
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   __lowerCAmelCase		(		unittest.TestCase ):



      """simple docstring"""







      def __init__( self						:    Optional[Any]			,    _snake_case						:    Optional[int]			,    _snake_case						:    List[Any]=7			,    _snake_case						:    int=3			,    _snake_case						:    List[Any]=18			,    _snake_case						:    Union[str, Any]=30			,    _snake_case						:    Dict=400			,    _snake_case						:    Optional[int]=True			,    _snake_case						:    Union[str, Any]=None			,    _snake_case						:    int=True			,    _snake_case						:    int=[0.5, 0.5, 0.5]			,    _snake_case						:    Optional[int]=[0.5, 0.5, 0.5]			,    ):
          __lowercase						:     Tuple		      =    size if size is not None else {'''height''': 18, '''width''': 18}
          __lowercase						:     Tuple		      =    parent
          __lowercase						:     Optional[Any]		      =    batch_size
          __lowercase						:     Optional[int]		      =    num_channels
          __lowercase						:     List[Any]		      =    image_size
          __lowercase						:     List[Any]		      =    min_resolution
          __lowercase						:     Dict		      =    max_resolution
          __lowercase						:     int		      =    do_resize
          __lowercase						:     Union[str, Any]		      =    size
          __lowercase						:     Optional[Any]		      =    do_normalize
          __lowercase						:     Optional[Any]		      =    image_mean
          __lowercase						:     str		      =    image_std




      def 						snake_case_     ( self						:    int			):
          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   __lowerCAmelCase		(		lowerCAmelCase_	,	unittest.TestCase ):



      """simple docstring"""







      A__		:     int       				=      ViTImageProcessor if is_vision_available() else None




      def 						snake_case_     ( self						:    Tuple			):
          __lowercase						:     Union[str, Any]		      =    ViTImageProcessingTester(self			)




      @property
      def 						snake_case_     ( self						:    str			):
          return self.image_processor_tester.prepare_image_processor_dict()




      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     int		      =    self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(_snake_case			,    '''image_mean'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''image_std'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_normalize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_resize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''size'''			)			)




      def 						snake_case_     ( self						:    List[str]			):
          __lowercase						:     int		      =    self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size			,    {'''height''': 18, '''width''': 18}			)

          __lowercase						:     str		      =    self.image_processing_class.from_dict(self.image_processor_dict			,    size=42			)
          self.assertEqual(image_processor.size			,    {'''height''': 42, '''width''': 42}			)




      def 						snake_case_     ( self						:    Tuple			):
          pass




      def 						snake_case_     ( self						:    int			):
          # Initialize image_processing
          __lowercase						:     Dict		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          __lowercase						:     Any		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    Image.Image			)

          # Test not batched input
          __lowercase						:     int		      =    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )

          # Test batched
          __lowercase						:     List[str]		      =    image_processing(_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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )




      def 						snake_case_     ( self						:    Optional[Any]			):
          # Initialize image_processing
          __lowercase						:     List[str]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          __lowercase						:     int		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    numpify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    np.ndarray			)

          # Test not batched input
          __lowercase						:     Tuple		      =    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )

          # Test batched
          __lowercase						:     Optional[int]		      =    image_processing(_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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )




      def 						snake_case_     ( self						:    List[Any]			):
          # Initialize image_processing
          __lowercase						:     List[Any]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          __lowercase						:     int		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    torchify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    torch.Tensor			)

          # Test not batched input
          __lowercase						:     Any		      =    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )

          # Test batched
          __lowercase						:     Optional[Any]		      =    image_processing(_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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )




__lowerCAmelCase   :     List[Any]      =						"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase   :     Optional[Any]      =						[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase   :     List[str]      =						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


