




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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase__					:  int      							=				logging.get_logger(__name__)


if is_vision_available():
   import PIL





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ["pixel_values"]



 def __init__(				self    :						List[str]							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Dict[str, int] = None							,							_lowerCAmelCase    :						PILImageResampling = PILImageResampling.BICUBIC							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Dict[str, int] = None							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Union[int, float] = 1 / 255							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Optional[Union[float, List[float]]] = None							,							_lowerCAmelCase    :						Optional[Union[float, List[float]]] = None							,							_lowerCAmelCase    :						bool = True							,							**_lowerCAmelCase    :						Optional[Any]							,							):
       super().__init__(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     size if size is not None else {'shortest_edge': 224}
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase							,							default_to_square=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     crop_size if crop_size is not None else {'height': 224, 'width': 224}
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase							,							default_to_square=_lowerCAmelCase							,							param_name='crop_size'		)

       SCREAMING_SNAKE_CASE_      		=     do_resize
       SCREAMING_SNAKE_CASE_      		=     size
       SCREAMING_SNAKE_CASE_      		=     resample
       SCREAMING_SNAKE_CASE_      		=     do_center_crop
       SCREAMING_SNAKE_CASE_      		=     crop_size
       SCREAMING_SNAKE_CASE_      		=     do_rescale
       SCREAMING_SNAKE_CASE_      		=     rescale_factor
       SCREAMING_SNAKE_CASE_      		=     do_normalize
       SCREAMING_SNAKE_CASE_      		=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       SCREAMING_SNAKE_CASE_      		=     image_std if image_std is not None else OPENAI_CLIP_STD
       SCREAMING_SNAKE_CASE_      		=     do_convert_rgb



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						np.ndarray							,							_lowerCAmelCase    :						Dict[str, int]							,							_lowerCAmelCase    :						PILImageResampling = PILImageResampling.BICUBIC							,							_lowerCAmelCase    :						Optional[Union[str, ChannelDimension]] = None							,							**_lowerCAmelCase    :						List[str]							,							):
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase							,							default_to_square=_lowerCAmelCase		)
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"		)
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						np.ndarray							,							_lowerCAmelCase    :						Dict[str, int]							,							_lowerCAmelCase    :						Optional[Union[str, ChannelDimension]] = None							,							**_lowerCAmelCase    :						Any							,							):
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase		)
       if "height" not in size or "width" not in size:
             raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"		)
       return center_crop(_lowerCAmelCase							,							size=(size['height'], size['width'])							,							data_format=_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						np.ndarray							,							_lowerCAmelCase    :						Union[int, float]							,							_lowerCAmelCase    :						Optional[Union[str, ChannelDimension]] = None							,							**_lowerCAmelCase    :						Any							,							):
       return rescale(_lowerCAmelCase							,							scale=_lowerCAmelCase							,							data_format=_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						np.ndarray							,							_lowerCAmelCase    :						Union[float, List[float]]							,							_lowerCAmelCase    :						Union[float, List[float]]							,							_lowerCAmelCase    :						Optional[Union[str, ChannelDimension]] = None							,							**_lowerCAmelCase    :						List[Any]							,							):
       return normalize(_lowerCAmelCase							,							mean=_lowerCAmelCase							,							std=_lowerCAmelCase							,							data_format=_lowerCAmelCase							,							**_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						ImageInput							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						Dict[str, int] = None							,							_lowerCAmelCase    :						PILImageResampling = None							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						int = None							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						float = None							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						Optional[Union[float, List[float]]] = None							,							_lowerCAmelCase    :						Optional[Union[float, List[float]]] = None							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						Optional[Union[str, TensorType]] = None							,							_lowerCAmelCase    :						Optional[ChannelDimension] = ChannelDimension.FIRST							,							**_lowerCAmelCase    :						Union[str, Any]							,							):
       SCREAMING_SNAKE_CASE_      		=     do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE_      		=     size if size is not None else self.size
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase							,							param_name='size'							,							default_to_square=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE_      		=     do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE_      		=     crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase							,							param_name='crop_size'							,							default_to_square=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE_      		=     rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE_      		=     do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE_      		=     image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE_      		=     image_std if image_std is not None else self.image_std
       SCREAMING_SNAKE_CASE_      		=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       SCREAMING_SNAKE_CASE_      		=     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_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.'		)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             SCREAMING_SNAKE_CASE_      		=     [convert_to_rgb(_lowerCAmelCase		) for image in images]

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

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

       if do_center_crop:
             SCREAMING_SNAKE_CASE_      		=     [self.center_crop(image=_lowerCAmelCase							,							size=_lowerCAmelCase		) for image in images]

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

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

       SCREAMING_SNAKE_CASE_      		=     [to_channel_dimension_format(_lowerCAmelCase							,							_lowerCAmelCase		) for image in images]

       SCREAMING_SNAKE_CASE_      		=     {'pixel_values': images}
       return BatchFeature(data=_lowerCAmelCase							,							tensor_type=_lowerCAmelCase		)





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     {}



 def  lowerCAmelCase_    (				self    :						List[str]		):
       print(self.vertex		)
       for i in self.vertex:
             print(_lowerCAmelCase							,							' -> '							,							' -> '.join([str(_lowerCAmelCase		) for j in self.vertex[i]]		)		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase		)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_      		=     [to_vertex]



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_      		=     [False] * len(self.vertex		)

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




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						list		):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_      		=     True

       print(_lowerCAmelCase							,							end=' '		)

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


if __name__ == "__main__":
   lowerCamelCase__					:  List[Any]      							=				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