
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 							UpperCamelCase__						:






       """simple docstring"""


       def __init__(				self      ,			_A      ,			_A=13      ,			_A=30      ,			_A=2      ,			_A=3      ,			_A=True      ,			_A=True      ,			_A=32      ,			_A=5      ,			_A=4      ,			_A=37      ,			_A="gelu"      ,			_A=0.1      ,			_A=0.1      ,			_A=10      ,			_A=0.02      ,			_A=3      ,			_A=0.6      ,			_A=None      ,			)							->							int:
          SCREAMING_SNAKE_CASE_     =	parent
          SCREAMING_SNAKE_CASE_     =	batch_size
          SCREAMING_SNAKE_CASE_     =	image_size
          SCREAMING_SNAKE_CASE_     =	patch_size
          SCREAMING_SNAKE_CASE_     =	num_channels
          SCREAMING_SNAKE_CASE_     =	is_training
          SCREAMING_SNAKE_CASE_     =	use_labels
          SCREAMING_SNAKE_CASE_     =	hidden_size
          SCREAMING_SNAKE_CASE_     =	num_hidden_layers
          SCREAMING_SNAKE_CASE_     =	num_attention_heads
          SCREAMING_SNAKE_CASE_     =	intermediate_size
          SCREAMING_SNAKE_CASE_     =	hidden_act
          SCREAMING_SNAKE_CASE_     =	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_     =	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_     =	type_sequence_label_size
          SCREAMING_SNAKE_CASE_     =	initializer_range
          SCREAMING_SNAKE_CASE_     =	mask_ratio
          SCREAMING_SNAKE_CASE_     =	scope

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



       def   _UpperCamelCase     (				self     )							->							Dict:
          SCREAMING_SNAKE_CASE_     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

          SCREAMING_SNAKE_CASE_     =	None
          if self.use_labels:
             SCREAMING_SNAKE_CASE_     =	ids_tensor([self.batch_size]      ,			self.type_sequence_label_size     )

          SCREAMING_SNAKE_CASE_     =	self.get_config()

          return config, pixel_values, labels



       def   _UpperCamelCase     (				self     )							->							List[Any]:
          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=_A      ,			initializer_range=self.initializer_range      ,			mask_ratio=self.mask_ratio      ,			)



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A     )							->							Tuple:
          SCREAMING_SNAKE_CASE_     =	ViTMAEModel(config=_A     )
          model.to(_A     )
          model.eval()
          SCREAMING_SNAKE_CASE_     =	model(_A     )
          self.parent.assertEqual(result.last_hidden_state.shape      ,			(self.batch_size, self.seq_length, self.hidden_size)     )



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A     )							->							List[str]:
          SCREAMING_SNAKE_CASE_     =	ViTMAEForPreTraining(_A     )
          model.to(_A     )
          model.eval()
          SCREAMING_SNAKE_CASE_     =	model(_A     )
          SCREAMING_SNAKE_CASE_     =	(self.image_size // self.patch_size) ** 2
          SCREAMING_SNAKE_CASE_     =	self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, num_patches, expected_num_channels)     )

          # test greyscale images
          SCREAMING_SNAKE_CASE_     =	1
          SCREAMING_SNAKE_CASE_     =	ViTMAEForPreTraining(_A     )
          model.to(_A     )
          model.eval()
          SCREAMING_SNAKE_CASE_     =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
          SCREAMING_SNAKE_CASE_     =	model(_A     )
          SCREAMING_SNAKE_CASE_     =	self.patch_size**2
          self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, num_patches, expected_num_channels)     )




       def   _UpperCamelCase     (				self     )							->							Tuple:
          SCREAMING_SNAKE_CASE_     =	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_     =	config_and_inputs
          SCREAMING_SNAKE_CASE_     =	{"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class 							UpperCamelCase__						(     __lowerCamelCase    ,  __lowerCamelCase    ,  unittest.TestCase     ):






       """simple docstring"""


       UpperCAmelCase_							    =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       UpperCAmelCase_							    ={"feature-extraction": ViTMAEModel} if is_torch_available() else {}

