


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__snake_case		:Any   	=  logging.get_logger(__name__)





def   __snake_case			(    _UpperCAmelCase       ):
    if isinstance(__lowerCAmelCase     ,				(list, tuple)       ) and isinstance(videos[0]     ,				(list, tuple)       ) and is_valid_image(videos[0][0]       ):
        return videos

    elif isinstance(__lowerCAmelCase     ,				(list, tuple)       ) and is_valid_image(videos[0]       ):
        return [videos]

    elif is_valid_image(__lowerCAmelCase       ):
        return [[videos]]

    raise ValueError(f'Could not make batched video from {videos}'       )

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Any		      =		['''pixel_values''']



       def __init__(	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str = True  ,	__SCREAMING_SNAKE_CASE							:  List[Any] = None  ,	__SCREAMING_SNAKE_CASE							:  Any = PILImageResampling.BILINEAR  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = True  ,	__SCREAMING_SNAKE_CASE							:  Dict = None  ,	__SCREAMING_SNAKE_CASE							:  List[str] = True  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 1 / 255  ,	__SCREAMING_SNAKE_CASE							:  Any = True  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = True  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  List[str] = None  ,	**__SCREAMING_SNAKE_CASE							:  Dict  ,	):


           '''simple docstring'''

           super().__init__(**_lowerCAmelCase)
           __a		 =       size if size is not None else {'''shortest_edge''': 256}
           __a		 =       get_size_dict(_lowerCAmelCase  ,	default_to_square=_lowerCAmelCase)
           __a		 =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           __a		 =       get_size_dict(_lowerCAmelCase  ,	param_name='''crop_size''')

           __a		 =       do_resize
           __a		 =       size
           __a		 =       do_center_crop
           __a		 =       crop_size
           __a		 =       resample
           __a		 =       do_rescale
           __a		 =       rescale_factor
           __a		 =       offset
           __a		 =       do_normalize
           __a		 =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           __a		 =       image_std if image_std is not None else IMAGENET_STANDARD_STD



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any] = PILImageResampling.BILINEAR  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	**__SCREAMING_SNAKE_CASE							:  Dict  ,	):


           '''simple docstring'''

           __a		 =       get_size_dict(_lowerCAmelCase  ,	default_to_square=_lowerCAmelCase)
           if "shortest_edge" in size:
               __a		 =       get_resize_output_image_size(_lowerCAmelCase  ,	size['''shortest_edge''']  ,	default_to_square=_lowerCAmelCase)
           elif "height" in size and "width" in size:
               __a		 =       (size['''height'''], size['''width'''])
           else:
               raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
           return resize(_lowerCAmelCase  ,	size=_lowerCAmelCase  ,	resample=_lowerCAmelCase  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase)



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[str] = None  ,	**__SCREAMING_SNAKE_CASE							:  List[str]  ,	):


           '''simple docstring'''

           __a		 =       get_size_dict(_lowerCAmelCase)
           if "height" not in size or "width" not in size:
               raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
           return center_crop(_lowerCAmelCase  ,	size=(size['''height'''], size['''width'''])  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase)



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Any = True  ,	__SCREAMING_SNAKE_CASE							:  Tuple = None  ,	**__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


           '''simple docstring'''

           __a		 =       image.astype(np.floataa)
           if offset:
               __a		 =       image - (scale / 2)
           return rescale(_lowerCAmelCase  ,	scale=_lowerCAmelCase  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase)



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = None  ,	**__SCREAMING_SNAKE_CASE							:  List[str]  ,	):


           '''simple docstring'''

           return normalize(_lowerCAmelCase  ,	mean=_lowerCAmelCase  ,	std=_lowerCAmelCase  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase)



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  Dict = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = None  ,	__SCREAMING_SNAKE_CASE							:  List[Any] = None  ,	__SCREAMING_SNAKE_CASE							:  Tuple = None  ,	__SCREAMING_SNAKE_CASE							:  List[str] = None  ,	__SCREAMING_SNAKE_CASE							:  int = None  ,	__SCREAMING_SNAKE_CASE							:  Tuple = None  ,	__SCREAMING_SNAKE_CASE							:  Tuple = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any] = None  ,	__SCREAMING_SNAKE_CASE							:  Tuple = ChannelDimension.FIRST  ,	):


           '''simple docstring'''

           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_center_crop and crop_size is None:
               raise ValueError('''Crop size must be specified if do_center_crop 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.''')

           if offset and not do_rescale:
               raise ValueError('''For offset, do_rescale must also be set to True.''')

