from ..utils import requires_backends


UpperCamelCase__       							=      None





class   A							:


   def __init__(self  :		List[Any] ,						*__UpperCAmelCase  :		Union[str, Any] ,						**__UpperCAmelCase  :		int	)		->       Optional[Any]:
         """simple docstring"""



         requires_backends(self ,						["detectron2"]	)





   @classmethod
   def     lowercase_   (cls  :		Dict ,						*__UpperCAmelCase  :		Tuple ,						**__UpperCAmelCase  :		List[Any]	)		->       Union[str, Any]:
         """simple docstring"""



         requires_backends(cls ,						["detectron2"]	)




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 EfficientNetImageProcessor





class   A							(       unittest.TestCase  ):


   def __init__(self  :		Optional[int] ,						__UpperCAmelCase  :		Dict ,						__UpperCAmelCase  :		List[Any]=1_3 ,						__UpperCAmelCase  :		int=3 ,						__UpperCAmelCase  :		int=1_8 ,						__UpperCAmelCase  :		Tuple=3_0 ,						__UpperCAmelCase  :		List[Any]=4_0_0 ,						__UpperCAmelCase  :		Any=True ,						__UpperCAmelCase  :		Dict=None ,						__UpperCAmelCase  :		Optional[Any]=True ,						__UpperCAmelCase  :		Tuple=[0.5, 0.5, 0.5] ,						__UpperCAmelCase  :		Optional[Any]=[0.5, 0.5, 0.5] ,						)		->       Any:
         """simple docstring"""



         UpperCAmelCase__			=						size if size is not None else {"height": 1_8, "width": 1_8}
         UpperCAmelCase__			=						parent
         UpperCAmelCase__			=						batch_size
         UpperCAmelCase__			=						num_channels
         UpperCAmelCase__			=						image_size
         UpperCAmelCase__			=						min_resolution
         UpperCAmelCase__			=						max_resolution
         UpperCAmelCase__			=						do_resize
         UpperCAmelCase__			=						size
         UpperCAmelCase__			=						do_normalize
         UpperCAmelCase__			=						image_mean
         UpperCAmelCase__			=						image_std





   def     lowercase_   (self  :		Optional[int]	)		->       Optional[Any]:
         """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,
         }





@require_torch
@require_vision
class   A							(       UpperCAmelCase_	,       unittest.TestCase  ):
   __UpperCAmelCase				:				Dict          =    EfficientNetImageProcessor if is_vision_available() else None


   def     lowercase_   (self  :		str	)		->       int:
         """simple docstring"""



         UpperCAmelCase__			=						EfficientNetImageProcessorTester(self	)


   @property
   def     lowercase_   (self  :		Tuple	)		->       Optional[Any]:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def     lowercase_   (self  :		Tuple	)		->       List[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						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"	)	)


   def     lowercase_   (self  :		Tuple	)		->       str:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size ,						{"height": 1_8, "width": 1_8}	)

         UpperCAmelCase__			=						self.image_processing_class.from_dict(self.image_processor_dict ,						size=4_2	)
         self.assertEqual(image_processor.size ,						{"height": 4_2, "width": 4_2}	)


   def     lowercase_   (self  :		List[Any]	)		->       Optional[int]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         UpperCAmelCase__			=						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__UpperCAmelCase	)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase ,						Image.Image	)

         # Test not batched input
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						image_processing(__UpperCAmelCase ,						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"],
             ) ,						)


   def     lowercase_   (self  :		Tuple	)		->       Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						image_processing(__UpperCAmelCase ,						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"],
             ) ,						)





   def     lowercase_   (self  :		Optional[int]	)		->       str:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						image_processing(__UpperCAmelCase ,						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"],
             ) ,						)



