


'''simple docstring'''






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


__A :  List[str]    							=				"src/transformers"


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







def     UpperCamelCase_      (   A__ :       Optional[Any] ):
  if _re_test_backend.search(lowerCamelCase_ ) is None:
    return None
  lowerCAmelCase_      :				Union[str, Any]      =       [b[0] for b in _re_backend.findall(lowerCamelCase_ )]
  backends.sort()
  return "_and_".join(lowerCamelCase_ )







def     UpperCamelCase_      (   A__ :       List[str] ):
  with open(lowerCamelCase_							,   """r"""							,   encoding="""utf-8"""							,   newline="""\n""" ) as f:
    lowerCAmelCase_      :				int      =       f.readlines()

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

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

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

  lowerCAmelCase_      :				Dict      =       {'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.
    lowerCAmelCase_      :				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:
      lowerCAmelCase_      :				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

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

      lowerCAmelCase_      :				List[str]      =       objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  lowerCAmelCase_      :				int      =       []
  while (
      line_index < len(lowerCamelCase_ )
      and find_backend(lines[line_index] ) is None
      and not lines[line_index].startswith("""else""" )
  ):
    lowerCAmelCase_      :				Any      =       lines[line_index]
    lowerCAmelCase_      :				Dict      =       _re_import.search(lowerCamelCase_ )
    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

  lowerCAmelCase_      :				int      =       {'none': objects}
  # Let's continue with backend-specific objects
  while line_index < len(lowerCamelCase_ ):
    # If the line is an if is_backend_available, we grab all objects associated.
    lowerCAmelCase_      :				List[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:
      lowerCAmelCase_      :				Optional[int]      =       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

      lowerCAmelCase_      :				List[Any]      =       []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
        lowerCAmelCase_      :				Tuple      =       lines[line_index]
        lowerCAmelCase_      :				List[Any]      =       _re_import.search(lowerCamelCase_ )
        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

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

  return import_dict_objects, type_hint_objects







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       Union[str, Any] ):

  def find_duplicates(A__ :       Optional[int] ):
    return [k for k, v in collections.Counter(lowerCamelCase_ ).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!"]

  lowerCAmelCase_      :				Optional[Any]      =       []
  for key in import_dict_objects.keys():
    lowerCAmelCase_      :				List[str]      =       find_duplicates(import_dict_objects[key] )
    if duplicate_imports:
      errors.append(f'Duplicate _import_structure definitions for: {duplicate_imports}' )
    lowerCAmelCase_      :				List[Any]      =       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] ) ):
      lowerCAmelCase_      :				Optional[int]      =       '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     UpperCamelCase_      (   ):
  lowerCAmelCase_      :				Union[str, Any]      =       []
  for root, _, files in os.walk(lowerCamelCase_ ):
    if "__init__.py" in files:
      lowerCAmelCase_      :				str      =       os.path.join(lowerCamelCase_							,   """__init__.py""" )
      lowerCAmelCase_      :				Optional[Any]      =       parse_init(lowerCamelCase_ )
      if objects is not None:
        lowerCAmelCase_      :				Optional[int]      =       analyze_results(*lowerCamelCase_ )
        if len(lowerCamelCase_ ) > 0:
          lowerCAmelCase_      :				Union[str, Any]      =       f'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
          failures.append("""\n""".join(lowerCamelCase_ ) )
  if len(lowerCamelCase_ ) > 0:
    raise ValueError("""\n\n""".join(lowerCamelCase_ ) )







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


__A :  Dict    							=				[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]







def     UpperCamelCase_      (   ):
  # This is to make sure the transformers module imported is the one in the repo.
  lowerCAmelCase_      :				Optional[int]      =       importlib.util.spec_from_file_location(
      """transformers"""							,   os.path.join(lowerCamelCase_							,   """__init__.py""" )							,   submodule_search_locations=[PATH_TO_TRANSFORMERS]							,   )
  lowerCAmelCase_      :				Tuple      =       spec.loader.load_module()

  lowerCAmelCase_      :				List[str]      =       [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(lowerCamelCase_ ) > 0:
    lowerCAmelCase_      :				Union[str, 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()







'''simple docstring'''






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

  from transformers import ViTMAEForPreTraining, ViTMAEModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Tuple=13   ,						lowerCamelCase   :  Dict=30   ,						lowerCamelCase   :  Dict=2   ,						lowerCamelCase   :  Optional[int]=3   ,						lowerCamelCase   :  List[Any]=True   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  str=32   ,						lowerCamelCase   :  Any=5   ,						lowerCamelCase   :  int=4   ,						lowerCamelCase   :  List[str]=37   ,						lowerCamelCase   :  Any="gelu"   ,						lowerCamelCase   :  Optional[Any]=0.1   ,						lowerCamelCase   :  List[str]=0.1   ,						lowerCamelCase   :  str=10   ,						lowerCamelCase   :  Optional[Any]=0.02   ,						lowerCamelCase   :  List[str]=3   ,						lowerCamelCase   :  Union[str, Any]=0.6   ,						lowerCamelCase   :  List[Any]=None   ,						)       ->  Optional[int]:
      lowerCAmelCase_      :				Optional[Any]      =       parent
      lowerCAmelCase_      :				Optional[int]      =       batch_size
      lowerCAmelCase_      :				int      =       image_size
      lowerCAmelCase_      :				List[Any]      =       patch_size
      lowerCAmelCase_      :				int      =       num_channels
      lowerCAmelCase_      :				Any      =       is_training
      lowerCAmelCase_      :				Tuple      =       use_labels
      lowerCAmelCase_      :				Optional[Any]      =       hidden_size
      lowerCAmelCase_      :				List[Any]      =       num_hidden_layers
      lowerCAmelCase_      :				Optional[Any]      =       num_attention_heads
      lowerCAmelCase_      :				Dict      =       intermediate_size
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_act
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Any      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				List[Any]      =       type_sequence_label_size
      lowerCAmelCase_      :				Dict      =       initializer_range
      lowerCAmelCase_      :				List[str]      =       mask_ratio
      lowerCAmelCase_      :				Tuple      =       scope

      # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
      # (we add 1 for the [CLS] token)
      lowerCAmelCase_      :				Union[str, Any]      =       (image_size // patch_size) ** 2
      lowerCAmelCase_      :				Any      =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				str      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

      lowerCAmelCase_      :				str      =       self.get_config()

      return config, pixel_values, labels

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      return ViTMAEConfig(
          image_size=self.image_size   ,						patch_size=self.patch_size   ,						num_channels=self.num_channels   ,						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   ,						is_decoder=lowerCamelCase   ,						initializer_range=self.initializer_range   ,						mask_ratio=self.mask_ratio   ,						)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict		)       ->  Tuple:
      lowerCAmelCase_      :				Tuple      =       ViTMAEModel(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Dict:
      lowerCAmelCase_      :				Tuple      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				List[str]      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       (self.image_size // self.patch_size) ** 2
      lowerCAmelCase_      :				int      =       self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_patches, expected_num_channels)		)

      # test greyscale images
      lowerCAmelCase_      :				List[Any]      =       1
      lowerCAmelCase_      :				List[str]      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Optional[Any]      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
      lowerCAmelCase_      :				Tuple      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				List[Any]      =       self.patch_size**2
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_patches, expected_num_channels)		)





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







@require_torch
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    lowercase       			= {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Optional[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEModelTester(self		)
      lowerCAmelCase_      :				Optional[int]      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase   ,						hidden_size=37		)

    def __lowercase       (       self   :  Dict		)       ->  Tuple:
      self.config_tester.run_common_tests()

    @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""		)
    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      pass

    def __lowercase       (       self   :  List[str]		)       ->  Tuple:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		)
        self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module)		)
        lowerCAmelCase_      :				Tuple      =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase   ,						nn.Linear		)		)

    def __lowercase       (       self   :  Optional[Any]		)       ->  Any:
      lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase_      :				Optional[Any]      =       ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Tuple		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				Tuple      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[Any]		)       ->  str:
      # make masks reproducible
      np.random.seed(2		)

      lowerCAmelCase_      :				Tuple      =       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
      lowerCAmelCase_      :				Optional[int]      =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
      lowerCAmelCase_      :				Optional[Any]      =       torch.from_numpy(lowerCamelCase		)

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      lowerCAmelCase_      :				int      =       pt_noise

      super().check_pt_tf_models(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)

    def __lowercase       (       self   :  int		)       ->  Dict:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2		)
        with torch.no_grad():
          lowerCAmelCase_      :				Any      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)

        lowerCAmelCase_      :				Any      =       outputs[0].cpu().numpy()
        lowerCAmelCase_      :				List[str]      =       0

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(lowerCamelCase		)
          lowerCAmelCase_      :				int      =       model_class.from_pretrained(lowerCamelCase		)
          model.to(lowerCamelCase		)
          # make random mask reproducible
          torch.manual_seed(2		)
          with torch.no_grad():
            lowerCAmelCase_      :				str      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)

          # Make sure we don't have nans
          lowerCAmelCase_      :				Optional[Any]      =       after_outputs[0].cpu().numpy()
          lowerCAmelCase_      :				str      =       0
          lowerCAmelCase_      :				List[Any]      =       np.amax(np.abs(out_a - out_a		)		)
          self.assertLessEqual(lowerCamelCase   ,						1E-5		)

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
      pass

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Union[str, Any]		)       ->  str:
      pass

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Optional[Any]		)       ->  Union[str, Any]:
      pass

    @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""		)
    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      pass

    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def __lowercase       (       self   :  List[Any]		)       ->  str:
      pass





    @slow
    def __lowercase       (       self   :  List[str]		)       ->  List[Any]:
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_      :				List[Any]      =       ViTMAEModel.from_pretrained(lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image






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







    """simple docstring"""



    @cached_property
    def __lowercase       (       self   :  Union[str, Any]		)       ->  Union[str, Any]:
      return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""		) if is_vision_available() else None





    @slow
    def __lowercase       (       self   :  int		)       ->  List[Any]:
      # make random mask reproducible across the PT and TF model
      np.random.seed(2		)

      lowerCAmelCase_      :				Dict      =       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(lowerCamelCase		)

      lowerCAmelCase_      :				Union[str, Any]      =       self.default_image_processor
      lowerCAmelCase_      :				Union[str, Any]      =       prepare_img()
      lowerCAmelCase_      :				Dict      =       image_processor(images=lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEConfig()
      lowerCAmelCase_      :				Optional[Any]      =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
      lowerCAmelCase_      :				Optional[int]      =       np.random.uniform(size=(1, num_patches)		)

      # forward pass
      with torch.no_grad():
        lowerCAmelCase_      :				str      =       model(**lowerCamelCase   ,						noise=torch.from_numpy(lowerCamelCase		).to(device=lowerCamelCase		)		)

      # verify the logits
      lowerCAmelCase_      :				str      =       torch.Size((1, 1_96, 7_68)		)
      self.assertEqual(outputs.logits.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				str      =       torch.tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

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



