






"""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


lowerCAmelCase__    						=			logging.get_logger(__name__)


class _lowerCAmelCase						(    _A       ):
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				["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_   ,				)		->							None:
										super().__init__(**__lowerCAmelCase							)
										_SCREAMING_SNAKE_CASE   :		str       		=    size if size is not None else {'shortest_edge': 2_2_4}
										_SCREAMING_SNAKE_CASE   :		int       		=    get_size_dict(__lowerCAmelCase   ,				default_to_square=__lowerCAmelCase							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    get_size_dict(__lowerCAmelCase   ,				param_name='crop_size'							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    do_resize
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    size
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    resample
										_SCREAMING_SNAKE_CASE   :		int       		=    do_rescale
										_SCREAMING_SNAKE_CASE   :		Dict       		=    rescale_factor
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    do_center_crop
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    crop_size
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    do_flip_channel_order







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = PIL.Image.BILINEAR   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							np.ndarray:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    get_size_dict(__lowerCAmelCase   ,				default_to_square=__lowerCAmelCase							)
										if "shortest_edge" not in size:
														raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""							)
										_SCREAMING_SNAKE_CASE   :		str       		=    get_resize_output_image_size(__lowerCAmelCase   ,				size=size['shortest_edge']   ,				default_to_square=__lowerCAmelCase							)
										return resize(__lowerCAmelCase   ,				size=__lowerCAmelCase   ,				resample=__lowerCAmelCase   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							np.ndarray:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_size_dict(__lowerCAmelCase							)
										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(__lowerCAmelCase   ,				size=(size['height'], size['width'])   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							Optional[int]:
										return rescale(__lowerCAmelCase   ,				scale=__lowerCAmelCase   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							np.ndarray:
										return flip_channel_order(__lowerCAmelCase   ,				data_format=__lowerCAmelCase							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = ChannelDimension.FIRST   ,				**lowerCAmelCase_   ,				)		->							PIL.Image.Image:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE   :		int       		=    resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    do_rescale if do_rescale is not None else self.do_rescale
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    rescale_factor if rescale_factor is not None else self.rescale_factor
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    do_center_crop if do_center_crop is not None else self.do_center_crop
										_SCREAMING_SNAKE_CASE   :		Dict       		=    (
										    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
										)

										_SCREAMING_SNAKE_CASE   :		str       		=    size if size is not None else self.size
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    get_size_dict(__lowerCAmelCase   ,				default_to_square=__lowerCAmelCase							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    crop_size if crop_size is not None else self.crop_size
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_size_dict(__lowerCAmelCase   ,				param_name='crop_size'							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    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:
														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.
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    [to_numpy_array(__lowerCAmelCase							) for image in images]

										if do_resize:
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    [self.resize(image=__lowerCAmelCase   ,				size=__lowerCAmelCase   ,				resample=__lowerCAmelCase							) for image in images]

										if do_center_crop:
														_SCREAMING_SNAKE_CASE   :		str       		=    [self.center_crop(image=__lowerCAmelCase   ,				size=__lowerCAmelCase							) for image in images]

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

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

										_SCREAMING_SNAKE_CASE   :		int       		=    [to_channel_dimension_format(__lowerCAmelCase   ,				__lowerCAmelCase							) for image in images]

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    {'pixel_values': images}
										return BatchFeature(data=__lowerCAmelCase   ,				tensor_type=__lowerCAmelCase							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Any       		=    outputs.logits

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

														if is_torch_tensor(__lowerCAmelCase							):
																		_SCREAMING_SNAKE_CASE   :		Tuple       		=    target_sizes.numpy()

														_SCREAMING_SNAKE_CASE   :		Dict       		=    []

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

										return semantic_segmentation






from __future__ import annotations

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:str				)  ->     set[str]:
   _A  ,    _A      =       set(_snake_case				), [start]

   while stack:
      _A      =       stack.pop()
      explored.add(_snake_case				)
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]				):
         if adj not in explored:
            stack.append(_snake_case				)
   return explored


UpperCAmelCase_		 =					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(depth_first_search(G, """A"""))



