
'''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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


A__   :		Union[str, Any] 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''pixel_values''']
 def __init__(   self			:    List[Any]				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    PILImageResampling = PILImageResampling.BILINEAR				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Union[int, float] = 1 / 2_55				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   **lowerCamelCase			:    str				,   ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )
   __A		:     Tuple												=		size if size is not None else {"""shortest_edge""": 3_84}
   __A		:     Optional[int]												=		get_size_dict(lowerCamelCase				,   default_to_square=lowerCamelCase       )

   __A		:     Tuple												=		do_resize
   __A		:     Union[str, Any]												=		size
   # Default value set here for backwards compatibility where the value in config is None
   __A		:     Union[str, Any]												=		crop_pct if crop_pct is not None else 2_24 / 2_56
   __A		:     Any												=		resample
   __A		:     Any												=		do_rescale
   __A		:     Optional[Any]												=		rescale_factor
   __A		:     List[str]												=		do_normalize
   __A		:     Tuple												=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __A		:     List[str]												=		image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Dict[str, int]				,   lowerCamelCase			:    float				,   lowerCamelCase			:    PILImageResampling = PILImageResampling.BICUBIC				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   __A		:     Optional[int]												=		get_size_dict(lowerCamelCase				,   default_to_square=lowerCamelCase       )
   if "shortest_edge" not in size:
     raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"       )
   __A		:     Union[str, Any]												=		size["""shortest_edge"""]

   if shortest_edge < 3_84:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     __A		:     int												=		int(shortest_edge / crop_pct       )
     __A		:     List[Any]												=		get_resize_output_image_size(lowerCamelCase				,   size=lowerCamelCase				,   default_to_square=lowerCamelCase       )
     __A		:     Tuple												=		resize(image=lowerCamelCase				,   size=lowerCamelCase				,   resample=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=lowerCamelCase				,   size=(shortest_edge, shortest_edge)				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
   else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         lowerCamelCase				,   size=(shortest_edge, shortest_edge)				,   resample=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Tuple				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Union[int, float]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    Optional[Any]				,   ):



   """simple docstring"""





   return rescale(lowerCamelCase				,   scale=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Union[float, List[float]]				,   lowerCamelCase			:    Union[float, List[float]]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    List[str]				,   ):



   """simple docstring"""





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






 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    ImageInput				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    PILImageResampling = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    ChannelDimension = ChannelDimension.FIRST				,   **lowerCamelCase			:    Any				,   ):



   """simple docstring"""





   __A		:     int												=		do_resize if do_resize is not None else self.do_resize
   __A		:     Optional[Any]												=		crop_pct if crop_pct is not None else self.crop_pct
   __A		:     Union[str, Any]												=		resample if resample is not None else self.resample
   __A		:     Any												=		do_rescale if do_rescale is not None else self.do_rescale
   __A		:     Union[str, Any]												=		rescale_factor if rescale_factor is not None else self.rescale_factor
   __A		:     Any												=		do_normalize if do_normalize is not None else self.do_normalize
   __A		:     Optional[int]												=		image_mean if image_mean is not None else self.image_mean
   __A		:     Optional[int]												=		image_std if image_std is not None else self.image_std

   __A		:     Any												=		size if size is not None else self.size
   __A		:     Union[str, Any]												=		get_size_dict(lowerCamelCase				,   default_to_square=lowerCamelCase       )

   __A		:     str												=		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 or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize is True."""       )

   if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
     raise ValueError("""crop_pct must be specified if size < 384."""       )

   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		:     Any												=		[to_numpy_array(lowerCamelCase       ) for image in images]

   if do_resize:
     __A		:     Dict												=		[self.resize(image=lowerCamelCase				,   size=lowerCamelCase				,   crop_pct=lowerCamelCase				,   resample=lowerCamelCase       ) for image in images]

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

   if do_normalize:
     __A		:     Dict												=		[self.normalize(image=lowerCamelCase				,   mean=lowerCamelCase				,   std=lowerCamelCase       ) for image in images]

   __A		:     int												=		[to_channel_dimension_format(lowerCamelCase				,   lowerCamelCase       ) for image in images]

   __A		:     Optional[Any]												=		{"""pixel_values""": images}
   return BatchFeature(data=lowerCamelCase				,   tensor_type=lowerCamelCase       )





'''simple docstring'''
from __future__ import annotations







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[int] )				->       int:





  """simple docstring"""

  if not nums:
    return 0
  __A		:     List[Any]												=		nums[0]
  __A		:     Union[str, Any]												=		0
  for num in nums[1:]:
    __A							,					__A		:     Union[str, Any]												=		(
        max_excluding + num,
        max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ),
    )
  return max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



