



"""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     UpperCAmelCase    :



   def __init__(       self    :	Any							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Any=1_3							,       __lowerCamelCase    :	Tuple=3_0							,       __lowerCamelCase    :	Optional[Any]=2							,       __lowerCamelCase    :	Optional[int]=3							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Optional[Any]=True							,       __lowerCamelCase    :	Optional[Any]=3_2							,       __lowerCamelCase    :	str=5							,       __lowerCamelCase    :	Tuple=4							,       __lowerCamelCase    :	List[Any]=3_7							,       __lowerCamelCase    :	Dict="gelu"							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	Dict=0.1							,       __lowerCamelCase    :	Dict=1_0							,       __lowerCamelCase    :	Any=0.0_2							,       __lowerCamelCase    :	Union[str, Any]=3							,       __lowerCamelCase    :	Any=0.6							,       __lowerCamelCase    :	int=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      image_size
     _snake_case   	=      patch_size
     _snake_case   	=      num_channels
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      type_sequence_label_size
     _snake_case   	=      initializer_range
     _snake_case   	=      mask_ratio
     _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)
     _snake_case   	=      (image_size // patch_size) ** 2
     _snake_case   	=      int(math.ceil((1 - mask_ratio) * (num_patches + 1)				)				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     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    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      ViTMAEModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      ViTMAEForPreTraining(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     _snake_case   	=      (self.image_size // self.patch_size) ** 2
     _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
     _snake_case   	=      1
     _snake_case   	=      ViTMAEForPreTraining(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
     _snake_case   	=      model(__lowerCamelCase				)
     _snake_case   	=      self.patch_size**2
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, num_patches, expected_num_channels)				)





   def    __UpperCAmelCase    (       self    :	List[str]				):



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



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Any           =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
   A__		:	Tuple           =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

   A__		:	List[str]           =  False
   A__		:	List[Any]           =  False
   A__		:	Optional[int]           =  False
   A__		:	List[str]           =  False



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      ViTMAEModelTester(self				)
     _snake_case   	=      ConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase							,       hidden_size=3_7				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



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



   @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     # make masks reproducible
     np.random.seed(2				)

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

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

     super().check_pt_tf_models(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

         # Make sure we don't have nans
         _snake_case   	=      after_outputs[0].cpu().numpy()
         _snake_case   	=      0
         _snake_case   	=      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    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     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    :	Any				):



     """simple docstring"""
     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    :	Union[str, Any]				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''				)
   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     pass



   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass





   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      ViTMAEModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				List[Any]:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







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



   @cached_property
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''				) if is_vision_available() else None





   @slow
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     # make random mask reproducible across the PT and TF model
     np.random.seed(2				)

     _snake_case   	=      ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''				).to(__lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      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)
     _snake_case   	=      ViTMAEConfig()
     _snake_case   	=      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
     _snake_case   	=      np.random.uniform(size=(1, num_patches)				)

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

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_9_6, 7_6_8)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor(
         [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]				)

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










"""simple docstring"""




import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")

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





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       test_file.split(os.path.sep							)
     if components[0:2] != ["tests", "models"]:
          raise ValueError(
              "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
              f'{test_file} instead.'							)
     _UpperCAmelCase     :      int														=       components[-1]
     if not test_fn.endswith("py"							):
          raise ValueError(f'`test_file` should be a python file. Got {test_fn} instead.'							)
     if not test_fn.startswith("test_modeling_"							):
          raise ValueError(
              f'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'							)

     _UpperCAmelCase     :      Any														=       components[:-1] + [test_fn.replace(".py"		,    ""							)]
     _UpperCAmelCase     :      List[str]														=       ".".join(SCREAMING_SNAKE_CASE__							)

     return test_module_path

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       get_module_path(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       importlib.import_module(SCREAMING_SNAKE_CASE__							)

     return test_module

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





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      str														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          if attr.endswith("ModelTester"							):
               tester_classes.append(getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

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





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      Tuple														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      List[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
          # `all_model_classes` is not empty (which also excludes other special classes).
          _UpperCAmelCase     :      Optional[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    "all_model_classes"		,    []							)
          if len(SCREAMING_SNAKE_CASE__							) > 0:
               test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       set()
     for test_class in test_classes:
          model_classes.update(test_class.all_model_classes							)

     # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       test_class()
     if hasattr(SCREAMING_SNAKE_CASE__		,    "setUp"							):
          test.setUp()

     _UpperCAmelCase     :      int														=       None
     if hasattr(SCREAMING_SNAKE_CASE__		,    "model_tester"							):
          # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
          if test.model_tester is not None:
               _UpperCAmelCase     :      List[str]														=       test.model_tester.__class__

     return model_tester

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





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       []
     for test_class in test_classes:
          if model_class in test_class.all_model_classes:
               target_test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

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





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      int														=       []
     for test_class in test_classes:
          _UpperCAmelCase     :      Optional[Any]														=       get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							)
          if tester_class is not None:
               tester_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       {test_class: get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							) for test_class in test_classes}
     return test_tester_mapping

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





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       {
         model_class: get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_test_mapping

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





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       {
         model_class: get_tester_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_to_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  List[str]:





     '''simple docstring'''

     if isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o.__name__
     elif isinstance(SCREAMING_SNAKE_CASE__		,    (list, tuple)							):
          return [to_json(SCREAMING_SNAKE_CASE__							) for x in o]
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return {to_json(SCREAMING_SNAKE_CASE__							): to_json(SCREAMING_SNAKE_CASE__							) for k, v in o.items()}
     else:
          return o
