
"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


A__ :       Union[str, Any]				= logging.get_logger(__name__)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		['''pixel_values''']
       def __init__(  self			,					A_ = True			,					A_ = None			,					A_ = PILImageResampling.BICUBIC			,					A_ = True			,					A_ = 1 / 255			,					A_ = True			,					A_ = None			,					A_ = None			,					A_ = True			,					**A_			,					)  ->						None:





         """simple docstring"""




         super().__init__(**A_							)
         _lowercase:      str   			=  size if size is not None else {'''height''': 384, '''width''': 384}
         _lowercase:      List[str]   			=  get_size_dict(A_			,					default_to_square=A_							)

         _lowercase:      List[Any]   			=  do_resize
         _lowercase:      Tuple   			=  size
         _lowercase:      Tuple   			=  resample
         _lowercase:      List[Any]   			=  do_rescale
         _lowercase:      str   			=  rescale_factor
         _lowercase:      Dict   			=  do_normalize
         _lowercase:      int   			=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _lowercase:      Any   			=  image_std if image_std is not None else OPENAI_CLIP_STD
         _lowercase:      List[str]   			=  do_convert_rgb
       def 				lowercase_				(  self			,					A_			,					A_			,					A_ = PILImageResampling.BICUBIC			,					A_ = None			,					**A_			,					)  ->						np.ndarray:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  get_size_dict(A_			,					default_to_square=A_							)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''							)
         _lowercase:      List[Any]   			=  (size['''height'''], size['''width'''])
         return resize(A_			,					size=A_			,					resample=A_			,					data_format=A_			,					**A_							)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_ = None			,					**A_			,					)  ->						List[Any]:





         """simple docstring"""




         return rescale(A_			,					scale=A_			,					data_format=A_			,					**A_							)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_ = None			,					**A_			,					)  ->						np.ndarray:





         """simple docstring"""




         return normalize(A_			,					mean=A_			,					std=A_			,					data_format=A_			,					**A_							)



       def 				lowercase_				(  self			,					A_			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = ChannelDimension.FIRST			,					**A_			,					)  ->						PIL.Image.Image:





         """simple docstring"""




         _lowercase:      Any   			=  do_resize if do_resize is not None else self.do_resize
         _lowercase:      int   			=  resample if resample is not None else self.resample
         _lowercase:      str   			=  do_rescale if do_rescale is not None else self.do_rescale
         _lowercase:      List[Any]   			=  rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase:      int   			=  do_normalize if do_normalize is not None else self.do_normalize
         _lowercase:      Tuple   			=  image_mean if image_mean is not None else self.image_mean
         _lowercase:      List[Any]   			=  image_std if image_std is not None else self.image_std
         _lowercase:      List[Any]   			=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _lowercase:      Tuple   			=  size if size is not None else self.size
         _lowercase:      Tuple   			=  get_size_dict(A_			,					default_to_square=A_							)

         _lowercase:      Union[str, Any]   			=  make_list_of_images(A_							)

         if not valid_images(A_							):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

         if do_resize and size is None or resample is None:
           raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

         if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
           _lowercase:      int   			=  [convert_to_rgb(A_							) for image in images]

         # All transformations expect numpy arrays.
         _lowercase:      Tuple   			=  [to_numpy_array(A_							) for image in images]

         if do_resize:
           _lowercase:      Union[str, Any]   			=  [self.resize(image=A_			,					size=A_			,					resample=A_							) for image in images]

         if do_rescale:
           _lowercase:      str   			=  [self.rescale(image=A_			,					scale=A_							) for image in images]

         if do_normalize:
           _lowercase:      Union[str, Any]   			=  [self.normalize(image=A_			,					mean=A_			,					std=A_							) for image in images]

         _lowercase:      List[str]   			=  [to_channel_dimension_format(A_			,					A_							) for image in images]

         _lowercase:      Dict   			=  BatchFeature(data={'''pixel_values''': images}			,					tensor_type=A_							)

         return encoded_outputs


"""simple docstring"""




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       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=3			,					A_=18			,					A_=30			,					A_=400			,					A_=True			,					A_=None			,					A_=True			,					A_=False			,					A_=True			,					A_=True			,					A_=[0.5, 0.5, 0.5]			,					A_=[0.5, 0.5, 0.5]			,					)  ->						Optional[int]:





         """simple docstring"""




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



       def 				lowercase_				(  self							)  ->						List[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       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		DonutImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      str   			=  DonutImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(A_			,					'''do_resize'''							)							)
         self.assertTrue(hasattr(A_			,					'''size'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_thumbnail'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_align_long_axis'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_pad'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_normalize'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_mean'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_std'''							)							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




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

         _lowercase:      List[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:      Any   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=(42, 84)							)
         self.assertEqual(image_processor.size			,					{'''height''': 84, '''width''': 42}							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         pass
       @is_flaky()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




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

         # 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:      List[Any]   			=  image_processing(A_			,					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 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      str   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					numpify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					np.ndarray							)

         # Test not batched input
         _lowercase:      Union[str, 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:      Dict   			=  image_processing(A_			,					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 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      List[Any]   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					torchify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					torch.Tensor							)

         # Test not batched input
         _lowercase:      Union[str, 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:      Dict   			=  image_processing(A_			,					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'''],
             )			,					)
