



import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
						import torch

						from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor






class  SCREAMING_SNAKE_CASE      (				_UpperCamelCase       ):





							def     UpperCamelCase_       ( self		:		List[str]						):

											'''simple docstring'''
											__a	  =		self.config_class(**self.inputs_dict						)
											self.parent.assertTrue(hasattr(__lowercase   ,					"""tf_padding"""						)						)
											self.parent.assertTrue(hasattr(__lowercase   ,					"""depth_multiplier"""						)						)






class  SCREAMING_SNAKE_CASE      :





							def __init__( self		:		Optional[int]   ,					__lowercase		:		int   ,					__lowercase		:		Any=13   ,					__lowercase		:		Dict=3   ,					__lowercase		:		List[str]=32   ,					__lowercase		:		Tuple=0.25   ,					__lowercase		:		Any=8   ,					__lowercase		:		Union[str, Any]=8   ,					__lowercase		:		Optional[int]=6   ,					__lowercase		:		Optional[Any]=32   ,					__lowercase		:		List[str]=True   ,					__lowercase		:		Tuple=True   ,					__lowercase		:		Dict=True   ,					__lowercase		:		str="relu6"   ,					__lowercase		:		Union[str, Any]=1280   ,					__lowercase		:		Any=0.1   ,					__lowercase		:		Dict=0.02   ,					__lowercase		:		Any=True   ,					__lowercase		:		List[str]=True   ,					__lowercase		:		Union[str, Any]=10   ,					__lowercase		:		List[str]=None   ,					):

											'''simple docstring'''
											__a	  =		parent
											__a	  =		batch_size
											__a	  =		num_channels
											__a	  =		image_size
											__a	  =		depth_multiplier
											__a	  =		depth_divisible_by
											__a	  =		min_depth
											__a	  =		expand_ratio
											__a	  =		tf_padding
											__a	  =		output_stride
											__a	  =		first_layer_is_expansion
											__a	  =		finegrained_output
											__a	  =		hidden_act
											__a	  =		last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier						)
											__a	  =		classifier_dropout_prob
											__a	  =		use_labels
											__a	  =		is_training
											__a	  =		num_labels
											__a	  =		initializer_range
											__a	  =		scope





							def     UpperCamelCase_       ( self		:		int						):

											'''simple docstring'''
											__a	  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											__a	  =		None
											__a	  =		None
											if self.use_labels:
															__a	  =		ids_tensor([self.batch_size]   ,					self.num_labels						)
															__a	  =		ids_tensor([self.batch_size, self.image_size, self.image_size]   ,					self.num_labels						)

											__a	  =		self.get_config()

											return config, pixel_values, labels, pixel_labels





							def     UpperCamelCase_       ( self		:		int						):

											'''simple docstring'''
											return MobileNetVaConfig(
											    num_channels=self.num_channels   ,					image_size=self.image_size   ,					depth_multiplier=self.depth_multiplier   ,					depth_divisible_by=self.depth_divisible_by   ,					min_depth=self.min_depth   ,					expand_ratio=self.expand_ratio   ,					output_stride=self.output_stride   ,					first_layer_is_expansion=self.first_layer_is_expansion   ,					finegrained_output=self.finegrained_output   ,					hidden_act=self.hidden_act   ,					tf_padding=self.tf_padding   ,					classifier_dropout_prob=self.classifier_dropout_prob   ,					initializer_range=self.initializer_range   ,					)





							def     UpperCamelCase_       ( self		:		Optional[int]   ,					__lowercase		:		List[str]   ,					__lowercase		:		str   ,					__lowercase		:		str   ,					__lowercase		:		List[Any]						):

											'''simple docstring'''
											__a	  =		MobileNetVaModel(config=__lowercase						)
											model.to(__lowercase						)
											model.eval()
											__a	  =		model(__lowercase						)
											self.parent.assertEqual(
											    result.last_hidden_state.shape   ,					(
											        self.batch_size,
											        self.last_hidden_size,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )   ,					)
											self.parent.assertEqual(
											    result.pooler_output.shape   ,					(self.batch_size, self.last_hidden_size)   ,					)





							def     UpperCamelCase_       ( self		:		Optional[Any]   ,					__lowercase		:		Optional[Any]   ,					__lowercase		:		Any   ,					__lowercase		:		Any   ,					__lowercase		:		Any						):