       UpperCAmelCase_							    =False
       UpperCAmelCase_							    =False
       UpperCAmelCase_							    =False
       UpperCAmelCase_							    =False



       def   _UpperCamelCase     (				self     )							->							List[str]:
          SCREAMING_SNAKE_CASE_     =	ViTMAEModelTester(self     )
          SCREAMING_SNAKE_CASE_     =	ConfigTester(self      ,			config_class=_A      ,			has_text_modality=_A      ,			hidden_size=37     )



       def   _UpperCamelCase     (				self     )							->							Dict:
          self.config_tester.run_common_tests()



       @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''     )
       def   _UpperCamelCase     (				self     )							->							List[str]:
          pass



       def   _UpperCamelCase     (				self     )							->							int:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs_for_common()

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



       def   _UpperCamelCase     (				self     )							->							Optional[int]:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_     =	["""pixel_values"""]
             self.assertListEqual(arg_names[:1]      ,			_A     )



       def   _UpperCamelCase     (				self     )							->							List[str]:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_A     )



       def   _UpperCamelCase     (				self     )							->							str:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*_A     )



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A     )							->							Any:
          # make masks reproducible
          np.random.seed(2     )

          SCREAMING_SNAKE_CASE_     =	int((pt_model.config.image_size // pt_model.config.patch_size) ** 2     )
          SCREAMING_SNAKE_CASE_     =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
          SCREAMING_SNAKE_CASE_     =	torch.from_numpy(_A     )

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

          super().check_pt_tf_models(_A      ,			_A      ,			_A     )



       def   _UpperCamelCase     (				self     )							->							Dict:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_     =	model_class(_A     )
             model.to(_A     )
             model.eval()
             # make random mask reproducible
             torch.manual_seed(2     )
             with torch.no_grad():
                SCREAMING_SNAKE_CASE_     =	model(**self._prepare_for_class(_A      ,			_A     )     )

             SCREAMING_SNAKE_CASE_     =	outputs[0].cpu().numpy()
             SCREAMING_SNAKE_CASE_     =	0

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

                # Make sure we don't have nans
                SCREAMING_SNAKE_CASE_     =	after_outputs[0].cpu().numpy()
                SCREAMING_SNAKE_CASE_     =	0
                SCREAMING_SNAKE_CASE_     =	np.amax(np.abs(out_a - out_a     )     )
                self.assertLessEqual(_A      ,			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   _UpperCamelCase     (				self     )							->							Union[str, 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   _UpperCamelCase     (				self     )							->							int:
          pass



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



       @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''     )
       def   _UpperCamelCase     (				self     )							->							List[str]:
          pass



       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
       def   _UpperCamelCase     (				self     )							->							int:
          pass




       @slow
       def   _UpperCamelCase     (				self     )							->							Union[str, Any]:
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_     =	ViTMAEModel.from_pretrained(_A     )
             self.assertIsNotNone(_A     )







def       A__      (    ):
   SCREAMING_SNAKE_CASE_     =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
   return image





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






       """simple docstring"""


       @cached_property
       def   _UpperCamelCase     (				self     )							->							List[Any]:
          return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''     ) if is_vision_available() else None




       @slow
       def   _UpperCamelCase     (				self     )							->							Union[str, Any]:
          # make random mask reproducible across the PT and TF model
          np.random.seed(2     )

          SCREAMING_SNAKE_CASE_     =	ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''     ).to(_A     )

          SCREAMING_SNAKE_CASE_     =	self.default_image_processor
          SCREAMING_SNAKE_CASE_     =	prepare_img()
          SCREAMING_SNAKE_CASE_     =	image_processor(images=_A      ,			return_tensors='''pt'''     ).to(_A     )

          # 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)
          SCREAMING_SNAKE_CASE_     =	ViTMAEConfig()
          SCREAMING_SNAKE_CASE_     =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
          SCREAMING_SNAKE_CASE_     =	np.random.uniform(size=(1, num_patches)     )

