






"""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 (
    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


_UpperCamelCase		:  Optional[int]						=		logging.get_logger(__name__)


if is_vision_available():
    import PIL

class        a     (   a_	):
    UpperCAmelCase_ :     Dict	     =["pixel_values"]







    def __init__(			self							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = PILImageResampling.BICUBIC							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = True							,		_lowerCamelCase = 1 / 2_5_5							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      super().__init__(**_lowerCamelCase       )
      lowercase							    =       size if size is not None else {'shortest_edge': 2_2_4}
      lowercase							    =       get_size_dict(_lowerCamelCase							,		default_to_square=_lowerCamelCase       )
      lowercase							    =       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
      lowercase							    =       get_size_dict(_lowerCamelCase							,		default_to_square=_lowerCamelCase							,		param_name='crop_size'       )

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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = PILImageResampling.BICUBIC							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      lowercase							    =       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()}'       )
      lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      return rescale(_lowerCamelCase							,		scale=_lowerCamelCase							,		data_format=_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      return normalize(_lowerCamelCase							,		mean=_lowerCamelCase							,		std=_lowerCamelCase							,		data_format=_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = ChannelDimension.FIRST							,		**_lowerCamelCase							,		):
      lowercase							    =       do_resize if do_resize is not None else self.do_resize
      lowercase							    =       size if size is not None else self.size
      lowercase							    =       get_size_dict(_lowerCamelCase							,		param_name='size'							,		default_to_square=_lowerCamelCase       )
      lowercase							    =       resample if resample is not None else self.resample
      lowercase							    =       do_center_crop if do_center_crop is not None else self.do_center_crop
      lowercase							    =       crop_size if crop_size is not None else self.crop_size
      lowercase							    =       get_size_dict(_lowerCamelCase							,		param_name='crop_size'							,		default_to_square=_lowerCamelCase       )
      lowercase							    =       do_rescale if do_rescale is not None else self.do_rescale
      lowercase							    =       rescale_factor if rescale_factor is not None else self.rescale_factor
      lowercase							    =       do_normalize if do_normalize is not None else self.do_normalize
      lowercase							    =       image_mean if image_mean is not None else self.image_mean
      lowercase							    =       image_std if image_std is not None else self.image_std
      lowercase							    =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      lowercase							    =       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:
        lowercase							    =       [convert_to_rgb(_lowerCamelCase       ) for image in images]

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

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

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

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

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

      lowercase							    =       [to_channel_dimension_format(_lowerCamelCase							,		_lowerCamelCase       ) for image in images]

      lowercase							    =       {'pixel_values': images}
      return BatchFeature(data=_lowerCamelCase							,		tensor_type=_lowerCamelCase       )










"""simple docstring"""



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_UpperCamelCase		:  Optional[Any]						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase = True       ):
      lowercase							    =       {}  # dictionary of lists
      lowercase							    =       directed







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):

      if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       [source_vertex]
      else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       []

      return self







    def __repr__(			self       ):
      return pformat(self.adj_list       )