											'''simple docstring'''
											__a	  =		self.num_labels
											__a	  =		MobileNetVaForImageClassification(__lowercase						)
											model.to(__lowercase						)
											model.eval()
											__a	  =		model(__lowercase   ,					labels=__lowercase						)
											self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)						)





							def     UpperCamelCase_       ( self		:		Tuple   ,					__lowercase		:		Any   ,					__lowercase		:		Optional[int]   ,					__lowercase		:		Dict   ,					__lowercase		:		List[Any]						):

											'''simple docstring'''
											__a	  =		self.num_labels
											__a	  =		MobileNetVaForSemanticSegmentation(__lowercase						)
											model.to(__lowercase						)
											model.eval()
											__a	  =		model(__lowercase						)
											self.parent.assertEqual(
											    result.logits.shape   ,					(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )   ,					)
											__a	  =		model(__lowercase   ,					labels=__lowercase						)
											self.parent.assertEqual(
											    result.logits.shape   ,					(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )   ,					)







							def     UpperCamelCase_       ( self		:		Union[str, Any]						):

											'''simple docstring'''
											__a	  =		self.prepare_config_and_inputs()
											__a	  =		config_and_inputs
											__a	  =		{"pixel_values": pixel_values}
											return config, inputs_dict






@require_torch
class  SCREAMING_SNAKE_CASE      (				_UpperCamelCase      ,   _UpperCamelCase      ,   unittest.TestCase       ):
							__lowerCamelCase						:						Tuple     =(
							    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							__lowerCamelCase						:						int     =(
							    {
							        'feature-extraction': MobileNetVaModel,
							        'image-classification': MobileNetVaForImageClassification,
							        'image-segmentation': MobileNetVaForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							__lowerCamelCase						:						List[Any]     =False
							__lowerCamelCase						:						Any     =False
							__lowerCamelCase						:						List[str]     =False
							__lowerCamelCase						:						Tuple     =False





							def     UpperCamelCase_       ( self		:		List[str]						):

											'''simple docstring'''
											__a	  =		MobileNetVaModelTester(self						)
											__a	  =		MobileNetVaConfigTester(self   ,					config_class=__lowercase   ,					has_text_modality=__lowercase						)





							def     UpperCamelCase_       ( self		:		List[str]						):

											'''simple docstring'''
											self.config_tester.run_common_tests()





							@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""						)
							def     UpperCamelCase_       ( self		:		List[Any]						):

											'''simple docstring'''
											pass





							@unittest.skip(reason="""MobileNetV2 does not support input and output embeddings"""						)
							def     UpperCamelCase_       ( self		:		Optional[Any]						):

											'''simple docstring'''
											pass





							@unittest.skip(reason="""MobileNetV2 does not output attentions"""						)
							def     UpperCamelCase_       ( self		:		List[Any]						):

											'''simple docstring'''
											pass





							def     UpperCamelCase_       ( self		:		Optional[Any]						):

											'''simple docstring'''
											__a	  =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__a	  =		model_class(__lowercase						)
															__a	  =		inspect.signature(model.forward						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__a	  =		[*signature.parameters.keys()]

															__a	  =		["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,					__lowercase						)





							def     UpperCamelCase_       ( self		:		int						):

											'''simple docstring'''
											__a	  =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase						)





							def     UpperCamelCase_       ( self		:		Tuple						):

											'''simple docstring'''
											def check_hidden_states_output(__lowercase		:		int   ,					__lowercase		:		Optional[Any]   ,					__lowercase		:		Optional[int]						):
															__a	  =		model_class(__lowercase						)
															model.to(__lowercase						)
															model.eval()

															with torch.no_grad():
																			__a	  =		model(**self._prepare_for_class(__lowercase   ,					__lowercase						)						)

															__a	  =		outputs.hidden_states

															__a	  =		16
															self.assertEqual(len(__lowercase						)   ,					__lowercase						)

											__a	  =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__a	  =		True
															check_hidden_states_output(__lowercase   ,					__lowercase   ,					__lowercase						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__a	  =		True

															check_hidden_states_output(__lowercase   ,					__lowercase   ,					__lowercase						)





							def     UpperCamelCase_       ( self		:		Any						):

											'''simple docstring'''
											__a	  =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__lowercase						)





							def     UpperCamelCase_       ( self		:		List[Any]						):

											'''simple docstring'''
											__a	  =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase						)







							@slow
							def     UpperCamelCase_       ( self		:		int						):

