torch.no_grad():
               __A		:					Optional[Any]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

          __A		:					Optional[int]						=			outputs.hidden_states

          __A		:					Union[str, Any]						=			getattr(
              self.model_tester		,      'expected_num_hidden_layers'		,      len(self.model_tester.depths) + 1)
          self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

          # Swin has a different seq_length
          __A		:					int						=			(
              config.patch_size
              if isinstance(config.patch_size		,      collections.abc.Iterable)
              else (config.patch_size, config.patch_size)
          )

          __A		:					Dict						=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:])		,      [num_patches, self.model_tester.embed_dim]		,      )


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Any						=			self.model_tester.prepare_config_and_inputs_for_common()

          __A		:					Tuple						=			(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size		,      collections.abc.Iterable)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
               __A		:					List[Any]						=			True
               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __A		:					List[str]						=			True

               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Any						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Optional[Any]						=			3

          __A		:					List[str]						=			(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size		,      collections.abc.Iterable)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          __A		:					Any						=			(
              config.patch_size
              if isinstance(config.patch_size		,      collections.abc.Iterable)
              else (config.patch_size, config.patch_size)
          )

          __A		:					Optional[Any]						=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          __A		:					Union[str, Any]						=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
               __A		:					Optional[int]						=			True
               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      (padded_height, padded_width))

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __A		:					Optional[int]						=			True
               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      (padded_height, padded_width))


     @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

          def set_nan_tensor_to_zero(_UpperCAmelCase):
               __A		:					Any						=			0
               return t

          def check_equivalence(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase={}):
               with torch.no_grad():
                    __A		:					Dict						=			model(**_UpperCAmelCase		,      return_dict=_UpperCAmelCase		,      **_UpperCAmelCase)
                    __A		:					Union[str, Any]						=			model(**_UpperCAmelCase		,      return_dict=_UpperCAmelCase		,      **_UpperCAmelCase).to_tuple()

                    def recursive_check(_UpperCAmelCase		,      _UpperCAmelCase):
                         if isinstance(_UpperCAmelCase		,      (List, Tuple)):
                              for tuple_iterable_value, dict_iterable_value in zip(_UpperCAmelCase		,      _UpperCAmelCase):
                                   recursive_check(_UpperCAmelCase		,      _UpperCAmelCase)
                         elif isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                              for tuple_iterable_value, dict_iterable_value in zip(
                                  tuple_object.values()		,      dict_object.values()):
                                   recursive_check(_UpperCAmelCase		,      _UpperCAmelCase)
                         elif tuple_object is None:
                              return
                         else:
                              self.assertTrue(
                                  torch.allclose(
                                      set_nan_tensor_to_zero(_UpperCAmelCase)		,      set_nan_tensor_to_zero(_UpperCAmelCase)		,      atol=1e-5)		,      msg=(
                                      'Tuple and dict output are not equal. Difference:'
                                      F' {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:'
                                      F' {torch.isnan(_UpperCAmelCase).any()} and `inf`: {torch.isinf(_UpperCAmelCase)}. Dict has'
                                      F' `nan`: {torch.isnan(_UpperCAmelCase).any()} and `inf`: {torch.isinf(_UpperCAmelCase)}.'
                                  )		,      )

                    recursive_check(_UpperCAmelCase		,      _UpperCAmelCase)

          for model_class in self.all_model_classes:
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               __A		:					Tuple						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase)
               __A		:					List[str]						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase)
               check_equivalence(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

               __A		:					int						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)
               __A		:					Dict						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)
               check_equivalence(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

               __A		:					Any						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase)
               __A		:					Any						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase)
               check_equivalence(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      {'output_hidden_states': True})

               __A		:					List[str]						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)
               __A		:					str						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)
               check_equivalence(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      {'output_hidden_states': True})




@require_torch
class 					SCREAMING_SNAKE_CASE       (unittest.TestCase   ,						a__				):
     lowerCAmelCase						     =			(MaskFormerSwinBackbone,) if is_torch_available() else ()
     lowerCAmelCase						     =			MaskFormerSwinConfig


