


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=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], )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						parent
           _lowercase			:				Union[str, Any] 						=						batch_size
           _lowercase			:				List[Any] 						=						num_channels
           _lowercase			:				str 						=						image_size
           _lowercase			:				str 						=						min_resolution
           _lowercase			:				str 						=						max_resolution
           _lowercase			:				int 						=						do_resize
           _lowercase			:				Dict 						=						size if size is not None else {'height': 18, 'width': 20}
           _lowercase			:				Optional[int] 						=						do_thumbnail
           _lowercase			:				Optional[Any] 						=						do_align_axis
           _lowercase			:				Optional[Any] 						=						do_pad
           _lowercase			:				str 						=						do_normalize
           _lowercase			:				Any 						=						image_mean
           _lowercase			:				Tuple 						=						image_std




       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """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 _lowerCamelCase(		_a,					unittest.TestCase  ):
       lowercase_					:      List[str]       					=     DonutImageProcessor if is_vision_available() else None
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				int 						=						DonutImageProcessingTester(self)
       @property
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           return self.image_processor_tester.prepare_image_processor_dict()
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Dict 						=						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)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size, {'height': 18, 'width': 20})

           _lowercase			:				str 						=						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
           _lowercase			:				List[str] 						=						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)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @is_flaky()
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Any 						=						self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           _lowercase			:				int 						=						prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase)
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase, Image.Image)

           # Test not batched input
           _lowercase			:				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
           _lowercase			:				Optional[int] 						=						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)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Dict 						=						self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           _lowercase			:				Optional[Any] 						=						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
           _lowercase			:				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
           _lowercase			:				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)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           _lowercase			:				Any 						=						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
           _lowercase			:				List[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[int] 						=						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'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase   :							Dict         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["pixel_values"]




      def __init__(	self			, lowerCamelCase = True			, lowerCamelCase = 32			, lowerCamelCase=PILImageResampling.BILINEAR			, lowerCamelCase = True			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         __a					  =     do_resize
         __a					  =     do_rescale
         __a					  =     size_divisor
         __a					  =     resample
         super().__init__(**lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         __a  ,      __a					  =     get_image_size(lowerCamelCase   )
         # Rounds the height and width down to the closest multiple of size_divisor
         __a					  =     height // size_divisor * size_divisor
         __a					  =     width // size_divisor * size_divisor
         __a					  =     resize(lowerCamelCase			, (new_h, new_w)			, resample=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )
         return image




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         return rescale(image=lowerCamelCase			, scale=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase=None			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = ChannelDimension.FIRST			, **lowerCamelCase			, )				->BatchFeature:



         '''simple docstring'''
         __a					  =     do_resize if do_resize is not None else self.do_resize
         __a					  =     do_rescale if do_rescale is not None else self.do_rescale
         __a					  =     size_divisor if size_divisor is not None else self.size_divisor
         __a					  =     resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
            raise ValueError('size_divisor is required for resizing'   )

         __a					  =     make_list_of_images(lowerCamelCase   )

         if not valid_images(lowerCamelCase   ):
            raise ValueError('Invalid image(s)'   )

         # All transformations expect numpy arrays.
         __a					  =     [to_numpy_array(lowerCamelCase   ) for img in images]

         if do_resize:
            __a					  =     [self.resize(lowerCamelCase			, size_divisor=lowerCamelCase			, resample=lowerCamelCase   ) for image in images]

         if do_rescale:
            __a					  =     [self.rescale(lowerCamelCase			, scale=1 / 255   ) for image in images]

         __a					  =     [to_channel_dimension_format(lowerCamelCase			, lowerCamelCase   ) for image in images]

         __a					  =     {'pixel_values': images}
         return BatchFeature(data=lowerCamelCase			, tensor_type=lowerCamelCase   )