
'''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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SwinaSRImageProcessor
					from transformers.image_transforms import get_image_size


class   a_  (unittest.TestCase    ):




							def __init__(     self   ,   snake_case_   ,   snake_case_=7   ,   snake_case_=3   ,   snake_case_=1_8   ,   snake_case_=3_0   ,   snake_case_=4_0_0   ,   snake_case_=True   ,   snake_case_=1 / 2_5_5   ,   snake_case_=True   ,   snake_case_=8   ,   ):
									_lowerCAmelCase       :  int   				=					parent
									_lowerCAmelCase       :  Any   				=					batch_size
									_lowerCAmelCase       :  List[str]   				=					num_channels
									_lowerCAmelCase       :  Any   				=					image_size
									_lowerCAmelCase       :  Optional[Any]   				=					min_resolution
									_lowerCAmelCase       :  Optional[int]   				=					max_resolution
									_lowerCAmelCase       :  Union[str, Any]   				=					do_rescale
									_lowerCAmelCase       :  Optional[int]   				=					rescale_factor
									_lowerCAmelCase       :  Optional[Any]   				=					do_pad
									_lowerCAmelCase       :  List[Any]   				=					pad_size




							def 							__UpperCamelCase       (     self  ):
									return {
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									    "pad_size": self.pad_size,
									}







							def 							__UpperCamelCase       (     self   ,   snake_case_=False   ,   snake_case_=False   ,   snake_case_=False  ):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
											_lowerCAmelCase       :  int   				=					[]
											for i in range(self.batch_size  ):
													image_inputs.append(
													    np.random.randint(
													        2_5_5   ,   size=(self.num_channels, self.max_resolution, self.max_resolution)   ,   dtype=np.uinta  )  )
									else:
											_lowerCAmelCase       :  List[str]   				=					[]
											for i in range(self.batch_size  ):
													_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[int]   				=					np.random.choice(np.arange(self.min_resolution   ,   self.max_resolution  )   ,   2  )
													image_inputs.append(np.random.randint(2_5_5   ,   size=(self.num_channels, width, height)   ,   dtype=np.uinta  )  )

									if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											_lowerCAmelCase       :  List[str]   				=					[Image.fromarray(np.moveaxis(snake_case_   ,   0   ,   -1  )  ) for x in image_inputs]

									if torchify:
											_lowerCAmelCase       :  str   				=					[torch.from_numpy(snake_case_  ) for x in image_inputs]

									return image_inputs



@require_torch
@require_vision
class   a_  (_a							,			unittest.TestCase    ):
							__lowerCAmelCase    :				List[Any]    =      SwinaSRImageProcessor if is_vision_available() else None




							def 							__UpperCamelCase       (     self  ):
									_lowerCAmelCase       :  int   				=					SwinaSRImageProcessingTester(self  )




							@property
							def 							__UpperCamelCase       (     self  ):
									return self.image_processor_tester.prepare_image_processor_dict()




							def 							__UpperCamelCase       (     self  ):
									_lowerCAmelCase       :  Dict   				=					self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(snake_case_   ,   """do_rescale"""  )  )
									self.assertTrue(hasattr(snake_case_   ,   """rescale_factor"""  )  )
									self.assertTrue(hasattr(snake_case_   ,   """do_pad"""  )  )
									self.assertTrue(hasattr(snake_case_   ,   """pad_size"""  )  )




							def 							__UpperCamelCase       (     self  ):
									pass




							def 							__UpperCamelCase       (     self   ,   snake_case_  ):
									_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[Any]   				=					get_image_size(snake_case_  )
									_lowerCAmelCase       :  Tuple   				=					self.image_processor_tester.pad_size

									_lowerCAmelCase       :  Any   				=					(old_height // size + 1) * size - old_height
									_lowerCAmelCase       :  List[Any]   				=					(old_width // size + 1) * size - old_width
									return old_height + pad_height, old_width + pad_width




							def 							__UpperCamelCase       (     self  ):
									# Initialize image_processor
									_lowerCAmelCase       :  Optional[int]   				=					self.image_processing_class(**self.image_processor_dict  )
									# create random PIL images
									_lowerCAmelCase       :  Dict   				=					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_  )
									for image in image_inputs:
											self.assertIsInstance(snake_case_   ,   Image.Image  )

									# Test not batched input
									_lowerCAmelCase       :  int   				=					image_processor(image_inputs[0]   ,   return_tensors="""pt"""  ).pixel_values
									_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[int]   				=					self.calculate_expected_size(np.array(image_inputs[0]  )  )
									self.assertEqual(
									    encoded_images.shape   ,   (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,   )




							def 							__UpperCamelCase       (     self  ):
									# Initialize image_processor
									_lowerCAmelCase       :  str   				=					self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									_lowerCAmelCase       :  Union[str, Any]   				=					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_   ,   numpify=snake_case_  )
									for image in image_inputs:
											self.assertIsInstance(snake_case_   ,   np.ndarray  )

									# Test not batched input
									_lowerCAmelCase       :  Union[str, Any]   				=					image_processor(image_inputs[0]   ,   return_tensors="""pt"""  ).pixel_values
									_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[Any]   				=					self.calculate_expected_size(image_inputs[0]  )
									self.assertEqual(
									    encoded_images.shape   ,   (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,   )







							def 							__UpperCamelCase       (     self  ):
									# Initialize image_processor
									_lowerCAmelCase       :  Tuple   				=					self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									_lowerCAmelCase       :  str   				=					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_   ,   torchify=snake_case_  )
									for image in image_inputs:
											self.assertIsInstance(snake_case_   ,   torch.Tensor  )

									# Test not batched input
									_lowerCAmelCase       :  Tuple   				=					image_processor(image_inputs[0]   ,   return_tensors="""pt"""  ).pixel_values
									_lowerCAmelCase				,					_lowerCAmelCase       :  List[Any]   				=					self.calculate_expected_size(image_inputs[0]  )
									self.assertEqual(
									    encoded_images.shape   ,   (
									        1,
									        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


UpperCamelCase_     			=       {
    """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_     			=       ["""DeformableDetrFeatureExtractor"""]
     UpperCamelCase_     			=       ["""DeformableDetrImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_     			=       [
         """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

     UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


