



"""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       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			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__=400						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]						,	SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]						,	)      ->	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   snake_case__	(      self		)      ->	Union[str, 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       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     DonutImageProcessor if is_vision_available() else None


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					DonutImageProcessingTester(self		)


			@property
			def   snake_case__	(      self		)      ->	List[str]:
				return self.image_processor_tester.prepare_image_processor_dict()


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_resize"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"size"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_thumbnail"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_align_long_axis"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_pad"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_normalize"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"image_mean"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"image_std"		)		)


			def   snake_case__	(      self		)      ->	Dict:
				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   snake_case__	(      self		)      ->	Any:
				pass


			@is_flaky()
			def   snake_case__	(      self		)      ->	Tuple:
				# 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=SCREAMING_SNAKE_CASE__		)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	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(SCREAMING_SNAKE_CASE__						,	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   snake_case__	(      self		)      ->	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=SCREAMING_SNAKE_CASE__						,	numpify=SCREAMING_SNAKE_CASE__		)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	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(SCREAMING_SNAKE_CASE__						,	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   snake_case__	(      self		)      ->	Dict:
				# 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=SCREAMING_SNAKE_CASE__						,	torchify=SCREAMING_SNAKE_CASE__		)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	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(SCREAMING_SNAKE_CASE__						,	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


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCAmelCase     :  Optional[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