          # forward pass
          with torch.no_grad():
             SCREAMING_SNAKE_CASE_     =	model(**_A      ,			noise=torch.from_numpy(_A     ).to(device=_A     )     )

          # verify the logits
          SCREAMING_SNAKE_CASE_     =	torch.Size((1, 196, 768)     )
          self.assertEqual(outputs.logits.shape      ,			_A     )

          SCREAMING_SNAKE_CASE_     =	torch.tensor(
              [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]     )

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









import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__UpperCAmelCase       :   Any				       =							"src/diffusers"

# Matches is_xxx_available()
__UpperCAmelCase       :   List[str]				       =							re.compile(R"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
__UpperCAmelCase       :   Dict				       =							re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


__UpperCAmelCase       :   int				       =							"\n{0} = None\n"

__UpperCAmelCase       :   List[str]				       =							"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


__UpperCAmelCase       :   Tuple				       =							"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						int   =						_re_backend.findall(SCREAMING_SNAKE_CASE__)
  if len(SCREAMING_SNAKE_CASE__) == 0:
    return None

  return "_and_".join(SCREAMING_SNAKE_CASE__)







def 			A__							(    )   ->    Optional[int]:
  with open(os.path.join(SCREAMING_SNAKE_CASE__					,					"""__init__.py""")					,					"""r"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
    __snake_case:						Optional[Any]   =						f.readlines()

  # Get to the point we do the actual imports for type checking
  __snake_case:						Tuple   =						0
  __snake_case:						Any   =						{}
  # Go through the end of the file
  while line_index < len(SCREAMING_SNAKE_CASE__):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    __snake_case:						List[Any]   =						find_backend(lines[line_index])
    if backend is not None:
      while not lines[line_index].startswith("""else:"""):
        line_index += 1
      line_index += 1
      __snake_case:						Any   =						[]
      # Until we unindent, add backend objects to the list
      while line_index < len(SCREAMING_SNAKE_CASE__) and len(lines[line_index]) > 1:
        __snake_case:						List[Any]   =						lines[line_index]
        __snake_case:						str   =						_re_single_line_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

      if len(SCREAMING_SNAKE_CASE__) > 0:
        __snake_case:						Optional[Any]   =						objects
    else:
      line_index += 1

  return backend_specific_objects







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    List[Any]:
  if name.isupper():
    return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__)
  elif name.islower():
    return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
  else:
    return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)







def 			A__							(    SCREAMING_SNAKE_CASE__=None)   ->    Optional[int]:
  if backend_specific_objects is None:
    __snake_case:						Union[str, Any]   =						read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __snake_case:						Union[str, Any]   =						{}

  for backend, objects in backend_specific_objects.items():
    __snake_case:						List[Any]   =						"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_""")) + """]"""
    __snake_case:						Dict   =						"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__) for o in objects])
    __snake_case:						List[str]   =						dummy_file

  return dummy_files







def 			A__							(    SCREAMING_SNAKE_CASE__=False)   ->    int:
  __snake_case:						List[str]   =						create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __snake_case:						Dict   =						{"""torch""": """pt"""}

  # Locate actual dummy modules and read their content.
  __snake_case:						Optional[Any]   =						os.path.join(SCREAMING_SNAKE_CASE__					,					"""utils""")
  __snake_case:						List[Any]   =						{
      backend: os.path.join(SCREAMING_SNAKE_CASE__					,					F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py''')
      for backend in dummy_files.keys()
  }

  __snake_case:						int   =						{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(SCREAMING_SNAKE_CASE__):
      with open(SCREAMING_SNAKE_CASE__					,					"""r"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
        __snake_case:						Any   =						f.read()
    else:
      __snake_case:						Tuple   =						""""""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py as the main '''
            """__init__ has new objects.""")
        with open(dummy_file_paths[backend]					,					"""w"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
          f.write(dummy_files[backend])
      else:
        raise ValueError(
            """The main __init__ has objects that are not present in """
            F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py. Run `make fix-copies` '''
            """to fix this.""")


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      __UpperCAmelCase       :   List[Any]				       =							parser.parse_args()

      check_dummies(args.fix_and_overwrite)






