'''simple docstring'''




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


_a   :	str 							=   logging.get_logger(__name__)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple[int, int]:





       """simple docstring"""


       def constraint_to_multiple_of(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=0							,    lowerCamelCase__=None						):
              __UpperCAmelCase  :				str       				=		round(val / multiple						) * multiple

              if max_val is not None and x > max_val:
                     __UpperCAmelCase  :				List[str]       				=		math.floor(val / multiple						) * multiple

              if x < min_val:
                     __UpperCAmelCase  :				int       				=		math.ceil(val / multiple						) * multiple

              return x

       __UpperCAmelCase  :				List[str]       				=		(output_size, output_size) if isinstance(lowerCamelCase__							,    lowerCamelCase__						) else output_size

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		get_image_size(lowerCamelCase__						)
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		output_size

       # determine new height and width
       __UpperCAmelCase  :				int       				=		output_height / input_height
       __UpperCAmelCase  :				List[str]       				=		output_width / input_width

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

       __UpperCAmelCase  :				str       				=		constraint_to_multiple_of(scale_height * input_height							,    multiple=lowerCamelCase__						)
       __UpperCAmelCase  :				int       				=		constraint_to_multiple_of(scale_width * input_width							,    multiple=lowerCamelCase__						)

       return (new_height, new_width)






class 		__A       (__magic_name__   ):
       snake_case       :int				    =					["pixel_values"]






       def __init__(      self				,     UpperCamelCase_ = True				,     UpperCamelCase_ = None				,     UpperCamelCase_ = PILImageResampling.BILINEAR				,     UpperCamelCase_ = False				,     UpperCamelCase_ = 1				,     UpperCamelCase_ = True				,     UpperCamelCase_ = 1 / 2_55				,     UpperCamelCase_ = True				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              super().__init__(**UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[Any]       				=		size if size is not None else {"height": 3_84, "width": 3_84}
              __UpperCAmelCase  :				int       				=		get_size_dict(UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		do_resize
              __UpperCAmelCase  :				List[str]       				=		size
              __UpperCAmelCase  :				Dict       				=		keep_aspect_ratio
              __UpperCAmelCase  :				Optional[Any]       				=		ensure_multiple_of
              __UpperCAmelCase  :				List[Any]       				=		resample
              __UpperCAmelCase  :				str       				=		do_rescale
              __UpperCAmelCase  :				str       				=		rescale_factor
              __UpperCAmelCase  :				Tuple       				=		do_normalize
              __UpperCAmelCase  :				Dict       				=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              __UpperCAmelCase  :				Dict       				=		image_std if image_std is not None else IMAGENET_STANDARD_STD






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = False				,     UpperCamelCase_ = 1				,     UpperCamelCase_ = PILImageResampling.BICUBIC				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				List[Any]       				=		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()}"""      )
              __UpperCAmelCase  :				Optional[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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              return rescale(UpperCamelCase_				,     scale=UpperCamelCase_				,     data_format=UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              return normalize(UpperCamelCase_				,     mean=UpperCamelCase_				,     std=UpperCamelCase_				,     data_format=UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = ChannelDimension.FIRST				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				List[str]       				=		do_resize if do_resize is not None else self.do_resize
              __UpperCAmelCase  :				List[str]       				=		size if size is not None else self.size
              __UpperCAmelCase  :				Any       				=		get_size_dict(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
              __UpperCAmelCase  :				Dict       				=		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
              __UpperCAmelCase  :				Any       				=		resample if resample is not None else self.resample
              __UpperCAmelCase  :				Any       				=		do_rescale if do_rescale is not None else self.do_rescale
              __UpperCAmelCase  :				List[str]       				=		rescale_factor if rescale_factor is not None else self.rescale_factor
              __UpperCAmelCase  :				Dict       				=		do_normalize if do_normalize is not None else self.do_normalize
              __UpperCAmelCase  :				List[Any]       				=		image_mean if image_mean is not None else self.image_mean
              __UpperCAmelCase  :				int       				=		image_std if image_std is not None else self.image_std

              __UpperCAmelCase  :				int       				=		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.
              __UpperCAmelCase  :				Optional[int]       				=		[to_numpy_array(UpperCamelCase_      ) for image in images]

              if do_resize:
                     __UpperCAmelCase  :				str       				=		[self.resize(image=UpperCamelCase_				,     size=UpperCamelCase_				,     resample=UpperCamelCase_      ) for image in images]

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

              if do_normalize:
                     __UpperCAmelCase  :				List[str]       				=		[self.normalize(image=UpperCamelCase_				,     mean=UpperCamelCase_				,     std=UpperCamelCase_      ) for image in images]

              __UpperCAmelCase  :				Dict       				=		[to_channel_dimension_format(UpperCamelCase_				,     UpperCamelCase_      ) for image in images]

              __UpperCAmelCase  :				List[Any]       				=		{"pixel_values": images}
              return BatchFeature(data=UpperCamelCase_				,     tensor_type=UpperCamelCase_      )







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				List[str]       				=		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_      ):
                            __UpperCAmelCase  :				List[str]       				=		target_sizes.numpy()

                     __UpperCAmelCase  :				Optional[int]       				=		[]

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

              return semantic_segmentation







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       return not any(
           neighbour == 1 and colored_vertices[i] == color
           for i, neighbour in enumerate(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       if index == len(lowerCamelCase__						):
              return True

       # Recursive Step
       for i in range(lowerCamelCase__						):
              if valid_coloring(graph[index]							,    lowerCamelCase__							,    lowerCamelCase__						):
                     # Color current vertex
                     __UpperCAmelCase  :				List[str]       				=		i
                     # Validate coloring
                     if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    index + 1						):
                            return True
                     # Backtrack
                     __UpperCAmelCase  :				Any       				=		-1
       return False


def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       list[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		[-1] * len(lowerCamelCase__						)

       if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    0						):
              return colored_vertices

       return []






