




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor





class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    def __init__(			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Any  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	int = 32  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float] = 1 / 255  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict=7  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=30  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=400  ,      SCREAMING_SNAKE_CASE__	:	Any=3  ,      ) ->			Tuple:
         lowerCAmelCase__            =					parent
         lowerCAmelCase__            =					do_resize
         lowerCAmelCase__            =					size if size is not None else {"shortest_edge": 288}
         lowerCAmelCase__            =					size_divisor
         lowerCAmelCase__            =					do_rescale
         lowerCAmelCase__            =					rescale_factor
         lowerCAmelCase__            =					do_normalize
         lowerCAmelCase__            =					do_center_crop
         lowerCAmelCase__            =					image_mean
         lowerCAmelCase__            =					image_std
         lowerCAmelCase__            =					do_pad
         lowerCAmelCase__            =					batch_size
         lowerCAmelCase__            =					num_channels
         lowerCAmelCase__            =					min_resolution
         lowerCAmelCase__            =					max_resolution


    def   a (			self	:	str  ) ->			Tuple:
         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	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Any=False  ) ->			Optional[Any]:
         if not batched:
              lowerCAmelCase__            =					self.size["shortest_edge"]
              lowerCAmelCase__            =					image_inputs[0]
              if isinstance(SCREAMING_SNAKE_CASE__  ,      Image.Image  ):
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					image.size
              else:
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					image.shape[1], image.shape[2]
              lowerCAmelCase__            =					size / min(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
              if h < w:
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					size, scale * w
              else:
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					scale * h, size

              lowerCAmelCase__            =					int((1_333 / 800) * size  )
              if max(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) > max_size:
                   lowerCAmelCase__            =					max_size / max(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
                   lowerCAmelCase__            =					newh * scale
                   lowerCAmelCase__            =					neww * scale

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

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

         return expected_height, expected_width






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




    """simple docstring"""

    snake_case__   = BridgeTowerImageProcessor if is_vision_available() else None


    def   a (			self	:	Tuple  ) ->			int:
         lowerCAmelCase__            =					BridgeTowerImageProcessingTester(self  )


    @property
    def   a (			self	:	str  ) ->			Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()


    def   a (			self	:	List[Any]  ) ->			Dict:
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "image_mean"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "image_std"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "do_normalize"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "do_resize"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "size"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "size_divisor"  )  )


    def   a (			self	:	Any  ) ->			Tuple:
         pass


    def   a (			self	:	Tuple  ) ->			Any:
         # Initialize image processor
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         lowerCAmelCase__            =					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
         lowerCAmelCase__            =					image_processing(image_inputs[0]  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

         # Test batched
         lowerCAmelCase__            =					image_processing(SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).pixel_values

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


    def   a (			self	:	Optional[Any]  ) ->			List[Any]:
         # Initialize image processor
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         lowerCAmelCase__            =					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
         lowerCAmelCase__            =					image_processing(image_inputs[0]  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

         # Test batched
         lowerCAmelCase__            =					image_processing(SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).pixel_values

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


    def   a (			self	:	Optional[Any]  ) ->			Tuple:
         # Initialize image processor
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         lowerCAmelCase__            =					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
         lowerCAmelCase__            =					image_processing(image_inputs[0]  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

         # Test batched
         lowerCAmelCase__            =					image_processing(SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).pixel_values

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








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 ChineseCLIPImageProcessor






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def __init__(				self	:Any,      snake_case	:Optional[int],      snake_case	:Optional[Any]=7,      snake_case	:str=3,      snake_case	:Optional[int]=18,      snake_case	:str=30,      snake_case	:List[Any]=400,      snake_case	:Any=True,      snake_case	:Dict=None,      snake_case	:Any=True,      snake_case	:Dict=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],      snake_case	:Any=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],      snake_case	:int=True,      ):



          """simple docstring"""

          _lowercase              =size if size is not None else {'height': 224, 'width': 224}
          _lowercase              =crop_size if crop_size is not None else {'height': 18, 'width': 18}
          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =num_channels
          _lowercase              =image_size
          _lowercase              =min_resolution
          _lowercase              =max_resolution
          _lowercase              =do_resize
          _lowercase              =size
          _lowercase              =do_center_crop
          _lowercase              =crop_size
          _lowercase              =do_normalize
          _lowercase              =image_mean
          _lowercase              =image_std
          _lowercase              =do_convert_rgb





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[Any]=False,      snake_case	:Any=False,      snake_case	:Union[str, Any]=False):



          """simple docstring"""

          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
             _lowercase              =[]
             for i in range(self.batch_size):
                image_inputs.append(
                    np.random.randint(
                        255,      size=(self.num_channels, self.max_resolution, self.max_resolution),      dtype=np.uinta))
          else:
             _lowercase              =[]
             for i in range(self.batch_size):
                _lowercase       ,       _lowercase              =np.random.choice(np.arange(self.min_resolution,      self.max_resolution),      2)
                image_inputs.append(np.random.randint(255,      size=(self.num_channels, width, height),      dtype=np.uinta))

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

          if torchify:
             _lowercase              =[torch.from_numpy(snake_case) for x in image_inputs]

          return image_inputs






@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		List[Any]					=ChineseCLIPImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =ChineseCLIPImageProcessingTester(self,      do_center_crop=snake_case)





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_resize'))
          self.assertTrue(hasattr(snake_case,      'size'))
          self.assertTrue(hasattr(snake_case,      'do_center_crop'))
          self.assertTrue(hasattr(snake_case,      'center_crop'))
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'image_mean'))
          self.assertTrue(hasattr(snake_case,      'image_std'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size,      {'height': 224, 'width': 224})
          self.assertEqual(image_processor.crop_size,      {'height': 18, 'width': 18})

          _lowercase              =self.image_processing_class.from_dict(self.image_processor_dict,      size=42,      crop_size=84)
          self.assertEqual(image_processor.size,      {'shortest_edge': 42})
          self.assertEqual(image_processor.crop_size,      {'height': 84, 'width': 84})





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          pass





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowercase              =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
          _lowercase              =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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowercase              =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
          _lowercase              =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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )






@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=ChineseCLIPImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =ChineseCLIPImageProcessingTester(self,      num_channels=4,      do_center_crop=snake_case)
          _lowercase              =3





       @property
       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_resize'))
          self.assertTrue(hasattr(snake_case,      'size'))
          self.assertTrue(hasattr(snake_case,      'do_center_crop'))
          self.assertTrue(hasattr(snake_case,      'center_crop'))
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'image_mean'))
          self.assertTrue(hasattr(snake_case,      'image_std'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          pass





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =image_processing(image_inputs[0],      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(snake_case,      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )


