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     _SCREAMING_SNAKE_CASE       ( 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=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       _A							(  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       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=False  ):


      '''simple docstring'''

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

         __lowerCamelCase								=				int((1333 / 800) * size  )
         if max(UpperCAmelCase_   ,							UpperCAmelCase_  ) > max_size:
            __lowerCamelCase								=				max_size / max(UpperCAmelCase_   ,							UpperCAmelCase_  )
            __lowerCamelCase								=				newh * scale
            __lowerCamelCase								=				neww * scale

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

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

      return expected_height, expected_width






@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase						,							unittest.TestCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Optional[Any]						=						ViltImageProcessor if is_vision_available() else None







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				ViltImageProcessingTester(self  )







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(UpperCAmelCase_   ,							'image_mean'  )  )
      self.assertTrue(hasattr(UpperCAmelCase_   ,							'image_std'  )  )
      self.assertTrue(hasattr(UpperCAmelCase_   ,							'do_normalize'  )  )
      self.assertTrue(hasattr(UpperCAmelCase_   ,							'do_resize'  )  )
      self.assertTrue(hasattr(UpperCAmelCase_   ,							'size'  )  )
      self.assertTrue(hasattr(UpperCAmelCase_   ,							'size_divisor'  )  )







   def       _A							(  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       _A							(  self  ):


      '''simple docstring'''

      pass







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

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

      __lowerCamelCase								=				self.image_processor_tester.get_expected_values(UpperCAmelCase_  )
      self.assertEqual(
          encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

      # Test batched
      __lowerCamelCase								=				image_processing(UpperCAmelCase_   ,							return_tensors='pt'  ).pixel_values

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







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

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

      __lowerCamelCase								=				self.image_processor_tester.get_expected_values(UpperCAmelCase_  )
      self.assertEqual(
          encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

      # Test batched
      __lowerCamelCase								=				image_processing(UpperCAmelCase_   ,							return_tensors='pt'  ).pixel_values

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







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

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

      __lowerCamelCase								=				self.image_processor_tester.get_expected_values(UpperCAmelCase_  )
      self.assertEqual(
          encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

      # Test batched
      __lowerCamelCase								=				image_processing(UpperCAmelCase_   ,							return_tensors='pt'  ).pixel_values

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







"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :		List[Any]        =      {
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Optional[Any]        =      ['''DeformableDetrFeatureExtractor''']
     _SCREAMING_SNAKE_CASE  :		List[str]        =      ['''DeformableDetrImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      [
         '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DeformableDetrForObjectDetection''',
         '''DeformableDetrModel''',
         '''DeformableDetrPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
          from .image_processing_deformable_detr import DeformableDetrImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deformable_detr import (
              DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DeformableDetrForObjectDetection,
              DeformableDetrModel,
              DeformableDetrPreTrainedModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE  :		Tuple        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






