





'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import 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 ViltImageProcessor





class     _UpperCAmelCase							(						unittest.TestCase   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=7,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=18,__SCREAMING_SNAKE_CASE=30,__SCREAMING_SNAKE_CASE=4_00,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5],__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5],):



					'''simple docstring'''

					__lowerCAmelCase											=				size if size is not None else {"""shortest_edge""": 30}
					__lowerCAmelCase											=				parent
					__lowerCAmelCase											=				batch_size
					__lowerCAmelCase											=				num_channels
					__lowerCAmelCase											=				image_size
					__lowerCAmelCase											=				min_resolution
					__lowerCAmelCase											=				max_resolution
					__lowerCAmelCase											=				do_resize
					__lowerCAmelCase											=				size
					__lowerCAmelCase											=				size_divisor
					__lowerCAmelCase											=				do_normalize
					__lowerCAmelCase											=				image_mean
					__lowerCAmelCase											=				image_std
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}







		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False			):



					'''simple docstring'''

					if not batched:
								__lowerCAmelCase											=				self.size["""shortest_edge"""]
								__lowerCAmelCase											=				image_inputs[0]
								if isinstance(_A,Image.Image			):
											__lowerCAmelCase						,       __lowerCAmelCase											=				image.size
								else:
											__lowerCAmelCase						,       __lowerCAmelCase											=				image.shape[1], image.shape[2]
								__lowerCAmelCase											=				size / min(_A,_A			)
								if h < w:
											__lowerCAmelCase						,       __lowerCAmelCase											=				size, scale * w
								else:
											__lowerCAmelCase						,       __lowerCAmelCase											=				scale * h, size

								__lowerCAmelCase											=				int((13_33 / 8_00) * size			)
								if max(_A,_A			) > max_size:
											__lowerCAmelCase											=				max_size / max(_A,_A			)
											__lowerCAmelCase											=				newh * scale
											__lowerCAmelCase											=				neww * scale

								__lowerCAmelCase						,       __lowerCAmelCase											=				int(newh + 0.5			), int(neww + 0.5			)
								__lowerCAmelCase						,       __lowerCAmelCase											=				(
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								__lowerCAmelCase											=				[]
								for image in image_inputs:
											__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_expected_values([image]			)
											expected_values.append((expected_height, expected_width)			)
								__lowerCAmelCase											=				max(_A,key=lambda __SCREAMING_SNAKE_CASE			: item[0]			)[0]
								__lowerCAmelCase											=				max(_A,key=lambda __SCREAMING_SNAKE_CASE			: item[1]			)[1]

					return expected_height, expected_width





@require_torch
@require_vision
class     _UpperCAmelCase							(						lowerCAmelCase__	,					unittest.TestCase   ):
		a   :   Any     =ViltImageProcessor if is_vision_available() else None
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				ViltImageProcessingTester(self			)
		@property
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					return self.image_processor_tester.prepare_image_processor_dict()
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(_A,"""image_mean"""			)			)
					self.assertTrue(hasattr(_A,"""image_std"""			)			)
					self.assertTrue(hasattr(_A,"""do_normalize"""			)			)
					self.assertTrue(hasattr(_A,"""do_resize"""			)			)
					self.assertTrue(hasattr(_A,"""size"""			)			)
					self.assertTrue(hasattr(_A,"""size_divisor"""			)			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size,{"""shortest_edge""": 30}			)

					__lowerCAmelCase											=				self.image_processing_class.from_dict(self.image_processor_dict,size=42			)
					self.assertEqual(image_processor.size,{"""shortest_edge""": 42}			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					pass
		def   lowerCamelCase__			(  self			):



					'''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=_A			)
					for image in image_inputs:
								self.assertIsInstance(_A,Image.Image			)

					# Test not batched input
					__lowerCAmelCase											=				image_processing(image_inputs[0],return_tensors="""pt"""			).pixel_values

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.image_processor_tester.get_expected_values(_A			)
					self.assertEqual(
					    encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

					# Test batched
					__lowerCAmelCase											=				image_processing(_A,return_tensors="""pt"""			).pixel_values

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.image_processor_tester.get_expected_values(_A,batched=_A			)
					self.assertEqual(
					    encoded_images.shape,(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),)
		def   lowerCamelCase__			(  self			):



					'''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=_A,numpify=_A			)
					for image in image_inputs:
								self.assertIsInstance(_A,np.ndarray			)

					# Test not batched input
					__lowerCAmelCase											=				image_processing(image_inputs[0],return_tensors="""pt"""			).pixel_values

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.image_processor_tester.get_expected_values(_A			)
					self.assertEqual(
					    encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

					# Test batched
					__lowerCAmelCase											=				image_processing(_A,return_tensors="""pt"""			).pixel_values

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.image_processor_tester.get_expected_values(_A,batched=_A			)
					self.assertEqual(
					    encoded_images.shape,(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),)







		def   lowerCamelCase__			(  self			):



					'''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=_A,torchify=_A			)
					for image in image_inputs:
								self.assertIsInstance(_A,torch.Tensor			)

					# Test not batched input
					__lowerCAmelCase											=				image_processing(image_inputs[0],return_tensors="""pt"""			).pixel_values

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.image_processor_tester.get_expected_values(_A			)
					self.assertEqual(
					    encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

					# Test batched
					__lowerCAmelCase											=				image_processing(_A,return_tensors="""pt"""			).pixel_values

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.image_processor_tester.get_expected_values(_A,batched=_A			)
					self.assertEqual(
					    encoded_images.shape,(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__     :					Dict								=			{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[int]								=			[
           "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DonutSwinModel",
           "DonutSwinPreTrainedModel",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					List[Any]								=			["DonutFeatureExtractor"]
       UpperCamelCase__     :					Dict								=			["DonutImageProcessor"]


if TYPE_CHECKING:
       from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
       from .processing_donut import DonutProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_donut_swin import (
                  DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DonutSwinModel,
                  DonutSwinPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_donut import DonutFeatureExtractor
              from .image_processing_donut import DonutImageProcessor

else:
       import sys

       UpperCamelCase__     :					Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


