
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor

class       a		(   unittest.TestCase					):







       def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  Tuple      ,				snake_case__  :  Any=7      ,				snake_case__  :  Optional[int]=3      ,				snake_case__  :  List[Any]=18      ,				snake_case__  :  Tuple=30      ,				snake_case__  :  int=400      ,				snake_case__  :  Any=True      ,				snake_case__  :  Dict=None      ,				snake_case__  :  int=True      ,				snake_case__  :  Any=False      ,				snake_case__  :  int=True      ,				snake_case__  :  Optional[int]=True      ,				snake_case__  :  Dict=[0.5, 0.5, 0.5]      ,				snake_case__  :  int=[0.5, 0.5, 0.5]      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							parent
        __lowerCAmelCase         =							batch_size
        __lowerCAmelCase         =							num_channels
        __lowerCAmelCase         =							image_size
        __lowerCAmelCase         =							min_resolution
        __lowerCAmelCase         =							max_resolution
        __lowerCAmelCase         =							do_resize
        __lowerCAmelCase         =							size if size is not None else {"height": 18, "width": 20}
        __lowerCAmelCase         =							do_thumbnail
        __lowerCAmelCase         =							do_align_axis
        __lowerCAmelCase         =							do_pad
        __lowerCAmelCase         =							do_normalize
        __lowerCAmelCase         =							image_mean
        __lowerCAmelCase         =							image_std







       def       UpperCAmelCase__					(						self  :  str ):
        """simple docstring"""







        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }

@require_torch
@require_vision
class       a		(   __UpperCAmelCase      ,	unittest.TestCase					):
       lowercase_		:						int   							=  DonutImageProcessor if is_vision_available() else None







       def       UpperCAmelCase__					(						self  :  int ):
        """simple docstring"""







        __lowerCAmelCase         =							DonutImageProcessingTester(self )







       @property
       def       UpperCAmelCase__					(						self  :  int ):
        """simple docstring"""







        return self.image_processor_tester.prepare_image_processor_dict()







       def       UpperCAmelCase__					(						self  :  Tuple ):
        """simple docstring"""







        __lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(snake_case__      ,				"do_resize" ) )
        self.assertTrue(hasattr(snake_case__      ,				"size" ) )
        self.assertTrue(hasattr(snake_case__      ,				"do_thumbnail" ) )
        self.assertTrue(hasattr(snake_case__      ,				"do_align_long_axis" ) )
        self.assertTrue(hasattr(snake_case__      ,				"do_pad" ) )
        self.assertTrue(hasattr(snake_case__      ,				"do_normalize" ) )
        self.assertTrue(hasattr(snake_case__      ,				"image_mean" ) )
        self.assertTrue(hasattr(snake_case__      ,				"image_std" ) )







       def       UpperCAmelCase__					(						self  :  List[Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							self.image_processing_class.from_dict(self.image_processor_dict )
        self.assertEqual(image_processor.size      ,				{"height": 18, "width": 20} )

        __lowerCAmelCase         =							self.image_processing_class.from_dict(self.image_processor_dict      ,				size=42 )
        self.assertEqual(image_processor.size      ,				{"height": 42, "width": 42} )

        # Previous config had dimensions in (width, height) order
        __lowerCAmelCase         =							self.image_processing_class.from_dict(self.image_processor_dict      ,				size=(42, 84) )
        self.assertEqual(image_processor.size      ,				{"height": 84, "width": 42} )







       def       UpperCAmelCase__					(						self  :  Tuple ):
        """simple docstring"""







        pass







       @is_flaky()
       def       UpperCAmelCase__					(						self  :  Tuple ):
        """simple docstring"""







        __lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        __lowerCAmelCase         =							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
        __lowerCAmelCase         =							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
        __lowerCAmelCase         =							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"],
            )      ,				)







       @is_flaky()
       def       UpperCAmelCase__					(						self  :  Dict ):
        """simple docstring"""







        __lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        __lowerCAmelCase         =							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
        __lowerCAmelCase         =							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
        __lowerCAmelCase         =							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"],
            )      ,				)







       @is_flaky()
       def       UpperCAmelCase__					(						self  :  Optional[int] ):
        """simple docstring"""







        __lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        __lowerCAmelCase         =							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
        __lowerCAmelCase         =							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
        __lowerCAmelCase         =							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"],
            )      ,				)








from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       a		(   __UpperCAmelCase					):







							@staticmethod
							@abstractmethod
							def       UpperCAmelCase__					(						snake_case__  :  ArgumentParser ):
								"""simple docstring"""







								raise NotImplementedError()







							@abstractmethod
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								raise NotImplementedError()






