
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import 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_vision_available, logging


if is_vision_available():
		import PIL


lowercase  :					Tuple													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			List[Any])					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					b.T
							__UpperCamelCase	:   Tuple			   =					np.sum(np.square(_lowerCamelCase)						,  axis=1)
							__UpperCamelCase	:   str			   =					np.sum(np.square(_lowerCamelCase)						,  axis=0)
							__UpperCamelCase	:   int			   =					np.matmul(_lowerCamelCase						,  _lowerCamelCase)
							__UpperCamelCase	:   Tuple			   =					aa[:, None] - 2 * ab + ba[None, :]
							return d




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Union[str, Any])					->    Tuple:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					x.reshape(-1						,  3)
							__UpperCamelCase	:   str			   =					squared_euclidean_distance(_lowerCamelCase						,  _lowerCamelCase)
							return np.argmin(_lowerCamelCase						,  axis=1)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ['pixel_values']







	def __init__(					self		:List[str]     ,   a		:Optional[Union[List[List[int]], np.ndarray]] = None     ,   a		:bool = True     ,   a		:Dict[str, int] = None     ,   a		:PILImageResampling = PILImageResampling.BILINEAR     ,   a		:bool = True     ,   a		:bool = True     ,   **a		:Any     ,   )		-> None:
								super().__init__(**a						)
								__UpperCamelCase	:   Optional[Any]			   =					size if size is not None else {"height": 2_5_6, "width": 2_5_6}
								__UpperCamelCase	:   str			   =					get_size_dict(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					np.array(a						) if clusters is not None else None
								__UpperCamelCase	:   Optional[Any]			   =					do_resize
								__UpperCamelCase	:   Tuple			   =					size
								__UpperCamelCase	:   Dict			   =					resample
								__UpperCamelCase	:   Any			   =					do_normalize
								__UpperCamelCase	:   Any			   =					do_color_quantize







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:np.ndarray     ,   a		:Dict[str, int]     ,   a		:PILImageResampling = PILImageResampling.BILINEAR     ,   a		:Optional[Union[str, ChannelDimension]] = None     ,   **a		:int     ,   )		-> np.ndarray:
								__UpperCamelCase	:   List[str]			   =					get_size_dict(a						)
								if "height" not in size or "width" not in size:
															raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'						)
								return resize(
								    a     ,   size=(size["height"], size["width"])     ,   resample=a     ,   data_format=a     ,   **a						)







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:np.ndarray     ,   a		:Optional[Union[str, ChannelDimension]] = None     ,   )		-> np.ndarray:
								__UpperCamelCase	:   Tuple			   =					rescale(image=a     ,   scale=1 / 127.5     ,   data_format=a						)
								__UpperCamelCase	:   Any			   =					image - 1
								return image







	def    _lowerCamelCase				(					self		:str     ,   a		:ImageInput     ,   a		:bool = None     ,   a		:Dict[str, int] = None     ,   a		:PILImageResampling = None     ,   a		:bool = None     ,   a		:Optional[bool] = None     ,   a		:Optional[Union[List[List[int]], np.ndarray]] = None     ,   a		:Optional[Union[str, TensorType]] = None     ,   a		:Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST     ,   **a		:int     ,   )		-> PIL.Image.Image:
								__UpperCamelCase	:   Tuple			   =					do_resize if do_resize is not None else self.do_resize
								__UpperCamelCase	:   int			   =					size if size is not None else self.size
								__UpperCamelCase	:   List[Any]			   =					get_size_dict(a						)
								__UpperCamelCase	:   Optional[Any]			   =					resample if resample is not None else self.resample
								__UpperCamelCase	:   List[Any]			   =					do_normalize if do_normalize is not None else self.do_normalize
								__UpperCamelCase	:   Optional[int]			   =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								__UpperCamelCase	:   List[str]			   =					clusters if clusters is not None else self.clusters
								__UpperCamelCase	:   List[Any]			   =					np.array(a						)

								__UpperCamelCase	:   str			   =					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 or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."						)

								if do_color_quantize and clusters is None:
															raise ValueError("Clusters must be specified if do_color_quantize is True."						)

								# All transformations expect numpy arrays.
								__UpperCamelCase	:   str			   =					[to_numpy_array(a						) for image in images]

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

								if do_normalize:
															__UpperCamelCase	:   Optional[Any]			   =					[self.normalize(image=a						) for image in images]

								if do_color_quantize:
															__UpperCamelCase	:   Dict			   =					[to_channel_dimension_format(a     ,   ChannelDimension.LAST						) for image in images]
															# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
															__UpperCamelCase	:   List[str]			   =					np.array(a						)
															__UpperCamelCase	:   int			   =					color_quantize(a     ,   a						).reshape(images.shape[:-1]						)

															# flatten to (batch_size, height*width)
															__UpperCamelCase	:   Union[str, Any]			   =					images.shape[0]
															__UpperCamelCase	:   List[Any]			   =					images.reshape(a     ,   -1						)

															# We need to convert back to a list of images to keep consistent behaviour across processors.
															__UpperCamelCase	:   Tuple			   =					list(a						)
								else:
															__UpperCamelCase	:   Dict			   =					[to_channel_dimension_format(a     ,   a						) for image in images]

								__UpperCamelCase	:   int			   =					{"input_ids": images}
								return BatchFeature(data=a     ,   tensor_type=a						)

class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					{}







	def    _lowerCamelCase				(					self		:str						)		-> None:
								print(self.vertex						)
								for i in self.vertex:
															print(a     ,   " -> "     ,   " -> ".join([str(a						) for j in self.vertex[i]]						)						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:int						)		-> None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
															self.vertex[from_vertex].append(a						)
								else:
															# else make a new vertex
															__UpperCamelCase	:   Optional[Any]			   =					[to_vertex]







	def    _lowerCamelCase				(					self		:Tuple						)		-> None:
								# visited array for storing already visited nodes
								__UpperCamelCase	:   Dict			   =					[False] * len(self.vertex						)

								# call the recursive helper function
								for i in range(len(self.vertex						)						):
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:int     ,   a		:list						)		-> None:
								# mark start vertex as visited
								__UpperCamelCase	:   int			   =					True

								print(a     ,   end=" "						)

								# Recur for all the vertices that are adjacent to this node
								for i in self.vertex:
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)


if __name__ == "__main__":
		lowercase  :					Dict													=       Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3