


'''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,
    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


lowerCAmelCase						=					logging.get_logger(__name__)






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=['pixel_values']






       def __init__( self			,     _UpperCamelCase = True			,     _UpperCamelCase = None			,     _UpperCamelCase = 0.9			,     _UpperCamelCase = PILImageResampling.BICUBIC			,     _UpperCamelCase = True			,     _UpperCamelCase = None			,     _UpperCamelCase = 1 / 255			,     _UpperCamelCase = True			,     _UpperCamelCase = True			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      None:
            super().__init__(**_UpperCamelCase   )
            _A				=							size if size is not None else {'shortest_edge': 224}
            _A				=							get_size_dict(_UpperCamelCase			,     default_to_square=_UpperCamelCase   )
            _A				=							crop_size if crop_size is not None else {'height': 224, 'width': 224}
            _A				=							get_size_dict(_UpperCamelCase			,     param_name='crop_size'   )

            _A				=							do_resize
            _A				=							size
            _A				=							crop_pct
            _A				=							resample
            _A				=							do_center_crop
            _A				=							crop_size
            _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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = PILImageResampling.BICUBIC			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      np.ndarray:
            _A				=							get_size_dict(_UpperCamelCase			,     default_to_square=_UpperCamelCase   )
            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:
                      _A				=							int(size['shortest_edge'] / crop_pct   )
                 elif "height" in size and "width" in size:
                      if size["height"] == size["width"]:
                           _A				=							int(size['height'] / crop_pct   )
                      else:
                           _A				=							(int(size['height'] / crop_pct   ), int(size['width'] / crop_pct   ))
                 else:
                      raise ValueError('Invalid size for resize: {}'.format(_UpperCamelCase   )   )

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

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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      np.ndarray:
            _A				=							get_size_dict(_UpperCamelCase   )
            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(_UpperCamelCase			,     size=(size['height'], size['width'])			,     data_format=_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      Union[str, Any]:
            return rescale(_UpperCamelCase			,     scale=_UpperCamelCase			,     data_format=_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      np.ndarray:
            return normalize(_UpperCamelCase			,     mean=_UpperCamelCase			,     std=_UpperCamelCase			,     data_format=_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( 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 = None			,     _UpperCamelCase = ChannelDimension.FIRST			,     **_UpperCamelCase			,     )->      PIL.Image.Image:
            _A				=							do_resize if do_resize is not None else self.do_resize
            _A				=							crop_pct if crop_pct is not None else self.crop_pct
            _A				=							resample if resample is not None else self.resample
            _A				=							do_center_crop if do_center_crop is not None else self.do_center_crop
            _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				=							size if size is not None else self.size
            _A				=							get_size_dict(_UpperCamelCase			,     default_to_square=_UpperCamelCase   )
            _A				=							crop_size if crop_size is not None else self.crop_size
            _A				=							get_size_dict(_UpperCamelCase			,     param_name='crop_size'   )

            _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_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.
            _A				=							[to_numpy_array(_UpperCamelCase   ) for image in images]

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

            if do_center_crop:
                 _A				=							[self.center_crop(image=_UpperCamelCase			,     size=_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'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       int = 0							,    __UpperCamelCase					:       int = 0		)					->	int:







     """simple docstring"""
     _A				=							right or len(__UpperCamelCase		) - 1
     if left > right:
          return -1
     elif list_data[left] == key:
          return left
     elif list_data[right] == key:
          return right
     else:
          return search(__UpperCamelCase							,    __UpperCamelCase							,    left + 1							,    right - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()





