import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor



class     _A   (    unittest.TestCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=7   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=18   ,				SCREAMING_SNAKE_CASE_=30   ,				SCREAMING_SNAKE_CASE_=400   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]   ,				SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      size if size is not None else {'''shortest_edge''': 18}
       UpperCamelCase__										=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      min_resolution
       UpperCamelCase__										=      max_resolution
       UpperCamelCase__										=      do_resize
       UpperCamelCase__										=      size
       UpperCamelCase__										=      do_center_crop
       UpperCamelCase__										=      crop_size
       UpperCamelCase__										=      do_normalize
       UpperCamelCase__										=      image_mean
       UpperCamelCase__										=      image_std






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





       '''simple docstring'''





       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }



@require_torch
@require_vision
class     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=LevitImageProcessor if is_vision_available() else None






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





       '''simple docstring'''





       UpperCamelCase__										=      LevitImageProcessingTester(self	)






  @property
  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''image_mean'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''image_std'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''do_normalize'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''do_resize'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''do_center_crop'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''size'''	)	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 18}	)
       self.assertEqual(image_processor.crop_size   ,				{'''height''': 18, '''width''': 18}	)

       UpperCamelCase__										=      self.image_processing_class.from_dict(self.image_processor_dict   ,				size=42   ,				crop_size=84	)
       self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 42}	)
       self.assertEqual(image_processor.crop_size   ,				{'''height''': 84, '''width''': 84}	)






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





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       UpperCamelCase__										=      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=SCREAMING_SNAKE_CASE_	)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				Image.Image	)

       # Test not batched input
       UpperCamelCase__										=      image_processing(image_inputs[0]   ,				return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_images.shape   ,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)

       # Test batched
       UpperCamelCase__										=      image_processing(SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_images.shape   ,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)






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





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       UpperCamelCase__										=      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=SCREAMING_SNAKE_CASE_   ,				numpify=SCREAMING_SNAKE_CASE_	)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				np.ndarray	)

       # Test not batched input
       UpperCamelCase__										=      image_processing(image_inputs[0]   ,				return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_images.shape   ,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)

       # Test batched
       UpperCamelCase__										=      image_processing(SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_images.shape   ,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       UpperCamelCase__										=      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=SCREAMING_SNAKE_CASE_   ,				torchify=SCREAMING_SNAKE_CASE_	)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				torch.Tensor	)

       # Test not batched input
       UpperCamelCase__										=      image_processing(image_inputs[0]   ,				return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_images.shape   ,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)

       # Test batched
       UpperCamelCase__										=      image_processing(SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_images.shape   ,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       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__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      qkv_bias
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



