






'''simple docstring'''







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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__lowerCAmelCase					   =       logging.get_logger(__name__)




class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   List[str]			 =    ['''pixel_values''']







  def __init__( self      ,  UpperCamelCase__ = True      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = True      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = PILImageResampling.BICUBIC      ,  UpperCamelCase__ = True      ,  UpperCamelCase__ = 1 / 255      ,  UpperCamelCase__ = True      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         super().__init__(**UpperCamelCase__  )
         a_	    = crop_size if crop_size is not None else {'height': 256, 'width': 256}
         a_	    = get_size_dict(UpperCamelCase__      ,  param_name='crop_size'  )
         a_	    = size if size is not None else {'height': 224, 'width': 224}
         a_	    = get_size_dict(UpperCamelCase__  )

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







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         a_	    = self.size if size is None else size
         a_	    = get_size_dict(UpperCamelCase__  )
         a_	    = get_size_dict(UpperCamelCase__      ,  param_name='crop_size'  )

         a_    ,  a_	    = get_image_size(UpperCamelCase__  )
         a_	    = min(UpperCamelCase__      ,  UpperCamelCase__  )
         a_	    = (size['height'] / crop_size['height']) * min_dim
         a_	    = (size['width'] / crop_size['width']) * min_dim
         return center_crop(UpperCamelCase__      ,  size=(cropped_height, cropped_width)      ,  data_format=UpperCamelCase__      ,  **UpperCamelCase__  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = PIL.Image.BICUBIC      ,  UpperCamelCase__ = None      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         a_	    = get_size_dict(UpperCamelCase__  )
         if "height" not in size or "width" not in size:
                raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'  )
         return resize(
             UpperCamelCase__      ,  size=(size['height'], size['width'])      ,  resample=UpperCamelCase__      ,  data_format=UpperCamelCase__      ,  **UpperCamelCase__  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = None      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = None      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = ChannelDimension.FIRST      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         a_	    = do_center_crop if do_center_crop is not None else self.do_center_crop
         a_	    = crop_size if crop_size is not None else self.crop_size
         a_	    = get_size_dict(UpperCamelCase__      ,  param_name='crop_size'  )
         a_	    = do_resize if do_resize is not None else self.do_resize
         a_	    = size if size is not None else self.size
         a_	    = get_size_dict(UpperCamelCase__  )
         a_	    = resample if resample is not None else self.resample
         a_	    = do_rescale if do_rescale is not None else self.do_rescale
         a_	    = rescale_factor if rescale_factor is not None else self.rescale_factor
         a_	    = do_normalize if do_normalize is not None else self.do_normalize
         a_	    = image_mean if image_mean is not None else self.image_mean
         a_	    = image_std if image_std is not None else self.image_std

         a_	    = make_list_of_images(UpperCamelCase__  )

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

         if do_center_crop and crop_size is None:
                raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'  )

         if do_resize and size is None:
                raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'  )

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

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

         if do_resize:
                a_	    = [self.resize(image=UpperCamelCase__      ,  size=UpperCamelCase__      ,  resample=UpperCamelCase__  ) for image in images]

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

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

         a_	    = [to_channel_dimension_format(UpperCamelCase__      ,  UpperCamelCase__  ) for image in images]

         a_	    = {'pixel_values': images}
         return BatchFeature(data=UpperCamelCase__      ,  tensor_type=UpperCamelCase__  )












'''simple docstring'''







import re


def 			__UpperCamelCase				(			lowercase_					:     int      ):
       """simple docstring"""




       def docstring_decorator(lowercase_					:     int      ):
              a_	    = fn.__doc__
              a_	    = func_doc.split('\n'      )
              a_	    = 0
              while i < len(lowercase_      ) and re.search(R'^\s*Examples?:\s*$'      ,					lines[i]      ) is None:
                     i += 1
              if i < len(lowercase_      ):
                     a_	    = example_docstring
                     a_	    = '\n'.join(lowercase_      )
              else:
                     raise ValueError(
                         F'The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '
                         F'current docstring is:\n{func_doc}'      )
              a_	    = func_doc
              return fn

       return docstring_decorator