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			MaskFormerSwinModelTester(self)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					str						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Tuple						=			inputs_dict['pixel_values'].shape[0]

          for backbone_class in self.all_model_classes:
               __A		:					Optional[int]						=			backbone_class(_UpperCAmelCase)
               backbone.to(_UpperCAmelCase)
               backbone.eval()

               __A		:					Tuple						=			backbone(**_UpperCAmelCase)

               # Test default outputs and verify feature maps
               self.assertIsInstance(outputs.feature_maps		,      _UpperCAmelCase)
               self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
               for feature_map, n_channels in zip(outputs.feature_maps		,      backbone.channels):
                    self.assertTrue(feature_map.shape[:2]		,      (batch_size, n_channels))
               self.assertIsNone(outputs.hidden_states)
               self.assertIsNone(outputs.attentions)

               # Test output_hidden_states=True
               __A		:					Dict						=			backbone(**_UpperCAmelCase		,      output_hidden_states=_UpperCAmelCase)
               self.assertIsNotNone(outputs.hidden_states)
               self.assertTrue(len(outputs.hidden_states)		,      len(backbone.stage_names))
               # We skip the stem layer
               for hidden_states, n_channels in zip(outputs.hidden_states[1:]		,      backbone.channels):
                    for hidden_state in hidden_states:
                         # Hidden states are in the format (batch_size, (height * width), n_channels)
                         __A					,__A					,__A		:					List[Any]						=			hidden_state.shape
                         self.assertTrue((h_batch_size, h_n_channels)		,      (batch_size, n_channels))

            # Test output_attentions=True
               if self.has_attentions:
                    __A		:					Union[str, Any]						=			backbone(**_UpperCAmelCase		,      output_attentions=_UpperCAmelCase)
                    self.assertIsNotNone(outputs.attentions)






'''simple docstring'''




import argparse
import os
import re


lowercase__       : Optional[int]         =       '''src/diffusers'''

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






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Tuple:
     __A		:					List[Any]						=			_re_indent.search(__snake_case     )
     return "" if search is None else search.groups()[0]






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]					,				__snake_case    :					str=""					,				__snake_case    :					Any=None					,				__snake_case    :					List[Any]=None     )	->	Optional[int]:
     __A		:					Tuple						=			0
     __A		:					Optional[int]						=			code.split('\n'     )
     if start_prompt is not None:
          while not lines[index].startswith(__snake_case     ):
               index += 1
          __A		:					Optional[int]						=			['\n'.join(lines[:index]     )]
     else:
          __A		:					Any						=			[]

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

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

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

     return blocks






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	int:

     def _inner(__snake_case    :					List[Any]     ):
          return key(__snake_case     ).lower().replace('_'					,				''     )

     return _inner






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any=None     )	->	List[Any]:

     # If no key is provided, we use a noop.
     def noop(__snake_case    :					List[Any]     ):
          return x

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

     __A		:					Tuple						=			ignore_underscore(__snake_case     )
     return sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]     )	->	Tuple:

     # This inner function sort imports between [ ].
     def _replace(__snake_case    :					Tuple     ):
          __A		:					List[str]						=			match.groups()[0]
          if "," not in imports:
               return f'[{imports}]'
          __A		:					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:
               __A		:					Dict						=			keys[:-1]
          return "[" + ", ".join([f'"{k}"' for k in sort_objects(__snake_case     )]     ) + "]"

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






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					List[Any]=True     )	->	Optional[Any]:
     with open(__snake_case					,				'r'     ) as f:
          __A		:					Dict						=			f.read()

     if "_import_structure" not in code:
          return

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

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

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

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

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

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






def 			_lowerCAmelCase     (    __snake_case    :					int=True     )	->	Optional[Any]:
     __A		:					Tuple						=			[]
     for root, _, files in os.walk(__snake_case     ):
          if "__init__.py" in files:
               __A		:					List[Any]						=			sort_imports(os.path.join(__snake_case					,				'__init__.py'     )					,				check_only=__snake_case     )
               if result:
                    __A		:					Dict						=			[os.path.join(__snake_case					,				'__init__.py'     )]
     if len(__snake_case     ) > 0:
          raise ValueError(f'Would overwrite {len(__snake_case     )} files, run `make style`.'     )


if __name__ == "__main__":
  lowercase__       : Optional[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)