											'''simple docstring'''
											for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a	  =		MobileNetVaModel.from_pretrained(__lowercase						)
															self.assertIsNotNone(__lowercase						)



def 	lowerCAmelCase__    (				):
				"""simple docstring"""







				__a	  =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
				return image






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





							@cached_property
							def     UpperCamelCase_       ( self		:		Dict						):

											'''simple docstring'''
											return (
											    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224"""						) if is_vision_available() else None
											)





							@slow
							def     UpperCamelCase_       ( self		:		Optional[int]						):

											'''simple docstring'''
											__a	  =		MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224"""						).to(__lowercase						)

											__a	  =		self.default_image_processor
											__a	  =		prepare_img()
											__a	  =		image_processor(images=__lowercase   ,					return_tensors="""pt"""						).to(__lowercase						)

											# forward pass
											with torch.no_grad():
															__a	  =		model(**__lowercase						)

											# verify the logits
											__a	  =		torch.Size((1, 1001)						)
											self.assertEqual(outputs.logits.shape   ,					__lowercase						)

											__a	  =		torch.tensor([0.2445, -1.1993, 0.1905]						).to(__lowercase						)

											self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,					__lowercase   ,					atol=1E-4						)						)







							@slow
							def     UpperCamelCase_       ( self		:		Any						):

											'''simple docstring'''
											__a	  =		MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""						)
											__a	  =		model.to(__lowercase						)

											__a	  =		MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""						)

											__a	  =		prepare_img()
											__a	  =		image_processor(images=__lowercase   ,					return_tensors="""pt"""						).to(__lowercase						)

											# forward pass
											with torch.no_grad():
															__a	  =		model(**__lowercase						)
											__a	  =		outputs.logits

											# verify the logits
											__a	  =		torch.Size((1, 21, 65, 65)						)
											self.assertEqual(logits.shape   ,					__lowercase						)

											__a	  =		torch.tensor(
											    [
											        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
											        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
											        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
											    ]   ,					device=__lowercase   ,					)

											self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,					__lowercase   ,					atol=1E-4						)						)





"""simple docstring"""




import collections
import importlib.util
import os
import re
from pathlib import Path


_lowerCAmelCase							:   Dict				=				"src/transformers"


# Matches is_xxx_available()
_lowerCAmelCase							:   List[Any]				=				re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
_lowerCAmelCase							:   Tuple				=				re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_lowerCAmelCase							:   Optional[int]				=				re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
_lowerCAmelCase							:   Tuple				=				re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
_lowerCAmelCase							:   List[str]				=				re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_lowerCAmelCase							:   Tuple				=				re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
_lowerCAmelCase							:   Dict				=				re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
_lowerCAmelCase							:   Any				=				re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
_lowerCAmelCase							:   Optional[int]				=				re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
_lowerCAmelCase							:   List[str]				=				re.compile(r"^\s*try:")
# Catches a line with else:
_lowerCAmelCase							:   Optional[int]				=				re.compile(r"^\s*else:")

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]							)    ->  int:





     '''simple docstring'''

     if _re_test_backend.search(SCREAMING_SNAKE_CASE__							) is None:
          return None
     _UpperCAmelCase     :      str														=       [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE__							)]
     backends.sort()
     return "_and_".join(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  Dict:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    "r"		,    encoding="utf-8"		,    newline="\n"							) as f:
          _UpperCAmelCase     :      int														=       f.readlines()

     _UpperCAmelCase     :      Optional[Any]														=       0
     while line_index < len(SCREAMING_SNAKE_CASE__							) and not lines[line_index].startswith("_import_structure = {"							):
          line_index += 1

     # If this is a traditional init, just return.
     if line_index >= len(SCREAMING_SNAKE_CASE__							):
          return None

     # First grab the objects without a specific backend in _import_structure
     _UpperCAmelCase     :      List[Any]														=       []
     while not lines[line_index].startswith("if TYPE_CHECKING"							) and find_backend(lines[line_index]							) is None:
          _UpperCAmelCase     :      List[Any]														=       lines[line_index]
          # If we have everything on a single line, let's deal with it.
          if _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE__							):
               _UpperCAmelCase     :      Tuple														=       _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE__							).groups()[0]
               _UpperCAmelCase     :      int														=       re.findall("\[([^\]]+)\]"		,    SCREAMING_SNAKE_CASE__							)
               for imp in imports:
                    objects.extend([obj[1:-1] for obj in imp.split(", "							)]							)
               line_index += 1
               continue
          _UpperCAmelCase     :      Optional[int]														=       _re_import_struct_key_value.search(SCREAMING_SNAKE_CASE__							)
          if single_line_import_search is not None:
               _UpperCAmelCase     :      Dict														=       [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "							) if len(SCREAMING_SNAKE_CASE__							) > 0]
               objects.extend(SCREAMING_SNAKE_CASE__							)
          elif line.startswith(" " * 8 + "\""							):
               objects.append(line[9:-3]							)
          line_index += 1

