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 		_lowerCamelCase		( unittest.TestCase     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=7			,			lowerCAmelCase=3			,			lowerCAmelCase=18			,			lowerCAmelCase=30			,			lowerCAmelCase=400			,			lowerCAmelCase=True			,			lowerCAmelCase=None			,			lowerCAmelCase=True			,			lowerCAmelCase=False			,			lowerCAmelCase=True			,			lowerCAmelCase=True			,			lowerCAmelCase=[0.5, 0.5, 0.5]			,			lowerCAmelCase=[0.5, 0.5, 0.5]			,			)							->       Dict:
     SCREAMING_SNAKE_CASE__: Dict=   parent
     SCREAMING_SNAKE_CASE__: List[Any]=   batch_size
     SCREAMING_SNAKE_CASE__: Tuple=   num_channels
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   image_size
     SCREAMING_SNAKE_CASE__: List[str]=   min_resolution
     SCREAMING_SNAKE_CASE__: Dict=   max_resolution
     SCREAMING_SNAKE_CASE__: List[str]=   do_resize
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   size if size is not None else {'''height''': 18, '''width''': 20}
     SCREAMING_SNAKE_CASE__: Any=   do_thumbnail
     SCREAMING_SNAKE_CASE__: Dict=   do_align_axis
     SCREAMING_SNAKE_CASE__: List[str]=   do_pad
     SCREAMING_SNAKE_CASE__: Optional[int]=   do_normalize
     SCREAMING_SNAKE_CASE__: int=   image_mean
     SCREAMING_SNAKE_CASE__: Optional[Any]=   image_std



    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     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 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   DonutImageProcessor if is_vision_available() else None
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Tuple=   DonutImageProcessingTester(self )
    @property
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: List[str]=   self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_resize''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''size''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_thumbnail''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_align_long_axis''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_pad''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_normalize''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''image_mean''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''image_std''' ) )
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Dict=   self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size			,			{'''height''': 18, '''width''': 20} )

     SCREAMING_SNAKE_CASE__: Any=   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
     SCREAMING_SNAKE_CASE__: int=   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 )							->       List[str]:
     pass
    @is_flaky()
    def     UpperCamelCase_					(	self )							->       List[str]:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: Dict=   self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     SCREAMING_SNAKE_CASE__: Any=   prepare_image_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase			,			Image.Image )

     # Test not batched input
     SCREAMING_SNAKE_CASE__: List[str]=   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
     SCREAMING_SNAKE_CASE__: Optional[Any]=   image_processing(lowerCAmelCase			,			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 )							->       str:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__: Dict=   prepare_image_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase			,			numpify=lowerCAmelCase )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase			,			np.ndarray )

     # Test not batched input
     SCREAMING_SNAKE_CASE__: Optional[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
     SCREAMING_SNAKE_CASE__: Any=   image_processing(lowerCAmelCase			,			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:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: Any=   self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__: Optional[int]=   prepare_image_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase			,			torchify=lowerCAmelCase )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase			,			torch.Tensor )

     # Test not batched input
     SCREAMING_SNAKE_CASE__: 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
     SCREAMING_SNAKE_CASE__: Tuple=   image_processing(lowerCAmelCase			,			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'''],
         )			,			)



"""simple docstring"""


def  lowercase						(	lowerCAmelCase__						):
			return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



