




'''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,		lowerCAmelCase,		lowerCAmelCase=13,		lowerCAmelCase=30,		lowerCAmelCase=2,		lowerCAmelCase=3,		lowerCAmelCase=True,		lowerCAmelCase=True,		lowerCAmelCase=32,		lowerCAmelCase=5,		lowerCAmelCase=4,		lowerCAmelCase=37,		lowerCAmelCase="gelu",		lowerCAmelCase=0.1,		lowerCAmelCase=0.1,		lowerCAmelCase=10,		lowerCAmelCase=0.0_2,		lowerCAmelCase=3,		lowerCAmelCase=0.6,		lowerCAmelCase=None,		):



              """simple docstring"""







              lowerCamelCase_							=parent
              lowerCamelCase_							=batch_size
              lowerCamelCase_							=image_size
              lowerCamelCase_							=patch_size
              lowerCamelCase_							=num_channels
              lowerCamelCase_							=is_training
              lowerCamelCase_							=use_labels
              lowerCamelCase_							=hidden_size
              lowerCamelCase_							=num_hidden_layers
              lowerCamelCase_							=num_attention_heads
              lowerCamelCase_							=intermediate_size
              lowerCamelCase_							=hidden_act
              lowerCamelCase_							=hidden_dropout_prob
              lowerCamelCase_							=attention_probs_dropout_prob
              lowerCamelCase_							=type_sequence_label_size
              lowerCamelCase_							=initializer_range
              lowerCamelCase_							=mask_ratio
              lowerCamelCase_							=scope

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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

              lowerCamelCase_							=self.get_config()

              return config, pixel_values, labels






       def      lowercase__     (							self  ):



              """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      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=ViTMAEModel(config=lowerCAmelCase  )
              model.to(lowerCAmelCase  )
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase  )
              self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







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





@require_torch
class   __UpperCamelCase       ( lowerCamelCase__			,					lowerCamelCase__			,					unittest.TestCase				):
       lowercase	:    Optional[int]      						=(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       lowercase	:    Dict      						={'feature-extraction': ViTMAEModel} if is_torch_available() else {}

       lowercase	:    Union[str, Any]      						=False
       lowercase	:    str      						=False
       lowercase	:    List[Any]      						=False
       lowercase	:    str      						=False






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=ViTMAEModelTester(self  )
              lowerCamelCase_							=ConfigTester(self,		config_class=lowerCAmelCase,		has_text_modality=lowerCAmelCase,		hidden_size=37  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              self.config_tester.run_common_tests()






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



              """simple docstring"""







              pass






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

                     lowerCamelCase_							=['''pixel_values''']
                     self.assertListEqual(arg_names[:1],		lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              np.random.seed(2  )

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

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

              super().check_pt_tf_models(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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



              """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      lowercase__     (							self  ):



              """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      lowercase__     (							self  ):



              """simple docstring"""







              pass






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



              """simple docstring"""







              pass






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



              """simple docstring"""







              pass






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowerCamelCase_							=ViTMAEModel.from_pretrained(lowerCAmelCase  )
                     self.assertIsNotNone(lowerCAmelCase  )





def      a_							(      )							->						Optional[int]:



       """simple docstring"""
       lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
       return image


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






       @cached_property
       def      lowercase__     (							self  ):



              """simple docstring"""







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






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              np.random.seed(2  )

              lowerCamelCase_							=ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  ).to(lowerCAmelCase  )

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

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

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

              lowerCamelCase_							=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'''

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       if not isinstance(A  , A       ):
              raise TypeError('''only integers accepted as input'''       )
       else:
              lowercase__      			= str(abs(A       )       )
              lowercase__      			= [list(A       ) for char in range(len(A       )       )]
              for index in range(len(A       )       ):
                     num_transpositions[index].pop(A       )
              return max(
                  int(''''''.join(list(A       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
 __import__('doctest').testmod()




