





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


_UpperCAmelCase				:       int	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple[int, int]:
    def constraint_to_multiple_of(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=0						,	_UpperCAmelCase=None ):
        lowerCamelCase__    :      int					 =		round(val / multiple ) * multiple

        if max_val is not None and x > max_val:
            lowerCamelCase__    :      Tuple					 =		math.floor(val / multiple ) * multiple

        if x < min_val:
            lowerCamelCase__    :      Tuple					 =		math.ceil(val / multiple ) * multiple

        return x

    lowerCamelCase__    :      Any					 =		(output_size, output_size) if isinstance(_UpperCAmelCase						,	_UpperCAmelCase ) else output_size

    lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		get_image_size(_UpperCAmelCase )
    lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		output_size

    # determine new height and width
    lowerCamelCase__    :      Dict					 =		output_height / input_height
    lowerCamelCase__    :      Optional[int]					 =		output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width ) < abs(1 - scale_height ):
            # fit width
            lowerCamelCase__    :      Union[str, Any]					 =		scale_width
        else:
            # fit height
            lowerCamelCase__    :      Tuple					 =		scale_height

    lowerCamelCase__    :      Optional[int]					 =		constraint_to_multiple_of(scale_height * input_height						,	multiple=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		constraint_to_multiple_of(scale_width * input_width						,	multiple=_UpperCAmelCase )

    return (new_height, new_width)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		["""pixel_values"""]
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      Dict[str, int] = None			,     UpperCAmelCase  :      PILImageResampling = PILImageResampling.BILINEAR			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      Union[int, float] = 1 / 255			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      Optional[Union[float, List[float]]] = None			,     UpperCAmelCase  :      Optional[Union[float, List[float]]] = None			,     **UpperCAmelCase  :      Optional[Any]			,     )			->	None:
     super().__init__(**UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		size if size is not None else {'height': 384, 'width': 384}
     lowerCamelCase__    :      str					 =		get_size_dict(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		do_resize
     lowerCamelCase__    :      List[str]					 =		size
     lowerCamelCase__    :      Optional[int]					 =		keep_aspect_ratio
     lowerCamelCase__    :      Union[str, Any]					 =		ensure_multiple_of
     lowerCamelCase__    :      int					 =		resample
     lowerCamelCase__    :      str					 =		do_rescale
     lowerCamelCase__    :      List[Any]					 =		rescale_factor
     lowerCamelCase__    :      List[Any]					 =		do_normalize
     lowerCamelCase__    :      str					 =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__    :      Tuple					 =		image_std if image_std is not None else IMAGENET_STANDARD_STD
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      np.ndarray			,     UpperCAmelCase  :      Dict[str, int]			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      PILImageResampling = PILImageResampling.BICUBIC			,     UpperCAmelCase  :      Optional[Union[str, ChannelDimension]] = None			,     **UpperCAmelCase  :      str			,     )			->	np.ndarray:
     lowerCamelCase__    :      Optional[int]					 =		get_size_dict(UpperCAmelCase	)
     if "height" not in size or "width" not in size:
         raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""	)
     lowerCamelCase__    :      Union[str, Any]					 =		get_resize_output_image_size(
         UpperCAmelCase			,     output_size=(size['height'], size['width'])			,     keep_aspect_ratio=UpperCAmelCase			,     multiple=UpperCAmelCase			,     )
     return resize(UpperCAmelCase			,     size=UpperCAmelCase			,     resample=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      np.ndarray			,     UpperCAmelCase  :      Union[int, float]			,     UpperCAmelCase  :      Optional[Union[str, ChannelDimension]] = None			,     **UpperCAmelCase  :      int			,     )			->	str:
     return rescale(UpperCAmelCase			,     scale=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      np.ndarray			,     UpperCAmelCase  :      Union[float, List[float]]			,     UpperCAmelCase  :      Union[float, List[float]]			,     UpperCAmelCase  :      Optional[Union[str, ChannelDimension]] = None			,     **UpperCAmelCase  :      str			,     )			->	np.ndarray:
     return normalize(UpperCAmelCase			,     mean=UpperCAmelCase			,     std=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      ImageInput			,     UpperCAmelCase  :      bool = None			,     UpperCAmelCase  :      int = None			,     UpperCAmelCase  :      bool = None			,     UpperCAmelCase  :      int = None			,     UpperCAmelCase  :      PILImageResampling = None			,     UpperCAmelCase  :      bool = None			,     UpperCAmelCase  :      float = None			,     UpperCAmelCase  :      bool = None			,     UpperCAmelCase  :      Optional[Union[float, List[float]]] = None			,     UpperCAmelCase  :      Optional[Union[float, List[float]]] = None			,     UpperCAmelCase  :      Optional[Union[str, TensorType]] = None			,     UpperCAmelCase  :      ChannelDimension = ChannelDimension.FIRST			,     **UpperCAmelCase  :      List[str]			,     )			->	PIL.Image.Image:
     lowerCamelCase__    :      Optional[int]					 =		do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__    :      str					 =		size if size is not None else self.size
     lowerCamelCase__    :      int					 =		get_size_dict(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowerCamelCase__    :      Union[str, Any]					 =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowerCamelCase__    :      Any					 =		resample if resample is not None else self.resample
     lowerCamelCase__    :      Optional[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__    :      Dict					 =		image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__    :      List[Any]					 =		image_std if image_std is not None else self.image_std

