





'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_SCREAMING_SNAKE_CASE		       =							logging.get_logger(__name__)


if is_vision_available():
       import PIL

class       lowerCAmelCase_						(						UpperCAmelCase__						):
 __lowerCamelCase  :							Optional[int]				     =    ["pixel_values"]




 def __init__(		self	,   _lowerCAmelCase = True	,   _lowerCAmelCase = None	,   _lowerCAmelCase = PILImageResampling.BICUBIC	,   _lowerCAmelCase = True	,   _lowerCAmelCase = None	,   _lowerCAmelCase = True	,   _lowerCAmelCase = 1 / 255	,   _lowerCAmelCase = True	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = True	,   **_lowerCAmelCase	,   )			->							Optional[int]:
     super().__init__(**_a						)
     _lowerCAmelCase			=					size if size is not None else {"shortest_edge": 224}
     _lowerCAmelCase			=					get_size_dict(_a	,   default_to_square=_a						)
     _lowerCAmelCase			=					crop_size if crop_size is not None else {"height": 224, "width": 224}
     _lowerCAmelCase			=					get_size_dict(_a	,   default_to_square=_a	,   param_name="crop_size"						)

     _lowerCAmelCase			=					do_resize
     _lowerCAmelCase			=					size
     _lowerCAmelCase			=					resample
     _lowerCAmelCase			=					do_center_crop
     _lowerCAmelCase			=					crop_size
     _lowerCAmelCase			=					do_rescale
     _lowerCAmelCase			=					rescale_factor
     _lowerCAmelCase			=					do_normalize
     _lowerCAmelCase			=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     _lowerCAmelCase			=					image_std if image_std is not None else OPENAI_CLIP_STD
     _lowerCAmelCase			=					do_convert_rgb




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase = PILImageResampling.BICUBIC	,   _lowerCAmelCase = None	,   **_lowerCAmelCase	,   )			->							Union[str, Any]:
     _lowerCAmelCase			=					get_size_dict(_a	,   default_to_square=_a						)
     if "shortest_edge" not in size:
         raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
     _lowerCAmelCase			=					get_resize_output_image_size(_a	,   size=size["shortest_edge"]	,   default_to_square=_a						)
     return resize(_a	,   size=_a	,   resample=_a	,   data_format=_a	,   **_a						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   **_lowerCAmelCase	,   )			->							Dict:
     _lowerCAmelCase			=					get_size_dict(_a						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
     return center_crop(_a	,   size=(size["height"], size["width"])	,   data_format=_a	,   **_a						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   **_lowerCAmelCase	,   )			->							str:
     return rescale(_a	,   scale=_a	,   data_format=_a	,   **_a						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   **_lowerCAmelCase	,   )			->							Dict:
     return normalize(_a	,   mean=_a	,   std=_a	,   data_format=_a	,   **_a						)





 def 							_snake_case      (		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	,   )			->							Any:
     _lowerCAmelCase			=					do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase			=					size if size is not None else self.size
     _lowerCAmelCase			=					get_size_dict(_a	,   param_name="size"	,   default_to_square=_a						)
     _lowerCAmelCase			=					resample if resample is not None else self.resample
     _lowerCAmelCase			=					do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase			=					crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase			=					get_size_dict(_a	,   param_name="crop_size"	,   default_to_square=_a						)
     _lowerCAmelCase			=					do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase			=					rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase			=					do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase			=					image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase			=					image_std if image_std is not None else self.image_std
     _lowerCAmelCase			=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     _lowerCAmelCase			=					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:
         raise ValueError("Size must be specified if do_resize is True."						)

     if do_center_crop and crop_size is None:
         raise ValueError("Crop size 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."						)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         _lowerCAmelCase			=					[convert_to_rgb(_a						) for image in images]

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

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

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

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

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

     _lowerCAmelCase			=					[to_channel_dimension_format(_a	,   _a						) for image in images]

     _lowerCAmelCase			=					{"pixel_values": images}
     return BatchFeature(data=_a	,   tensor_type=_a						)








"""simple docstring"""




def     a__ (				snake_case__       , snake_case__ = False				)						->					str:

			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected string as input, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)
			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected boolean as use_pascal parameter, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)

			lowerCamelCase			      =					input_str.split("""_"""				)

			lowerCamelCase			      =					0 if use_pascal else 1

			lowerCamelCase			      =					words[start_index:]

			lowerCamelCase			      =					[word[0].upper() + word[1:] for word in words_to_capitalize]

			lowerCamelCase			      =					"""""" if use_pascal else words[0]

			return "".join([initial_word, *capitalized_words]				)


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

					testmod()



