





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 ViTImageProcessor

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def __init__(self       :      Tuple				,			a__       :      int				,			a__       :      List[Any]=13				,			a__       :      Optional[int]=3				,			a__       :      List[str]=224				,			a__       :      List[Any]=30				,			a__       :      Union[str, Any]=400				,			a__       :      str=True				,			a__       :      List[Any]=None				,			a__       :      int=True				,			a__       :      Dict=[0.5, 0.5, 0.5]				,			a__       :      Union[str, Any]=[0.5, 0.5, 0.5]				,			):





         """simple docstring"""



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


      def    a (self       :      Any      ):





         """simple docstring"""



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






@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Optional[Any]							=    ViTImageProcessor if is_vision_available() else None


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   EfficientFormerImageProcessorTester(self      )


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         return self.image_proc_tester.prepare_image_processor_dict()


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(a__				,			'''image_mean'''      )      )
         self.assertTrue(hasattr(a__				,			'''image_std'''      )      )
         self.assertTrue(hasattr(a__				,			'''do_normalize'''      )      )
         self.assertTrue(hasattr(a__				,			'''do_resize'''      )      )
         self.assertTrue(hasattr(a__				,			'''size'''      )      )


      def    a (self       :      Any      ):





         """simple docstring"""



         pass


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         __snake_case							       =   prepare_image_inputs(self.image_proc_tester				,			equal_resolution=a__      )
         for image in image_inputs:
            self.assertIsInstance(a__				,			Image.Image      )

         # Test not batched input
         __snake_case							       =   image_processor(image_inputs[0]				,			return_tensors='''pt'''      ).pixel_values
         self.assertEqual(
             encoded_images.shape				,			(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )				,			)

         # Test batched
         __snake_case							       =   image_processor(a__				,			return_tensors='''pt'''      ).pixel_values
         self.assertEqual(
             encoded_images.shape				,			(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )				,			)


      def    a (self       :      List[str]      ):





         """simple docstring"""



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

         # Test not batched input
         __snake_case							       =   image_processor(image_inputs[0]				,			return_tensors='''pt'''      ).pixel_values
         self.assertEqual(
             encoded_images.shape				,			(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )				,			)

         # Test batched
         __snake_case							       =   image_processor(a__				,			return_tensors='''pt'''      ).pixel_values
         self.assertEqual(
             encoded_images.shape				,			(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )				,			)


      def    a (self       :      Dict      ):





         """simple docstring"""



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

         # Test not batched input
         __snake_case							       =   image_processor(image_inputs[0]				,			return_tensors='''pt'''      ).pixel_values
         self.assertEqual(
             encoded_images.shape				,			(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )				,			)

         # Test batched
         __snake_case							       =   image_processor(a__				,			return_tensors='''pt'''      ).pixel_values
         self.assertEqual(
             encoded_images.shape				,			(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )				,			)








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


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

