





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class    A__  (			unittest.TestCase     ):





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  TFCamembertModel.from_pretrained("jplu/tf-camembert-base"  )

       UpperCamelCase      :   Optional[Any]			      =  tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]			,				dtype=tf.intaa			,				)  # J'aime le camembert !"

       UpperCamelCase      :   Optional[Any]			      =  model(A_  )["last_hidden_state"]
       UpperCamelCase      :   List[str]			      =  tf.TensorShape((1, 10, 768)  )
       self.assertEqual(output.shape			,				A_  )
       # compare the actual values for a slice.
       UpperCamelCase      :   Optional[Any]			      =  tf.convert_to_tensor(
           [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,				dtype=tf.floataa			,				)
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,				expected_slice.numpy()			,				atol=1e-4  )  )







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    A__  :





      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			,				):
       '''simple docstring'''




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

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       UpperCamelCase      :   str			      =  self.get_config()

       return config, pixel_values, labels





      def 			__UpperCamelCase(					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=A_			,				initializer_range=self.initializer_range			,				mask_ratio=self.mask_ratio			,				)





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  ViTMAEModel(config=A_  )
       model.to(A_  )
       model.eval()
       UpperCamelCase      :   Optional[Any]			      =  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_  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  ViTMAEForPreTraining(A_  )
       model.to(A_  )
       model.eval()
       UpperCamelCase      :   List[str]			      =  model(A_  )
       UpperCamelCase      :   Any			      =  (self.image_size // self.patch_size) ** 2
       UpperCamelCase      :   List[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
       UpperCamelCase      :   str			      =  1
       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEForPreTraining(A_  )
       model.to(A_  )
       model.eval()
       UpperCamelCase      :   Union[str, Any]			      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       UpperCamelCase      :   Union[str, Any]			      =  model(A_  )
       UpperCamelCase      :   List[str]			      =  self.patch_size**2
       self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels)  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.prepare_config_and_inputs()
       UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  config_and_inputs
       UpperCamelCase      :   Optional[int]			      =  {"pixel_values": pixel_values}
       return config, inputs_dict



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

      _UpperCAmelCase      :Optional[Any]		       =  False
      _UpperCAmelCase      :Any		       =  False
      _UpperCAmelCase      :Optional[int]		       =  False
      _UpperCAmelCase      :Dict		       =  False





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEModelTester(self  )
       UpperCamelCase      :   Any			      =  ConfigTester(self			,				config_class=A_			,				has_text_modality=A_			,				hidden_size=37  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      @unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   List[Any]			      =  self.model_tester.prepare_config_and_inputs_for_common()

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase      :   Optional[int]			      =  ["pixel_values"]
        self.assertListEqual(arg_names[:1]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       np.random.seed(2  )

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

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

       super().check_pt_tf_models(A_			,				A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Tuple			      =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

         # Make sure we don't have nans
         UpperCamelCase      :   Dict			      =  after_outputs[0].cpu().numpy()
         UpperCamelCase      :   int			      =  0
         UpperCamelCase      :   str			      =  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\n    to get deterministic results."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @unittest.skip(
          reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @unittest.skip(
          reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





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




       pass





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




       pass





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase      :   List[str]			      =  ViTMAEModel.from_pretrained(A_  )
        self.assertIsNotNone(A_  )



def 						A_							(	)		->					Optional[int]:
 UpperCamelCase      :   List[Any]			      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
 return image



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





      @cached_property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(A_  )

       UpperCamelCase      :   Any			      =  self.default_image_processor
       UpperCamelCase      :   Optional[Any]			      =  prepare_img()
       UpperCamelCase      :   int			      =  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)
       UpperCamelCase      :   Dict			      =  ViTMAEConfig()
       UpperCamelCase      :   Any			      =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
       UpperCamelCase      :   Optional[Any]			      =  np.random.uniform(size=(1, num_patches)  )

       # forward pass
       with torch.no_grad():
        UpperCamelCase      :   Optional[Any]			      =  model(**A_			,				noise=torch.from_numpy(A_  ).to(device=A_  )  )

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

       UpperCamelCase      :   str			      =  torch.tensor(
           [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]  )

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