           # All transformations expect numpy arrays.
           __a		 =       to_numpy_array(_lowerCAmelCase)

           if do_resize:
               __a		 =       self.resize(image=_lowerCAmelCase  ,	size=_lowerCAmelCase  ,	resample=_lowerCAmelCase)

           if do_center_crop:
               __a		 =       self.center_crop(_lowerCAmelCase  ,	size=_lowerCAmelCase)

           if do_rescale:
               __a		 =       self.rescale(image=_lowerCAmelCase  ,	scale=_lowerCAmelCase  ,	offset=_lowerCAmelCase)

           if do_normalize:
               __a		 =       self.normalize(image=_lowerCAmelCase  ,	mean=_lowerCAmelCase  ,	std=_lowerCAmelCase)

           __a		 =       to_channel_dimension_format(_lowerCAmelCase  ,	_lowerCAmelCase)
           return image





       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  int = None  ,	__SCREAMING_SNAKE_CASE							:  Dict = None  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  Any = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any] = None  ,	__SCREAMING_SNAKE_CASE							:  Any = None  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  int = None  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  Any = None  ,	__SCREAMING_SNAKE_CASE							:  Any = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = ChannelDimension.FIRST  ,	**__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	):


           '''simple docstring'''

           __a		 =       do_resize if do_resize is not None else self.do_resize
           __a		 =       resample if resample is not None else self.resample
           __a		 =       do_center_crop if do_center_crop is not None else self.do_center_crop
           __a		 =       do_rescale if do_rescale is not None else self.do_rescale
           __a		 =       rescale_factor if rescale_factor is not None else self.rescale_factor
           __a		 =       offset if offset is not None else self.offset
           __a		 =       do_normalize if do_normalize is not None else self.do_normalize
           __a		 =       image_mean if image_mean is not None else self.image_mean
           __a		 =       image_std if image_std is not None else self.image_std

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

           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.''')

           __a		 =       make_batched(_lowerCAmelCase)

           __a		 =       [
               [
                   self._preprocess_image(
                       image=_lowerCAmelCase  ,	do_resize=_lowerCAmelCase  ,	size=_lowerCAmelCase  ,	resample=_lowerCAmelCase  ,	do_center_crop=_lowerCAmelCase  ,	crop_size=_lowerCAmelCase  ,	do_rescale=_lowerCAmelCase  ,	rescale_factor=_lowerCAmelCase  ,	offset=_lowerCAmelCase  ,	do_normalize=_lowerCAmelCase  ,	image_mean=_lowerCAmelCase  ,	image_std=_lowerCAmelCase  ,	data_format=_lowerCAmelCase  ,	)
                   for img in video
               ]
               for video in videos
           ]

           __a		 =       {'''pixel_values''': videos}
           return BatchFeature(data=_lowerCAmelCase  ,	tensor_type=_lowerCAmelCase)










from __future__ import annotations





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    print(f'Vertex\tShortest Distance from vertex {src}'       )
    for i, d in enumerate(_UpperCAmelCase       ):
        print(f'{i}\t\t{d}'       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for j in range(_UpperCAmelCase       ):
        __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
            return True
    return False





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [float('''inf'''       )] * vertex_count
    __a		 =       0.0

    for _ in range(vertex_count - 1       ):
        for j in range(_UpperCAmelCase       ):
            __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                __a		 =       distance[u] + w

    __a		 =       check_negative_cycle(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''       )

    return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __snake_case		:Dict   	=  int(input('''Enter number of vertices: ''').strip())
 __snake_case		:Any   	=  int(input('''Enter number of edges: ''').strip())

 __snake_case		:list[dict[str, int]]   	=  [{} for _ in range(E)]

 for i in range(E):
  print('''Edge ''', i + 1)
  __snake_case	,__snake_case	,__snake_case		:int   	=  (
      int(x)
      for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
  )
  __snake_case		:Any   	=  {'''src''': src, '''dst''': dest, '''weight''': weight}

 __snake_case		:List[str]   	=  int(input('''\nEnter shortest path source:''').strip())
 __snake_case		:Optional[Any]   	=  bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)





