






"""simple docstring"""





from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_=13 ,				snake_case_=30 ,				snake_case_=2 ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=2 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=10 ,				snake_case_=0.02 ,				snake_case_=3 ,				snake_case_=0.6 ,				snake_case_=None ,				)		->       Any:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_labels
       UpperCamelCase__         =    hidden_size
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    intermediate_size
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    type_sequence_label_size
       UpperCamelCase__         =    initializer_range
       UpperCamelCase__         =    mask_ratio
       UpperCamelCase__         =    scope

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

       UpperCamelCase__         =    self.get_config()

       return config, pixel_values, labels

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       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 ,				decoder_hidden_size=self.hidden_size ,				decoder_num_hidden_layers=self.num_hidden_layers ,				decoder_num_attention_heads=self.num_attention_heads ,				decoder_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=snake_case_ ,				initializer_range=self.initializer_range ,				mask_ratio=self.mask_ratio ,				)

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       int:
       UpperCamelCase__         =    TFViTMAEModel(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_ ,				training=snake_case_      )
       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       str:
       UpperCamelCase__         =    TFViTMAEForPreTraining(snake_case_      )
       UpperCamelCase__         =    model(snake_case_ ,				training=snake_case_      )
       # expected sequence length = num_patches
       UpperCamelCase__         =    (self.image_size // self.patch_size) ** 2
       UpperCamelCase__         =    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__         =    1
       UpperCamelCase__         =    TFViTMAEForPreTraining(snake_case_      )

       UpperCamelCase__         =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       UpperCamelCase__         =    model(snake_case_ ,				training=snake_case_      )
       UpperCamelCase__         =    self.patch_size**2
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, num_patches, expected_num_channels)      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       ((UpperCamelCase__)       ,						(UpperCamelCase__)       ,						(UpperCamelCase__))         =    config_and_inputs
       UpperCamelCase__         =    {'pixel_values': pixel_values}
       return config, inputs_dict







@require_tf
class    __lowerCamelCase    (  _a							,	_a							,	unittest.TestCase  ):
   a      :				int 						=(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   a      :				List[str] 						={"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

   a      :				List[Any] 						=False
   a      :				str 						=False
   a      :				Dict 						=False
   a      :				Dict 						=False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    TFViTMAEModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=snake_case_ ,				has_text_modality=snake_case_ ,				hidden_size=37      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       self.config_tester.run_common_tests()

   @unittest.skip(reason='ViTMAE does not use inputs_embeds'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       pass

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )
           self.assertIsInstance(model.get_input_embeddings() ,				(tf.keras.layers.Layer)      )
           UpperCamelCase__         =    model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(snake_case_ ,				tf.keras.layers.Layer      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__         =    ['pixel_values']
           self.assertListEqual(arg_names[:1] ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       # make the mask reproducible
       np.random.seed(2      )

       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__         =    int((config.image_size // config.patch_size) ** 2      )
       UpperCamelCase__         =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )
           UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )

           UpperCamelCase__         =    model(snake_case_ ,				noise=snake_case_      )

           UpperCamelCase__         =    copy.deepcopy(self._prepare_for_class(snake_case_ ,				snake_case_      )      )
           UpperCamelCase__         =    model(**snake_case_ ,				noise=snake_case_      )
           UpperCamelCase__         =    outputs_dict[0].numpy()
           UpperCamelCase__         =    outputs_keywords[0].numpy()

           self.assertLess(np.sum(np.abs(output_dict - output_keywords      )      ) ,				1E-6      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       # make the mask reproducible
       np.random.seed(2      )

       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__         =    int((config.image_size // config.patch_size) ** 2      )
       UpperCamelCase__         =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

       def prepare_numpy_arrays(snake_case_      ):
           UpperCamelCase__         =    {}
           for k, v in inputs_dict.items():
               if tf.is_tensor(snake_case_      ):
                   UpperCamelCase__         =    v.numpy()
               else:
                   UpperCamelCase__         =    np.array(snake_case_      )

           return inputs_np_dict

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )

           UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )
           UpperCamelCase__         =    prepare_numpy_arrays(snake_case_      )

           UpperCamelCase__         =    model(snake_case_ ,				noise=snake_case_      )
           UpperCamelCase__         =    model(**snake_case_ ,				noise=snake_case_      )
           self.assert_outputs_same(snake_case_ ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Any:
       # make masks reproducible
       np.random.seed(2      )

       UpperCamelCase__         =    int((tf_model.config.image_size // tf_model.config.patch_size) ** 2      )
       UpperCamelCase__         =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
       UpperCamelCase__         =    tf.constant(snake_case_      )

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

       super().check_pt_tf_models(snake_case_ ,				snake_case_ ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       # make mask reproducible
       np.random.seed(2      )

