






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''pixel_values''']





       def __init__(				self	:int,      snake_case	:bool = True,      snake_case	:Dict[str, int] = None,      snake_case	:int = 0.9,      snake_case	:PILImageResampling = PILImageResampling.BICUBIC,      snake_case	:bool = True,      snake_case	:Dict[str, int] = None,      snake_case	:Union[int, float] = 1 / 255,      snake_case	:bool = True,      snake_case	:bool = True,      snake_case	:Optional[Union[float, List[float]]] = None,      snake_case	:Optional[Union[float, List[float]]] = None,      **snake_case	:List[Any],      ):



          """simple docstring"""

          super().__init__(**snake_case)
          _lowercase              =size if size is not None else {'shortest_edge': 224}
          _lowercase              =get_size_dict(snake_case,      default_to_square=snake_case)
          _lowercase              =crop_size if crop_size is not None else {'height': 224, 'width': 224}
          _lowercase              =get_size_dict(snake_case,      param_name='crop_size')

          _lowercase              =do_resize
          _lowercase              =size
          _lowercase              =crop_pct
          _lowercase              =resample
          _lowercase              =do_center_crop
          _lowercase              =crop_size
          _lowercase              =do_rescale
          _lowercase              =rescale_factor
          _lowercase              =do_normalize
          _lowercase              =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          _lowercase              =image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def 				UpperCamelCase__						(				self	:Optional[int],      snake_case	:np.ndarray,      snake_case	:Dict[str, int],      snake_case	:Optional[float] = None,      snake_case	:PILImageResampling = PILImageResampling.BICUBIC,      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          _lowercase              =get_size_dict(snake_case,      default_to_square=snake_case)
          if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
          if crop_pct is not None:
             if "shortest_edge" in size:
                _lowercase              =int(size['shortest_edge'] / crop_pct)
             elif "height" in size and "width" in size:
                if size["height"] == size["width"]:
                   _lowercase              =int(size['height'] / crop_pct)
                else:
                   _lowercase              =(int(size['height'] / crop_pct), int(size['width'] / crop_pct))
             else:
                raise ValueError('Invalid size for resize: {}'.format(snake_case))

             _lowercase              =get_resize_output_image_size(snake_case,      size=snake_case,      default_to_square=snake_case)
          else:
             if "shortest_edge" in size:
                _lowercase              =get_resize_output_image_size(snake_case,      size=size['shortest_edge'],      default_to_square=snake_case)
             elif "height" in size and "width" in size:
                _lowercase              =(size['height'], size['width'])
             else:
                raise ValueError('Invalid size for resize: {}'.format(snake_case))

          return resize(snake_case,      size=snake_case,      resample=snake_case,      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:str,      snake_case	:np.ndarray,      snake_case	:Dict[str, int],      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:List[str],      ):



          """simple docstring"""

          _lowercase              =get_size_dict(snake_case)
          if "height" not in size or "width" not in size:
             raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}''')
          return center_crop(snake_case,      size=(size['height'], size['width']),      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:np.ndarray,      snake_case	:Union[int, float],      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          return rescale(snake_case,      scale=snake_case,      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:np.ndarray,      snake_case	:Union[float, List[float]],      snake_case	:Union[float, List[float]],      snake_case	:Optional[Union[str, ChannelDimension]] = None,      **snake_case	:int,      ):



          """simple docstring"""

          return normalize(snake_case,      mean=snake_case,      std=snake_case,      data_format=snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:ImageInput,      snake_case	:bool = None,      snake_case	:Dict[str, int] = None,      snake_case	:int = None,      snake_case	:PILImageResampling = None,      snake_case	:bool = None,      snake_case	:Dict[str, int] = None,      snake_case	:bool = None,      snake_case	:float = None,      snake_case	:bool = None,      snake_case	:Optional[Union[float, List[float]]] = None,      snake_case	:Optional[Union[float, List[float]]] = None,      snake_case	:Optional[Union[str, TensorType]] = None,      snake_case	:ChannelDimension = ChannelDimension.FIRST,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =do_resize if do_resize is not None else self.do_resize
          _lowercase              =crop_pct if crop_pct is not None else self.crop_pct
          _lowercase              =resample if resample is not None else self.resample
          _lowercase              =do_center_crop if do_center_crop is not None else self.do_center_crop
          _lowercase              =do_rescale if do_rescale is not None else self.do_rescale
          _lowercase              =rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowercase              =do_normalize if do_normalize is not None else self.do_normalize
          _lowercase              =image_mean if image_mean is not None else self.image_mean
          _lowercase              =image_std if image_std is not None else self.image_std

          _lowercase              =size if size is not None else self.size
          _lowercase              =get_size_dict(snake_case,      default_to_square=snake_case)
          _lowercase              =crop_size if crop_size is not None else self.crop_size
          _lowercase              =get_size_dict(snake_case,      param_name='crop_size')

          _lowercase              =make_list_of_images(snake_case)

          if not valid_images(snake_case):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.')

          if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.')

          if do_center_crop and crop_pct is None:
             raise ValueError('Crop_pct must be specified if do_center_crop is True.')

          if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.')

          if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.')

          # All transformations expect numpy arrays.
          _lowercase              =[to_numpy_array(snake_case) for image in images]

          if do_resize:
             _lowercase              =[self.resize(image=snake_case,      size=snake_case,      crop_pct=snake_case,      resample=snake_case) for image in images]

          if do_center_crop:
             _lowercase              =[self.center_crop(image=snake_case,      size=snake_case) for image in images]

          if do_rescale:
             _lowercase              =[self.rescale(image=snake_case,      scale=snake_case) for image in images]

          if do_normalize:
             _lowercase              =[self.normalize(image=snake_case,      mean=snake_case,      std=snake_case) for image in images]

          _lowercase              =[to_channel_dimension_format(snake_case,      snake_case) for image in images]

          _lowercase              ={'pixel_values': images}
          return BatchFeature(data=snake_case,      tensor_type=snake_case)










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'],
              ),      )


