

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


lowercase_             =     logging.get_logger(__name__)





class 				__lowerCAmelCase  (      __lowercase							):
  _a		    =						["""pixel_values"""]




  def __init__(       self							,     lowerCAmelCase = True							,     lowerCAmelCase = None							,     lowerCAmelCase = 0.9							,     lowerCAmelCase = PILImageResampling.BICUBIC							,     lowerCAmelCase = True							,     lowerCAmelCase = None							,     lowerCAmelCase = 1 / 255							,     lowerCAmelCase = True							,     lowerCAmelCase = True							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     **lowerCAmelCase							,     )						->				List[Any]:
        '''simple docstring'''






        super().__init__(**_A      )
        _lowercase			 =size if size is not None else {'shortest_edge': 224}
        _lowercase			 =get_size_dict(_A							,     default_to_square=_A      )
        _lowercase			 =crop_size if crop_size is not None else {'height': 224, 'width': 224}
        _lowercase			 =get_size_dict(_A							,     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 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = None							,     lowerCAmelCase = PILImageResampling.BICUBIC							,     lowerCAmelCase = None							,     **lowerCAmelCase							,     )						->				str:
        '''simple docstring'''






        _lowercase			 =get_size_dict(_A							,     default_to_square=_A      )
        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(_A      )      )

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

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




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = None							,     **lowerCAmelCase							,     )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =get_size_dict(_A      )
        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(_A							,     size=(size['height'], size['width'])							,     data_format=_A							,     **_A      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = None							,     **lowerCAmelCase							,     )						->				List[Any]:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = None							,     **lowerCAmelCase							,     )						->				Union[str, Any]:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = ChannelDimension.FIRST							,     **lowerCAmelCase							,     )						->				List[str]:
        '''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(_A							,     default_to_square=_A      )
        _lowercase			 =crop_size if crop_size is not None else self.crop_size
        _lowercase			 =get_size_dict(_A							,     param_name='crop_size'      )

        _lowercase			 =make_list_of_images(_A      )

        if not valid_images(_A      ):
              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(_A      ) for image in images]

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

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

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

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

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

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





def   a   (    A__			:     str   ,     A__			:     int  )					->   list[str]:





      """simple docstring"""



      return [sentence[i : i + ngram_size] for i in range(len(A__  ) - ngram_size + 1  )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()


