





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						:







  def __init__(     self						:   Optional[int]  ,					__snake_case						:   str  ,					__snake_case						:   Optional[Any]=13  ,					__snake_case						:   List[str]=30  ,					__snake_case						:   List[Any]=2  ,					__snake_case						:   Optional[int]=3  ,					__snake_case						:   int=True  ,					__snake_case						:   List[Any]=True  ,					__snake_case						:   str=32  ,					__snake_case						:   List[str]=5  ,					__snake_case						:   List[str]=4  ,					__snake_case						:   Any=37  ,					__snake_case						:   Dict="gelu"  ,					__snake_case						:   str=0.1  ,					__snake_case						:   Optional[int]=0.1  ,					__snake_case						:   List[str]=10  ,					__snake_case						:   Optional[int]=0.02  ,					__snake_case						:   str=3  ,					__snake_case						:   Union[str, Any]=0.6  ,					__snake_case						:   str=None  ,					)    ->		Any:
        _a							:  Tuple				= parent
        _a							:  Optional[int]				= batch_size
        _a							:  List[Any]				= image_size
        _a							:  List[str]				= patch_size
        _a							:  Tuple				= num_channels
        _a							:  List[str]				= is_training
        _a							:  Optional[Any]				= use_labels
        _a							:  Tuple				= hidden_size
        _a							:  Dict				= num_hidden_layers
        _a							:  Tuple				= num_attention_heads
        _a							:  int				= intermediate_size
        _a							:  str				= hidden_act
        _a							:  int				= hidden_dropout_prob
        _a							:  Optional[Any]				= attention_probs_dropout_prob
        _a							:  Optional[int]				= type_sequence_label_size
        _a							:  List[str]				= initializer_range
        _a							:  List[str]				= mask_ratio
        _a							:  str				= scope

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







  def      snake_case_							(     self						:   str       )    ->		List[Any]:
        _a							:  Optional[Any]				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

        _a							:  Dict				= self.get_config()

        return config, pixel_values, labels







  def      snake_case_							(     self						:   Tuple       )    ->		Optional[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=_UpperCamelCase  ,					initializer_range=self.initializer_range  ,					mask_ratio=self.mask_ratio  ,					)







  def      snake_case_							(     self						:   Dict  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   Union[str, Any]  ,					__snake_case						:   List[str]       )    ->		Optional[Any]:
        _a							:  Any				= ViTMAEModel(config=_UpperCamelCase       )
        model.to(_UpperCamelCase       )
        model.eval()
        _a							:  Dict				= model(_UpperCamelCase       )
        self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)       )







  def      snake_case_							(     self						:   List[Any]  ,					__snake_case						:   Any  ,					__snake_case						:   int  ,					__snake_case						:   Tuple       )    ->		Dict:
        _a							:  Optional[int]				= ViTMAEForPreTraining(_UpperCamelCase       )
        model.to(_UpperCamelCase       )
        model.eval()
        _a							:  List[Any]				= model(_UpperCamelCase       )
        _a							:  List[str]				= (self.image_size // self.patch_size) ** 2
        _a							:  List[Any]				= 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							:  Tuple				= 1
        _a							:  Union[str, Any]				= ViTMAEForPreTraining(_UpperCamelCase       )
        model.to(_UpperCamelCase       )
        model.eval()
        _a							:  Any				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        _a							:  Optional[Any]				= model(_UpperCamelCase       )
        _a							:  Union[str, Any]				= self.patch_size**2
        self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels)       )







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		List[str]:
        _a							:  Optional[int]				= self.prepare_config_and_inputs()
        _a	, _a	, _a							:  Dict				= config_and_inputs
        _a							:  List[str]				= {'''pixel_values''': pixel_values}
        return config, inputs_dict


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

  UpperCAmelCase		:       Dict										=     False
  UpperCAmelCase		:       List[Any]										=     False
  UpperCAmelCase		:       int										=     False
  UpperCAmelCase		:       Optional[int]										=     False







  def      snake_case_							(     self						:   Tuple       )    ->		Union[str, Any]:
        _a							:  Any				= ViTMAEModelTester(self       )
        _a							:  str				= ConfigTester(self  ,					config_class=_UpperCamelCase  ,					has_text_modality=_UpperCamelCase  ,					hidden_size=37       )







  def      snake_case_							(     self						:   List[Any]       )    ->		Tuple:
        self.config_tester.run_common_tests()







  @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''       )
  def      snake_case_							(     self						:   Optional[Any]       )    ->		int:
        pass







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		Dict:
        _a	, _a							:  Any				= self.model_tester.prepare_config_and_inputs_for_common()

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







  def      snake_case_							(     self						:   List[str]       )    ->		List[str]:
        _a	, _a							:  Dict				= self.model_tester.prepare_config_and_inputs_for_common()

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

              _a							:  List[Any]				= ['''pixel_values''']
              self.assertListEqual(arg_names[:1]  ,					_UpperCamelCase       )







  def      snake_case_							(     self						:   List[Any]       )    ->		int:
        _a							:  int				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCamelCase       )







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		Optional[int]:
        _a							:  str				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_UpperCamelCase       )







  def      snake_case_							(     self						:   Tuple  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   Tuple  ,					__snake_case						:   Optional[int]       )    ->		List[Any]:
        # make masks reproducible
        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[Any]				= torch.from_numpy(_UpperCamelCase       )

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        _a							:  Union[str, Any]				= pt_noise

        super().check_pt_tf_models(_UpperCamelCase  ,					_UpperCamelCase  ,					_UpperCamelCase       )







  def      snake_case_							(     self						:   Dict       )    ->		List[str]:
        _a	, _a							:  List[str]				= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _a							:  List[str]				= model_class(_UpperCamelCase       )
              model.to(_UpperCamelCase       )
              model.eval()
              # make random mask reproducible
              torch.manual_seed(2       )
              with torch.no_grad():
                    _a							:  Dict				= model(**self._prepare_for_class(_UpperCamelCase  ,					_UpperCamelCase       )       )

              _a							:  Tuple				= outputs[0].cpu().numpy()
              _a							:  str				= 0

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

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







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







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







  @slow
  def      snake_case_							(     self						:   str       )    ->		Tuple:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _a							:  List[Any]				= ViTMAEModel.from_pretrained(_UpperCamelCase       )
              self.assertIsNotNone(_UpperCamelCase       )




def 						lowerCamelCase_	(		):
      _a							:  List[str]				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
      return image





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







  @cached_property
  def      snake_case_							(     self						:   int       )    ->		str:
        return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''       ) if is_vision_available() else None







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

        _a							:  Optional[Any]				= ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''       ).to(_UpperCamelCase       )

        _a							:  Tuple				= self.default_image_processor
        _a							:  Any				= prepare_img()
        _a							:  str				= image_processor(images=_UpperCamelCase  ,					return_tensors='''pt'''       ).to(_UpperCamelCase       )

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

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

        # verify the logits
        _a							:  List[Any]				= torch.Size((1, 196, 768)       )
        self.assertEqual(outputs.logits.shape  ,					_UpperCamelCase       )

        _a							:  str				= torch.tensor(
            [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]       )

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









'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       int = 0							,    __UpperCamelCase					:       int = 0		)					->	int:







     """simple docstring"""
     _A				=							right or len(__UpperCamelCase		) - 1
     if left > right:
          return -1
     elif list_data[left] == key:
          return left
     elif list_data[right] == key:
          return right
     else:
          return search(__UpperCamelCase							,    __UpperCamelCase							,    left + 1							,    right - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()





