
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 ConvNextImageProcessor





class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=7 ,     _A=3 ,     _A=18 ,     _A=30 ,     _A=400 ,     _A=True ,     _A=None ,     _A=0.875 ,     _A=True ,     _A=[0.5, 0.5, 0.5] ,     _A=[0.5, 0.5, 0.5] ,     )				->				Any:
          SCREAMING_SNAKE_CASE_	=      size if size is not None else {'''shortest_edge''': 20}
          SCREAMING_SNAKE_CASE_	=      parent
          SCREAMING_SNAKE_CASE_	=      batch_size
          SCREAMING_SNAKE_CASE_	=      num_channels
          SCREAMING_SNAKE_CASE_	=      image_size
          SCREAMING_SNAKE_CASE_	=      min_resolution
          SCREAMING_SNAKE_CASE_	=      max_resolution
          SCREAMING_SNAKE_CASE_	=      do_resize
          SCREAMING_SNAKE_CASE_	=      size
          SCREAMING_SNAKE_CASE_	=      crop_pct
          SCREAMING_SNAKE_CASE_	=      do_normalize
          SCREAMING_SNAKE_CASE_	=      image_mean
          SCREAMING_SNAKE_CASE_	=      image_std




       def     _UpperCamelCase	(							self	)				->				Any:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "crop_pct": self.crop_pct,
          }







@require_torch
@require_vision
class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE       ,    unittest.TestCase	):






       """simple docstring"""


       UpperCAmelCase_				       =ConvNextImageProcessor if is_vision_available() else None



       def     _UpperCamelCase	(							self	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      ConvNextImageProcessingTester(self	)



       @property
       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(_A ,     '''do_resize'''	)	)
          self.assertTrue(hasattr(_A ,     '''size'''	)	)
          self.assertTrue(hasattr(_A ,     '''crop_pct'''	)	)
          self.assertTrue(hasattr(_A ,     '''do_normalize'''	)	)
          self.assertTrue(hasattr(_A ,     '''image_mean'''	)	)
          self.assertTrue(hasattr(_A ,     '''image_std'''	)	)



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class.from_dict(self.image_processor_dict	)
          self.assertEqual(image_processor.size ,     {'''shortest_edge''': 20}	)

          SCREAMING_SNAKE_CASE_	=      self.image_processing_class.from_dict(self.image_processor_dict ,     size=42	)
          self.assertEqual(image_processor.size ,     {'''shortest_edge''': 42}	)



       def     _UpperCamelCase	(							self	)				->				Tuple:
          pass



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          SCREAMING_SNAKE_CASE_	=      prepare_image_inputs(self.image_processor_tester ,     equal_resolution=_A	)
          for image in image_inputs:
             self.assertIsInstance(_A ,     Image.Image	)

          # Test not batched input
          SCREAMING_SNAKE_CASE_	=      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['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              ) ,     )

          # Test batched
          SCREAMING_SNAKE_CASE_	=      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['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              ) ,     )



       def     _UpperCamelCase	(							self	)				->				Dict:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          SCREAMING_SNAKE_CASE_	=      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
          SCREAMING_SNAKE_CASE_	=      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['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              ) ,     )

          # Test batched
          SCREAMING_SNAKE_CASE_	=      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['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              ) ,     )




       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          SCREAMING_SNAKE_CASE_	=      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
          SCREAMING_SNAKE_CASE_	=      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['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              ) ,     )

          # Test batched
          SCREAMING_SNAKE_CASE_	=      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['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              ) ,     )




from typing import TYPE_CHECKING

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


__UpperCAmelCase   						=     {
    "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   						=     [
       "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DonutSwinModel",
       "DonutSwinPreTrainedModel",
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     ["DonutFeatureExtractor"]
   __UpperCAmelCase   						=     ["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   						=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


