_SCREAMING_SNAKE_CASE										=						{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


_SCREAMING_SNAKE_CASE										=						{value: key for key, value in encode_dict.items()}






def  snake_case  (       snake_case__			:str)						->   str:
       _A        =  """"""
       for letter in word.lower():
              if letter.isalpha() or letter == " ":
                     encoded += encode_dict[letter]
              else:
                     raise Exception("""encode() accepts only letters of the alphabet and spaces""")
       return encoded






def  snake_case  (       snake_case__			:str)						->   str:
       if set(snake_case__) - {"A", "B", " "} != set():
              raise Exception("""decode() accepts only 'A', 'B' and spaces""")
       _A        =  """"""
       for word in coded.split():
              while len(snake_case__) != 0:
                     decoded += decode_dict[word[:5]]
                     _A        =  word[5:]
              decoded += " "
       return decoded.strip()


if __name__ == "__main__":
    from doctest import testmod

    testmod()





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       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=7		,				lowerCAmelCase_=3		,				lowerCAmelCase_=18		,				lowerCAmelCase_=30		,				lowerCAmelCase_=4_00		,				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]		,				)  ->					Optional[Any]:
        _A        =  parent
        _A        =  batch_size
        _A        =  num_channels
        _A        =  image_size
        _A        =  min_resolution
        _A        =  max_resolution
        _A        =  do_resize
        _A        =  size if size is not None else {"""height""": 18, """width""": 20}
        _A        =  do_thumbnail
        _A        =  do_align_axis
        _A        =  do_pad
        _A        =  do_normalize
        _A        =  image_mean
        _A        =  image_std



 def      UpperCAmelCase							(					self							)  ->					str:
        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							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		DonutImageProcessor if is_vision_available() else None



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  DonutImageProcessingTester(self							)



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  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:
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,				{"""height""": 18, """width""": 20}							)

        _A        =  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
        _A        =  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							)  ->					Optional[int]:
        pass



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				Image.Image							)

        # Test not batched input
        _A        =  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
        _A        =  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							)  ->					Union[str, Any]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        _A        =  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
        _A        =  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
        _A        =  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							)  ->					int:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        _A        =  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
        _A        =  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
        _A        =  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"""],
            )		,				)




