




"""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


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

class 				snake_case_			(					snake_case_			):







   """simple docstring"""




   A_				      =       ['''pixel_values''']







   def __init__(		self       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = PILImageResampling.BILINEAR       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = 1 / 2_5_5       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = True       ,					**lowerCamelCase_       ,					)				->   Union[str, Any]:
       super().__init__(**lowercase__)
       UpperCamelCase              =	size if size is not None else {"""shortest_edge""": 2_2_4}
       UpperCamelCase              =	get_size_dict(lowercase__       ,					default_to_square=lowercase__)
       UpperCamelCase              =	crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
       UpperCamelCase              =	get_size_dict(lowercase__       ,					param_name='''crop_size''')

       UpperCamelCase              =	do_resize
       UpperCamelCase              =	size
       UpperCamelCase              =	resample
       UpperCamelCase              =	do_rescale
       UpperCamelCase              =	rescale_factor
       UpperCamelCase              =	do_center_crop
       UpperCamelCase              =	crop_size
       UpperCamelCase              =	do_flip_channel_order







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = PIL.Image.BILINEAR       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   Any:
       UpperCamelCase              =	get_size_dict(lowercase__       ,					default_to_square=lowercase__)
       if "shortest_edge" not in size:
           raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}')
       UpperCamelCase              =	get_resize_output_image_size(lowercase__       ,					size=size['''shortest_edge''']       ,					default_to_square=lowercase__)
       return resize(lowercase__       ,					size=lowercase__       ,					resample=lowercase__       ,					data_format=lowercase__       ,					**lowercase__)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   Optional[int]:
       UpperCamelCase              =	get_size_dict(lowercase__)
       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(lowercase__       ,					size=(size['''height'''], size['''width'''])       ,					data_format=lowercase__       ,					**lowercase__)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   List[str]:
       return rescale(lowercase__       ,					scale=lowercase__       ,					data_format=lowercase__       ,					**lowercase__)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Dict:
       return flip_channel_order(lowercase__       ,					data_format=lowercase__)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = ChannelDimension.FIRST       ,					**lowerCamelCase_       ,					)				->   str:
       UpperCamelCase              =	do_resize if do_resize is not None else self.do_resize
       UpperCamelCase              =	resample if resample is not None else self.resample
       UpperCamelCase              =	do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase              =	rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase              =	do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase              =	(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       UpperCamelCase              =	size if size is not None else self.size
       UpperCamelCase              =	get_size_dict(lowercase__       ,					default_to_square=lowercase__)
       UpperCamelCase              =	crop_size if crop_size is not None else self.crop_size
       UpperCamelCase              =	get_size_dict(lowercase__       ,					param_name='''crop_size''')

       UpperCamelCase              =	make_list_of_images(lowercase__)

       if not valid_images(lowercase__):
           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.
       UpperCamelCase              =	[to_numpy_array(lowercase__) for image in images]

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

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

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

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

       UpperCamelCase              =	[to_channel_dimension_format(lowercase__       ,					lowercase__) for image in images]

       UpperCamelCase              =	{"""pixel_values""": images}
       return BatchFeature(data=lowercase__       ,					tensor_type=lowercase__)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   List[Any]:
       UpperCamelCase              =	outputs.logits

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

           if is_torch_tensor(lowercase__):
               UpperCamelCase              =	target_sizes.numpy()

           UpperCamelCase              =	[]

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

       return semantic_segmentation





'''simple docstring'''


from __future__ import annotations



def  _SCREAMING_SNAKE_CASE     (      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"""				)
     a_     :							int     						=  nums.copy()
     copy_nums.sort()
     return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
