





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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE		:Any				  =    logging.get_logger(__name__)






class 	__magic_name__				(       UpperCamelCase_							):
     UpperCamelCase_  :Tuple								=			["""pixel_values"""]






     def __init__(							self		,	_lowercase = True		,	_lowercase = None		,	_lowercase = PILImageResampling.BILINEAR		,	_lowercase = True		,	_lowercase = 1 / 255		,	_lowercase = True		,	_lowercase = None		,	_lowercase = None		,	**_lowercase		,	)->    None:
         super().__init__(**__SCREAMING_SNAKE_CASE  )
         UpperCamelCase_      =	size if size is not None else {"height": 224, "width": 224}
         UpperCamelCase_      =	get_size_dict(__SCREAMING_SNAKE_CASE  )
         UpperCamelCase_      =	do_resize
         UpperCamelCase_      =	do_rescale
         UpperCamelCase_      =	do_normalize
         UpperCamelCase_      =	size
         UpperCamelCase_      =	resample
         UpperCamelCase_      =	rescale_factor
         UpperCamelCase_      =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCamelCase_      =	image_std if image_std is not None else IMAGENET_STANDARD_STD






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase = PILImageResampling.BILINEAR		,	_lowercase = None		,	**_lowercase		,	)->    np.ndarray:
         UpperCamelCase_      =	get_size_dict(__SCREAMING_SNAKE_CASE  )
         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 resize(
             __SCREAMING_SNAKE_CASE		,	size=(size["height"], size["width"])		,	resample=__SCREAMING_SNAKE_CASE		,	data_format=__SCREAMING_SNAKE_CASE		,	**__SCREAMING_SNAKE_CASE  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase = None		,	**_lowercase  )->    np.ndarray:
         return rescale(__SCREAMING_SNAKE_CASE		,	scale=__SCREAMING_SNAKE_CASE		,	data_format=__SCREAMING_SNAKE_CASE		,	**__SCREAMING_SNAKE_CASE  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = None		,	**_lowercase		,	)->    np.ndarray:
         return normalize(__SCREAMING_SNAKE_CASE		,	mean=__SCREAMING_SNAKE_CASE		,	std=__SCREAMING_SNAKE_CASE		,	data_format=__SCREAMING_SNAKE_CASE		,	**__SCREAMING_SNAKE_CASE  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = ChannelDimension.FIRST		,	**_lowercase		,	)->    Any:
         UpperCamelCase_      =	do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_      =	do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_      =	do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_      =	resample if resample is not None else self.resample
         UpperCamelCase_      =	rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_      =	image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_      =	image_std if image_std is not None else self.image_std

         UpperCamelCase_      =	size if size is not None else self.size
         UpperCamelCase_      =	get_size_dict(__SCREAMING_SNAKE_CASE  )

         UpperCamelCase_      =	make_list_of_images(__SCREAMING_SNAKE_CASE  )

         if not valid_images(__SCREAMING_SNAKE_CASE  ):
             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."  )

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

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

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

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

         UpperCamelCase_      =	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE  ) for image in images]

         UpperCamelCase_      =	{"pixel_values": images}
         return BatchFeature(data=__SCREAMING_SNAKE_CASE		,	tensor_type=__SCREAMING_SNAKE_CASE  )



class 	__magic_name__				:






     def __init__(							self		,	_lowercase  )->    None:
         UpperCamelCase_      =	data
         UpperCamelCase_      =	42
         UpperCamelCase_      =	42




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	None:



    """simple docstring"""






    UpperCamelCase_      =	0
    UpperCamelCase_      =	x




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	None:



    """simple docstring"""






    UpperCamelCase_     ,					UpperCamelCase_      =	find_set(SCREAMING_SNAKE_CASE_ ), find_set(SCREAMING_SNAKE_CASE_ )
    if x == y:
        return

    elif x.rank > y.rank:
        UpperCamelCase_      =	x
    else:
        UpperCamelCase_      =	y
        if x.rank == y.rank:
            y.rank += 1




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	Node:



    """simple docstring"""






    if x != x.parent:
        UpperCamelCase_      =	find_set(x.parent )
    return x.parent




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	set:



    """simple docstring"""






    UpperCamelCase_      =	({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    UpperCamelCase_      =	f"{node.data} is not in {sets}"
    raise ValueError(SCREAMING_SNAKE_CASE_ )




def  lowerCAmelCase(			)->	None:



    """simple docstring"""






    UpperCamelCase_      =	[Node(SCREAMING_SNAKE_CASE_ ) for i in range(6 )]
    for v in vertex:
        make_set(SCREAMING_SNAKE_CASE_ )

    union_set(vertex[0] ,	vertex[1] )
    union_set(vertex[1] ,	vertex[2] )
    union_set(vertex[3] ,	vertex[4] )
    union_set(vertex[3] ,	vertex[5] )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(SCREAMING_SNAKE_CASE_ ).isdisjoint(find_python_set(SCREAMING_SNAKE_CASE_ ) ):
                assert find_set(SCREAMING_SNAKE_CASE_ ) != find_set(SCREAMING_SNAKE_CASE_ )
            else:
                assert find_set(SCREAMING_SNAKE_CASE_ ) == find_set(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
 test_disjoint_set()