       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__         =    {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__      ),)
           for module_member_name in dir(snake_case_      )
           if module_member_name.endswith('MainLayer'      )
           # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
           and module_member_name[: -len('MainLayer'      )] == model_class.__name__[: -len('Model'      )]
           for module_member in (getattr(snake_case_ ,				snake_case_      ),)
           if isinstance(snake_case_ ,				snake_case_      )
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(snake_case_ ,				'_keras_serializable' ,				snake_case_      )
       }

       UpperCamelCase__         =    int((config.image_size // config.patch_size) ** 2      )
       UpperCamelCase__         =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
       UpperCamelCase__         =    tf.convert_to_tensor(snake_case_      )
       inputs_dict.update({'noise': noise}      )

       for main_layer_class in tf_main_layer_classes:
           UpperCamelCase__         =    main_layer_class(snake_case_      )

           UpperCamelCase__         =    {
               name: tf.keras.Input(tensor.shape[1:] ,				dtype=tensor.dtype      ) for name, tensor in inputs_dict.items()
           }

           UpperCamelCase__         =    tf.keras.Model(snake_case_ ,				outputs=main_layer(snake_case_      )      )
           UpperCamelCase__         =    model(snake_case_      )

           with tempfile.TemporaryDirectory() as tmpdirname:
               UpperCamelCase__         =    os.path.join(snake_case_ ,				'keras_model.h5'      )
               model.save(snake_case_      )
               UpperCamelCase__         =    tf.keras.models.load_model(
                   snake_case_ ,				custom_objects={main_layer_class.__name__: main_layer_class}      )
               assert isinstance(snake_case_ ,				tf.keras.Model      )
               UpperCamelCase__         =    model(snake_case_      )
               self.assert_outputs_same(snake_case_ ,				snake_case_      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       # make mask reproducible
       np.random.seed(2      )

       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__         =    int((config.image_size // config.patch_size) ** 2      )
       UpperCamelCase__         =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )
           UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )
           UpperCamelCase__         =    model(snake_case_ ,				noise=snake_case_      )

           if model_class.__name__ == "TFViTMAEModel":
               UpperCamelCase__         =    outputs.last_hidden_state.numpy()
               UpperCamelCase__         =    0
           else:
               UpperCamelCase__         =    outputs.logits.numpy()
               UpperCamelCase__         =    0

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(snake_case_ ,				saved_model=snake_case_      )
               UpperCamelCase__         =    model_class.from_pretrained(snake_case_      )
               UpperCamelCase__         =    model(snake_case_ ,				noise=snake_case_      )

               if model_class.__name__ == "TFViTMAEModel":
                   UpperCamelCase__         =    after_outputs['last_hidden_state'].numpy()
                   UpperCamelCase__         =    0
               else:
                   UpperCamelCase__         =    after_outputs['logits'].numpy()
                   UpperCamelCase__         =    0

               UpperCamelCase__         =    np.amax(np.abs(out_a - out_a      )      )
               self.assertLessEqual(snake_case_ ,				1E-5      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       # make mask reproducible
       np.random.seed(2      )

       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__         =    int((config.image_size // config.patch_size) ** 2      )
       UpperCamelCase__         =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )
           UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )

           UpperCamelCase__         =    model(snake_case_ ,				noise=snake_case_      )
           UpperCamelCase__         =    model.get_config()
           # make sure that returned config is jsonifiable, which is required by keras
           json.dumps(snake_case_      )
           UpperCamelCase__         =    model_class.from_config(model.get_config()      )
           # make sure it also accepts a normal config
           UpperCamelCase__         =    model_class.from_config(model.config      )
           UpperCamelCase__         =    new_model(snake_case_      )  # Build model
           new_model.set_weights(model.get_weights()      )
           UpperCamelCase__         =    new_model(snake_case_ ,				noise=snake_case_      )

           self.assert_outputs_same(snake_case_ ,				snake_case_      )

   @unittest.skip(
       reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       pass

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







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'      )
       self.assertIsNotNone(snake_case_      )







def   lowerCAmelCase_( )					->	Optional[Any]:
    """simple docstring"""

    UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image





@require_tf
@require_vision
class    __lowerCamelCase    (  unittest.TestCase  ):

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







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       # make random mask reproducible across the PT and TF model
       np.random.seed(2      )

       UpperCamelCase__         =    TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='tf'      )

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

       # forward pass
       UpperCamelCase__         =    model(**snake_case_ ,				noise=snake_case_      )

       # verify the logits
       UpperCamelCase__         =    tf.convert_to_tensor([1, 196, 768]      )
       self.assertEqual(outputs.logits.shape ,				snake_case_      )

       UpperCamelCase__         =    tf.convert_to_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]]      )

       tf.debugging.assert_near(outputs.logits[0, :3, :3] ,				snake_case_ ,				atol=1E-4      )











"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




