






from typing import Dict, Iterable, 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, logging


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:int   =       ['pixel_values']







	def __init__(  self      :							List[str]       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							Dict[str, int] = None       ,				_lowerCAmelCase      :							PILImageResampling = PILImageResampling.BICUBIC       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							Dict[str, int] = None       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							Union[int, float] = 1 / 255       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN       ,				_lowerCAmelCase      :							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD       ,				**_lowerCAmelCase      :							str       ,				)		-> None:
		"""simple docstring"""







		super().__init__(**_lowerCAmelCase			)
		__lowercase             =  size if size is not None else {"""shortest_edge""": 224}
		__lowercase             =  get_size_dict(_lowerCAmelCase       ,				default_to_square=_lowerCAmelCase			)
		__lowercase             =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		__lowercase             =  get_size_dict(_lowerCAmelCase       ,				param_name="""crop_size"""			)

		__lowercase             =  do_resize
		__lowercase             =  size
		__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      :							Any       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							Dict[str, int]       ,				_lowerCAmelCase      :							PILImageResampling = PILImageResampling.BICUBIC       ,				_lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None       ,				**_lowerCAmelCase      :							Union[str, Any]       ,				)		-> np.ndarray:
		"""simple docstring"""







		__lowercase             =  get_size_dict(_lowerCAmelCase       ,				default_to_square=_lowerCAmelCase			)
		# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
		if "shortest_edge" in size:
			__lowercase             =  int((256 / 224) * size["""shortest_edge"""]			)
			__lowercase             =  get_resize_output_image_size(_lowerCAmelCase       ,				size=_lowerCAmelCase       ,				default_to_square=_lowerCAmelCase			)
			__lowercase             =  {"""height""": output_size[0], """width""": output_size[1]}
		if "height" not in size_dict or "width" not in size_dict:
			raise ValueError(
			    F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'			)
		return resize(
		    _lowerCAmelCase       ,				size=(size_dict["""height"""], size_dict["""width"""])       ,				resample=_lowerCAmelCase       ,				data_format=_lowerCAmelCase       ,				**_lowerCAmelCase			)







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							Dict[str, int]       ,				_lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None       ,				**_lowerCAmelCase      :							int       ,				)		-> np.ndarray:
		"""simple docstring"""







		__lowercase             =  get_size_dict(_lowerCAmelCase			)
		if "height" not in size or "width" not in size:
			raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'			)
		return center_crop(_lowerCAmelCase       ,				size=(size["""height"""], size["""width"""])       ,				data_format=_lowerCAmelCase       ,				**_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							Union[int, float]       ,				_lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None       ,				**_lowerCAmelCase      :							List[Any]       ,				)		-> np.ndarray:
		"""simple docstring"""







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







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							Union[float, List[float]]       ,				_lowerCAmelCase      :							Union[float, List[float]]       ,				_lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None       ,				**_lowerCAmelCase      :							Tuple       ,				)		-> np.ndarray:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							ImageInput       ,				_lowerCAmelCase      :							Optional[bool] = None       ,				_lowerCAmelCase      :							Optional[Dict[str, int]] = None       ,				_lowerCAmelCase      :							PILImageResampling = None       ,				_lowerCAmelCase      :							Optional[bool] = None       ,				_lowerCAmelCase      :							Optional[Dict[str, int]] = None       ,				_lowerCAmelCase      :							Optional[bool] = None       ,				_lowerCAmelCase      :							Optional[float] = None       ,				_lowerCAmelCase      :							Optional[bool] = None       ,				_lowerCAmelCase      :							Optional[Union[float, Iterable[float]]] = None       ,				_lowerCAmelCase      :							Optional[Union[float, Iterable[float]]] = None       ,				_lowerCAmelCase      :							Optional[TensorType] = None       ,				_lowerCAmelCase      :							ChannelDimension = ChannelDimension.FIRST       ,				**_lowerCAmelCase      :							List[Any]       ,				)		-> BatchFeature:
		"""simple docstring"""







		__lowercase             =  do_resize if do_resize is not None else self.do_resize
		__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(_lowerCAmelCase       ,				default_to_square=_lowerCAmelCase			)
		__lowercase             =  crop_size if crop_size is not None else self.crop_size
		__lowercase             =  get_size_dict(_lowerCAmelCase       ,				param_name="""crop_size"""			)

		__lowercase             =  make_list_of_images(_lowerCAmelCase			)

		if not valid_images(_lowerCAmelCase			):
			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."""			)

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

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

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

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

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

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

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









from __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if not nums:
		return 0
	__lowercase             =  nums[0]
	__lowercase             =  0
	for num in nums[1:]:
		__lowercase       ,						__lowercase             =  (
		    max_excluding + num,
		    max(lowerCamelCase				,  lowerCamelCase					),
		)
	return max(lowerCamelCase				,  lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