     _UpperCAmelCase     :      Optional[int]														=       {"none": objects}
     # Let's continue with backend-specific objects in _import_structure
     while not lines[line_index].startswith("if TYPE_CHECKING"							):
          # If the line is an if not is_backend_available, we grab all objects associated.
          _UpperCAmelCase     :      Optional[Any]														=       find_backend(lines[line_index]							)
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]							) is None:
               _UpperCAmelCase     :      Dict														=       None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]							) is None:
                    line_index += 1

               line_index += 1

               _UpperCAmelCase     :      Union[str, Any]														=       []
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 4							):
                    _UpperCAmelCase     :      Union[str, Any]														=       lines[line_index]
                    if _re_import_struct_add_one.search(SCREAMING_SNAKE_CASE__							) is not None:
                         objects.append(_re_import_struct_add_one.search(SCREAMING_SNAKE_CASE__							).groups()[0]							)
                    elif _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE__							) is not None:
                         _UpperCAmelCase     :      int														=       _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE__							).groups()[0].split(", "							)
                         _UpperCAmelCase     :      Dict														=       [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE__							) > 0]
                         objects.extend(SCREAMING_SNAKE_CASE__							)
                    elif _re_between_brackets.search(SCREAMING_SNAKE_CASE__							) is not None:
                         _UpperCAmelCase     :      str														=       _re_between_brackets.search(SCREAMING_SNAKE_CASE__							).groups()[0].split(", "							)
                         _UpperCAmelCase     :      List[Any]														=       [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE__							) > 0]
                         objects.extend(SCREAMING_SNAKE_CASE__							)
                    elif _re_quote_object.search(SCREAMING_SNAKE_CASE__							) is not None:
                         objects.append(_re_quote_object.search(SCREAMING_SNAKE_CASE__							).groups()[0]							)
                    elif line.startswith(" " * 8 + "\""							):
                         objects.append(line[9:-3]							)
                    elif line.startswith(" " * 12 + "\""							):
                         objects.append(line[13:-3]							)
                    line_index += 1

               _UpperCAmelCase     :      Optional[Any]														=       objects
          else:
               line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
     _UpperCAmelCase     :      int														=       []
     while (
         line_index < len(SCREAMING_SNAKE_CASE__							)
         and find_backend(lines[line_index]							) is None
         and not lines[line_index].startswith("else"							)
     ):
          _UpperCAmelCase     :      List[Any]														=       lines[line_index]
          _UpperCAmelCase     :      Optional[Any]														=       _re_import.search(SCREAMING_SNAKE_CASE__							)
          if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(", "							)							)
          elif line.startswith(" " * 8							):
               objects.append(line[8:-2]							)
          line_index += 1

     _UpperCAmelCase     :      Union[str, Any]														=       {"none": objects}
     # Let's continue with backend-specific objects
     while line_index < len(SCREAMING_SNAKE_CASE__							):
          # If the line is an if is_backend_available, we grab all objects associated.
          _UpperCAmelCase     :      Any														=       find_backend(lines[line_index]							)
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]							) is None:
               _UpperCAmelCase     :      List[str]														=       None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]							) is None:
                    line_index += 1

               line_index += 1

               _UpperCAmelCase     :      List[str]														=       []
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 8							):
                    _UpperCAmelCase     :      Any														=       lines[line_index]
                    _UpperCAmelCase     :      Optional[int]														=       _re_import.search(SCREAMING_SNAKE_CASE__							)
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(", "							)							)
                    elif line.startswith(" " * 12							):
                         objects.append(line[12:-2]							)
                    line_index += 1

               _UpperCAmelCase     :      Union[str, Any]														=       objects
          else:
               line_index += 1

     return import_dict_objects, type_hint_objects

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[int]:





     '''simple docstring'''

     def find_duplicates(SCREAMING_SNAKE_CASE__      :		int							):
          return [k for k, v in collections.Counter(SCREAMING_SNAKE_CASE__							).items() if v > 1]

     if list(import_dict_objects.keys()							) != list(type_hint_objects.keys()							):
          return ["Both sides of the init do not have the same backends!"]

