


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   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=30				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=5				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=37				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=0.6				,       _SCREAMING_SNAKE_CASE=None				,       )->Dict:




         '''simple docstring'''





         A_      :				int		     =					parent
         A_      :				Union[str, Any]		     =					batch_size
         A_      :				int		     =					image_size
         A_      :				Union[str, Any]		     =					patch_size
         A_      :				Union[str, Any]		     =					num_channels
         A_      :				Optional[Any]		     =					is_training
         A_      :				str		     =					use_labels
         A_      :				Optional[Any]		     =					hidden_size
         A_      :				str		     =					num_hidden_layers
         A_      :				str		     =					num_attention_heads
         A_      :				Optional[Any]		     =					intermediate_size
         A_      :				int		     =					hidden_act
         A_      :				Union[str, Any]		     =					hidden_dropout_prob
         A_      :				Optional[int]		     =					attention_probs_dropout_prob
         A_      :				Optional[int]		     =					type_sequence_label_size
         A_      :				List[str]		     =					initializer_range
         A_      :				Union[str, Any]		     =					mask_ratio
         A_      :				Dict		     =					scope

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



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				List[str]		     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

         A_      :				Tuple		     =					self.get_config()

         return config, pixel_values, labels



       def        _snake_case       (      self		)->List[str]:




         '''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=_SCREAMING_SNAKE_CASE				,       initializer_range=self.initializer_range				,       mask_ratio=self.mask_ratio				,       )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->List[str]:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					ViTMAEModel(config=_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Tuple		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->List[str]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Tuple		     =					model(_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					(self.image_size // self.patch_size) ** 2
         A_      :				str		     =					self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, num_patches, expected_num_channels)		)

         # test greyscale images
         A_      :				int		     =					1
         A_      :				str		     =					ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Union[str, Any]		     =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         A_      :				Dict		     =					model(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					self.patch_size**2
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, num_patches, expected_num_channels)		)







       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





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






@require_torch
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




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

       snake_case      =    False
       snake_case      =    False
       snake_case      =    False
       snake_case      =    False



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Dict		     =					ViTMAEModelTester(self		)
         A_      :				List[Any]		     =					ConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE				,       has_text_modality=_SCREAMING_SNAKE_CASE				,       hidden_size=37		)



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




         '''simple docstring'''





         self.config_tester.run_common_tests()



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




         '''simple docstring'''





         pass



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				Union[str, Any]		     =					self.model_tester.prepare_config_and_inputs_for_common()

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



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




         '''simple docstring'''





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

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

           A_      :				str		     =					['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				int		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				List[Any]		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         np.random.seed(2		)

         A_      :				Dict		     =					int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
         A_      :				Any		     =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
         A_      :				List[str]		     =					torch.from_numpy(_SCREAMING_SNAKE_CASE		)

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

         super().check_pt_tf_models(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





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

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

           A_      :				List[str]		     =					outputs[0].cpu().numpy()
           A_      :				Dict		     =					0

           with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(_SCREAMING_SNAKE_CASE		)
             A_      :				Union[str, Any]		     =					model_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
             model.to(_SCREAMING_SNAKE_CASE		)
             # make random mask reproducible
             torch.manual_seed(2		)
             with torch.no_grad():
               A_      :				Tuple		     =					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

             # Make sure we don't have nans
             A_      :				Union[str, Any]		     =					after_outputs[0].cpu().numpy()
             A_      :				Optional[int]		     =					0
             A_      :				List[str]		     =					np.amax(np.abs(out_a - out_a		)		)
             self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       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        _snake_case       (      self		)->Optional[int]:




         '''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        _snake_case       (      self		)->Optional[int]:




         '''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        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         pass



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




         '''simple docstring'''





         pass



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




         '''simple docstring'''





         pass







       @slow
       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A_      :				Optional[int]		     =					ViTMAEModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)




def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				Tuple		     =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
  return image







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




       """simple docstring"""



       @cached_property
       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





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







       @slow
       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         np.random.seed(2		)

         A_      :				Dict		     =					ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''		).to(_SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					self.default_image_processor
         A_      :				Optional[Any]		     =					prepare_img()
         A_      :				Dict		     =					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

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

         # forward pass
         with torch.no_grad():
           A_      :				int		     =					model(**_SCREAMING_SNAKE_CASE				,       noise=torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE		)		)

         # verify the logits
         A_      :				str		     =					torch.Size((1, 196, 768)		)
         self.assertEqual(outputs.logits.shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Tuple		     =					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(_SCREAMING_SNAKE_CASE		)				,       atol=1e-4		)		)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