     lowerCamelCase__    :      Optional[Any]					 =		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_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.
     lowerCamelCase__    :      Dict					 =		[to_numpy_array(UpperCAmelCase	) for image in images]

     if do_resize:
         lowerCamelCase__    :      Tuple					 =		[self.resize(image=UpperCAmelCase			,     size=UpperCAmelCase			,     resample=UpperCAmelCase	) for image in images]

     if do_rescale:
         lowerCamelCase__    :      Tuple					 =		[self.rescale(image=UpperCAmelCase			,     scale=UpperCAmelCase	) for image in images]

     if do_normalize:
         lowerCamelCase__    :      List[Any]					 =		[self.normalize(image=UpperCAmelCase			,     mean=UpperCAmelCase			,     std=UpperCAmelCase	) for image in images]

     lowerCamelCase__    :      List[str]					 =		[to_channel_dimension_format(UpperCAmelCase			,     UpperCAmelCase	) for image in images]

     lowerCamelCase__    :      Tuple					 =		{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase			,     tensor_type=UpperCAmelCase	)







 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[Tuple] = None	)			->	List[Any]:
     lowerCamelCase__    :      Dict					 =		outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
         if len(UpperCAmelCase	) != len(UpperCAmelCase	):
             raise ValueError(
                 'Make sure that you pass in as many target sizes as the batch dimension of the logits'	)

         if is_torch_tensor(UpperCAmelCase	):
             lowerCamelCase__    :      Dict					 =		target_sizes.numpy()

         lowerCamelCase__    :      int					 =		[]

         for idx in range(len(UpperCAmelCase	)	):
             lowerCamelCase__    :      Optional[int]					 =		torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0	)			,     size=target_sizes[idx]			,     mode='bilinear'			,     align_corners=UpperCAmelCase	)
             lowerCamelCase__    :      Union[str, Any]					 =		resized_logits[0].argmax(dim=0	)
             semantic_segmentation.append(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      Optional[Any]					 =		logits.argmax(dim=1	)
         lowerCamelCase__    :      Dict					 =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

     return semantic_segmentation










"""simple docstring"""
A:   Union[str, Any]								=		{
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    1_0: "a",
    1_1: "b",
    1_2: "c",
    1_3: "d",
    1_4: "e",
    1_5: "f",
}

def       _snake_case						(						UpperCamelCase						:		float						):
 assert type(UpperCamelCase						) in (int, float) and decimal == int(UpperCamelCase						)
 UpperCAmelCase	:					str	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Optional[int]	=	""""""
 UpperCAmelCase	:					List[str]	=	False
 if decimal < 0:
  UpperCAmelCase	:					Any	=	True
  decimal *= -1
 while decimal > 0:
  UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	divmod(UpperCamelCase			,		16						)
  UpperCAmelCase	:					Union[str, Any]	=	values[remainder] + hexadecimal
 UpperCAmelCase	:					int	=	"""0x""" + hexadecimal
 if negative:
  UpperCAmelCase	:					Optional[int]	=	"""-""" + hexadecimal
 return hexadecimal


if __name__ == "__main__":
      import doctest

      doctest.testmod()