     _UpperCAmelCase     :      Any														=       []
     for key in import_dict_objects.keys():
          _UpperCAmelCase     :      Dict														=       find_duplicates(import_dict_objects[key]							)
          if duplicate_imports:
               errors.append(f'Duplicate _import_structure definitions for: {duplicate_imports}'							)
          _UpperCAmelCase     :      Tuple														=       find_duplicates(type_hint_objects[key]							)
          if duplicate_type_hints:
               errors.append(f'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'							)

          if sorted(set(import_dict_objects[key]							)							) != sorted(set(type_hint_objects[key]							)							):
               _UpperCAmelCase     :      List[Any]														=       "base imports" if key == "none" else f'{key} backend'
               errors.append(f'Differences for {name}:'							)
               for a in type_hint_objects[key]:
                    if a not in import_dict_objects[key]:
                         errors.append(f'  {a} in TYPE_HINT but not in _import_structure.'							)
               for a in import_dict_objects[key]:
                    if a not in type_hint_objects[key]:
                         errors.append(f'  {a} in _import_structure but not in TYPE_HINT.'							)
     return errors

def 		__snake_case		( )    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       []
     for root, _, files in os.walk(SCREAMING_SNAKE_CASE__							):
          if "__init__.py" in files:
               _UpperCAmelCase     :      Union[str, Any]														=       os.path.join(SCREAMING_SNAKE_CASE__		,    "__init__.py"							)
               _UpperCAmelCase     :      Union[str, Any]														=       parse_init(SCREAMING_SNAKE_CASE__							)
               if objects is not None:
                    _UpperCAmelCase     :      int														=       analyze_results(*SCREAMING_SNAKE_CASE__							)
                    if len(SCREAMING_SNAKE_CASE__							) > 0:
                         _UpperCAmelCase     :      Dict														=       f'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
                         failures.append("\n".join(SCREAMING_SNAKE_CASE__							)							)
     if len(SCREAMING_SNAKE_CASE__							) > 0:
          raise ValueError("\n\n".join(SCREAMING_SNAKE_CASE__							)							)

def 		__snake_case		( )    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       []
     for path, directories, files in os.walk(SCREAMING_SNAKE_CASE__							):
          for folder in directories:
               # Ignore private modules
               if folder.startswith("_"							):
                    directories.remove(SCREAMING_SNAKE_CASE__							)
                    continue
               # Ignore leftovers from branches (empty folders apart from pycache)
               if len(list((Path(SCREAMING_SNAKE_CASE__							) / folder).glob("*.py"							)							)							) == 0:
                    continue
               _UpperCAmelCase     :      List[Any]														=       str((Path(SCREAMING_SNAKE_CASE__							) / folder).relative_to(SCREAMING_SNAKE_CASE__							)							)
               _UpperCAmelCase     :      Optional[int]														=       short_path.replace(os.path.sep		,    "."							)
               submodules.append(SCREAMING_SNAKE_CASE__							)
          for fname in files:
               if fname == "__init__.py":
                    continue
               _UpperCAmelCase     :      Tuple														=       str((Path(SCREAMING_SNAKE_CASE__							) / fname).relative_to(SCREAMING_SNAKE_CASE__							)							)
               _UpperCAmelCase     :      Dict														=       short_path.replace(".py"		,    ""							).replace(os.path.sep		,    "."							)
               if len(submodule.split("."							)							) == 1:
                    submodules.append(SCREAMING_SNAKE_CASE__							)
     return submodules


_lowerCAmelCase							:   Any				=				[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]

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





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       importlib.util.spec_from_file_location(
         "transformers"		,    os.path.join(SCREAMING_SNAKE_CASE__		,    "__init__.py"							)		,    submodule_search_locations=[PATH_TO_TRANSFORMERS]		,    )
     _UpperCAmelCase     :      Optional[int]														=       spec.loader.load_module()

     _UpperCAmelCase     :      str														=       [
         module
         for module in get_transformers_submodules()
         if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
     ]
     if len(SCREAMING_SNAKE_CASE__							) > 0:
          _UpperCAmelCase     :      Optional[Any]														=       "\n".join(f'- {module}' for module in module_not_registered							)
          raise ValueError(
              "The following submodules are not properly registered in the main init of Transformers:\n"
              f'{list_of_modules}\n'
              "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."							)


if __name__ == "__main__":
 check_all_inits()
 check_submodules()
