


'''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 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Dict=7 ,  lowerCAmelCase							:      Dict=3 ,  lowerCAmelCase							:      Tuple=18 ,  lowerCAmelCase							:      Any=30 ,  lowerCAmelCase							:      str=4_00 ,  lowerCAmelCase							:      List[str]=True ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      List[str]=True ,  lowerCAmelCase							:      Optional[int]=False ,  lowerCAmelCase							:      str=True ,  lowerCAmelCase							:      List[str]=True ,  lowerCAmelCase							:      Tuple=[0.5, 0.5, 0.5] ,  lowerCAmelCase							:      List[str]=[0.5, 0.5, 0.5] ,  ):
													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							:      Optional[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   (   lowercase				,			unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						=DonutImageProcessor if is_vision_available() else None






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		DonutImageProcessingTester(self	)






						@property
						def 				_UpperCAmelCase       (  self							:      Any	):
													return self.image_processor_tester.prepare_image_processor_dict()






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													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							:      int	):
													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[Any]	):
													pass






						@is_flaky()
						def 				_UpperCAmelCase       (  self							:      Dict	):
													# 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							:      Optional[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							:      Union[str, Any]	):
													# 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"],
													    ) ,  )









'''simple docstring'''
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE					:				List[str]									=		{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





