


"""simple docstring"""

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
       import PIL

if is_torch_available():
       import torch


UpperCAmelCase__  							=logging.get_logger(__name__)





class    lowerCamelCase__						(		_a    ):
      a :     Optional[Any]											=		["""pixel_values"""]
      def __init__(		self	:	List[Any]      ,       A_	:	bool = True      ,       A_	:	Dict[str, int] = None      ,       A_	:	PILImageResampling = PILImageResampling.BILINEAR      ,       A_	:	bool = True      ,       A_	:	Union[int, float] = 1 / 2_5_5      ,       A_	:	bool = True      ,       A_	:	Dict[str, int] = None      ,       A_	:	bool = True      ,       **A_	:	Optional[int]      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)
       __lowercase									=	size if size is not None else {"""shortest_edge""": 2_2_4}
       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)
       __lowercase									=	crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
       __lowercase									=	get_size_dict(A_      ,       param_name="""crop_size"""							)

       __lowercase									=	do_resize
       __lowercase									=	size
       __lowercase									=	resample
       __lowercase									=	do_rescale
       __lowercase									=	rescale_factor
       __lowercase									=	do_center_crop
       __lowercase									=	crop_size
       __lowercase									=	do_flip_channel_order
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	np.ndarray      ,       A_	:	Dict[str, int]      ,       A_	:	PILImageResampling = PIL.Image.BILINEAR      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	Union[str, Any]      ,       ):






       '''simple docstring'''


       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)
       if "shortest_edge" not in size:
        raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''							)
       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	np.ndarray      ,       A_	:	Dict[str, int]      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	Tuple      ,       ):






       '''simple docstring'''


       __lowercase									=	get_size_dict(A_							)
       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()}'''							)
       return center_crop(A_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	np.ndarray      ,       A_	:	Union[int, float]      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	Tuple      ,       ):






       '''simple docstring'''


       return rescale(A_      ,       scale=A_      ,       data_format=A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	np.ndarray      ,       A_	:	Optional[Union[str, ChannelDimension]] = None							):






       '''simple docstring'''


       return flip_channel_order(A_      ,       data_format=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	ImageInput      ,       A_	:	bool = None      ,       A_	:	Dict[str, int] = None      ,       A_	:	PILImageResampling = None      ,       A_	:	bool = None      ,       A_	:	float = None      ,       A_	:	bool = None      ,       A_	:	Dict[str, int] = None      ,       A_	:	bool = None      ,       A_	:	Optional[Union[str, TensorType]] = None      ,       A_	:	ChannelDimension = ChannelDimension.FIRST      ,       **A_	:	Tuple      ,       ):






       '''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_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_center_crop if do_center_crop is not None else self.do_center_crop
       __lowercase									=	(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       __lowercase									=	size if size is not None else self.size
       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)
       __lowercase									=	crop_size if crop_size is not None else self.crop_size
       __lowercase									=	get_size_dict(A_      ,       param_name="""crop_size"""							)

       __lowercase									=	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_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

       if do_center_crop and crop_size is None:
        raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

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

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

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

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

       # the pretrained checkpoints assume images are BGR, not RGB
       if do_flip_channel_order:
        __lowercase									=	[self.flip_channel_order(image=A_							) for image in images]

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

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






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	List[Any]      ,       A_	:	List[Tuple] = None							):






       '''simple docstring'''


       __lowercase									=	outputs.logits

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

        if is_torch_tensor(A_							):
         __lowercase									=	target_sizes.numpy()

        __lowercase									=	[]

        for idx in range(len(A_							)							):
         __lowercase									=	torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0							)      ,       size=target_sizes[idx]      ,       mode="""bilinear"""      ,       align_corners=A_							)
         __lowercase									=	resized_logits[0].argmax(dim=0							)
         semantic_segmentation.append(A_							)
       else:
        __lowercase									=	logits.argmax(dim=1							)
        __lowercase									=	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

       return semantic_segmentation




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_      (				UpperCamelCase__	: list[float] ):






 """simple docstring"""







 if len(UpperCamelCase__ ) < 2:
  raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
 if any(i <= 0 for i in nums ):
  raise ValueError("""All values must be greater than 0""" )
 __lowercase									=	nums.copy()
 copy_nums.sort()
 return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
