

"""simple docstring"""




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_  :      Optional[Any]							=      logging.get_logger(__name__)


class 		_A							(      __a       ):
      __a             =						['pixel_values']





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





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       np.ndarray:
         lowerCamelCase__						       =      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        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__		) ->       np.ndarray:
         return rescale(SCREAMING_SNAKE_CASE__     ,    scale=SCREAMING_SNAKE_CASE__     ,    data_format=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__     ,    mean=SCREAMING_SNAKE_CASE__     ,    std=SCREAMING_SNAKE_CASE__     ,    data_format=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)





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

         lowerCamelCase__						       =      size if size is not None else self.size
         lowerCamelCase__						       =      get_size_dict(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      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.
         lowerCamelCase__						       =      [to_numpy_array(SCREAMING_SNAKE_CASE__		) for image in images]

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

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

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

         lowerCamelCase__						       =      [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) for image in images]

         lowerCamelCase__						       =      {"pixel_values": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,    tensor_type=SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




SCREAMING_SNAKE_CASE_  :      Optional[int]							=      {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

SCREAMING_SNAKE_CASE_  :      str							=      {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}



def 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   list:




   """simple docstring"""
   lowerCamelCase__						       =      True
   lowerCamelCase__						       =      []

   for neighbour in graph[vert]:
      if not visited[neighbour]:
         connected_verts += dfs(A__						,     A__						,     A__ )

   return [vert, *connected_verts]



def 						UpperCAmelCase__ (					A__ )   ->   list:




   """simple docstring"""
   lowerCamelCase__						       =      len(A__ )
   lowerCamelCase__						       =      graph_size * [False]
   lowerCamelCase__						       =      []

   for i in range(A__ ):
      if not visited[i]:
         lowerCamelCase__						       =      dfs(A__						,     A__						,     A__ )
         components_list.append(A__ )

   return components_list


if __name__ == "__main__":
     import doctest

     doctest.testmod()




