"""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


_a				:		Dict	   =       logging.get_logger(__name__)


if is_vision_available():
     import PIL
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		["pixel_values"]






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

       _lowerCAmelCase						:	int        =					do_resize
       _lowerCAmelCase						:	Union[str, Any]        =					size
       _lowerCAmelCase						:	Optional[int]        =					resample
       _lowerCAmelCase						:	List[str]        =					do_center_crop
       _lowerCAmelCase						:	Tuple        =					crop_size
       _lowerCAmelCase						:	List[str]        =					do_rescale
       _lowerCAmelCase						:	Dict        =					rescale_factor
       _lowerCAmelCase						:	Any        =					do_normalize
       _lowerCAmelCase						:	Dict        =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _lowerCAmelCase						:	Any        =					image_std if image_std is not None else OPENAI_CLIP_STD
       _lowerCAmelCase						:	Optional[Any]        =					do_convert_rgb






     def    __A  (		self    ,					a__    ,					a__    ,					a__ = PILImageResampling.BICUBIC    ,					a__ = None    ,					**a__    ,					):
       _lowerCAmelCase						:	Dict        =					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						:	int        =					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    __A  (		self    ,					a__    ,					a__    ,					a__ = None    ,					**a__    ,					):
       _lowerCAmelCase						:	Dict        =					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    __A  (		self    ,					a__    ,					a__    ,					a__ = None    ,					**a__    ,					):
       return rescale(a__    ,					scale=a__    ,					data_format=a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__ = None    ,					**a__    ,					):
       return normalize(a__    ,					mean=a__    ,					std=a__    ,					data_format=a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = ChannelDimension.FIRST    ,					**a__    ,					):
       _lowerCAmelCase						:	Any        =					do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase						:	List[str]        =					size if size is not None else self.size
       _lowerCAmelCase						:	List[Any]        =					get_size_dict(a__    ,					param_name="""size"""    ,					default_to_square=a__  )
       _lowerCAmelCase						:	int        =					resample if resample is not None else self.resample
       _lowerCAmelCase						:	List[str]        =					do_center_crop if do_center_crop is not None else self.do_center_crop
       _lowerCAmelCase						:	Optional[int]        =					crop_size if crop_size is not None else self.crop_size
       _lowerCAmelCase						:	Any        =					get_size_dict(a__    ,					param_name="""crop_size"""    ,					default_to_square=a__  )
       _lowerCAmelCase						:	Any        =					do_rescale if do_rescale is not None else self.do_rescale
       _lowerCAmelCase						:	List[Any]        =					rescale_factor if rescale_factor is not None else self.rescale_factor
       _lowerCAmelCase						:	Dict        =					do_normalize if do_normalize is not None else self.do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean if image_mean is not None else self.image_mean
       _lowerCAmelCase						:	Dict        =					image_std if image_std is not None else self.image_std
       _lowerCAmelCase						:	List[str]        =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _lowerCAmelCase						:	Optional[int]        =					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						:	Tuple        =					[convert_to_rgb(a__  ) for image in images]

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

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

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

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

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

       _lowerCAmelCase						:	Optional[int]        =					[to_channel_dimension_format(a__    ,					a__  ) for image in images]

       _lowerCAmelCase						:	List[str]        =					{"""pixel_values""": images}
       return BatchFeature(data=a__    ,					tensor_type=a__  )






"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

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


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

     testmod()




