
"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[Any]   =			['''pixel_values''']






       def __init__(  self :							Dict       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Optional[Dict[str, int]] = None       ,      UpperCAmelCase_ :							PILImageResampling = PILImageResampling.BICUBIC       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Union[int, float] = 1 / 255       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      **UpperCAmelCase_ :							int       ,      )->  None:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_			)
              UpperCamelCase					 =		size if size is not None else {"height": 224, "width": 224}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              UpperCamelCase					 =		crop_size if crop_size is not None else {"height": 224, "width": 224}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      default_to_square=UpperCAmelCase_       ,      param_name="crop_size"			)

              UpperCamelCase					 =		do_resize
              UpperCamelCase					 =		do_rescale
              UpperCamelCase					 =		do_normalize
              UpperCamelCase					 =		do_center_crop
              UpperCamelCase					 =		crop_size
              UpperCamelCase					 =		size
              UpperCamelCase					 =		resample
              UpperCamelCase					 =		rescale_factor
              UpperCamelCase					 =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
              UpperCamelCase					 =		image_std if image_std is not None else IMAGENET_DEFAULT_STD






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Dict[str, int]       ,      UpperCAmelCase_ :							PILImageResampling = PILImageResampling.BILINEAR       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							Dict       ,      )->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)

              if "shortest_edge" in size:
                     UpperCamelCase					 =		get_resize_output_image_size(UpperCAmelCase_       ,      size=size["shortest_edge"]       ,      default_to_square=UpperCAmelCase_			)
                     # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
              elif "height" in size and "width" in size:
                     UpperCamelCase					 =		(size["height"], size["width"])
              else:
                     raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"			)
              return resize(UpperCAmelCase_       ,      size=UpperCAmelCase_       ,      resample=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Dict[str, int]       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							List[str]       ,      )->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              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(UpperCAmelCase_       ,      size=(size["height"], size["width"])       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							float       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							Tuple			)->  np.ndarray:







              """simple docstring"""
              return rescale(UpperCAmelCase_       ,      scale=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Union[float, List[float]]       ,      UpperCAmelCase_ :							Union[float, List[float]]       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							Union[str, Any]       ,      )->  np.ndarray:







              """simple docstring"""
              return normalize(UpperCAmelCase_       ,      mean=UpperCAmelCase_       ,      std=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							ImageInput       ,      UpperCAmelCase_ :							Optional[bool] = None       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							PILImageResampling = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							int = None       ,      UpperCAmelCase_ :							Optional[bool] = None       ,      UpperCAmelCase_ :							Optional[float] = None       ,      UpperCAmelCase_ :							Optional[bool] = None       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							Optional[Union[str, TensorType]] = None       ,      UpperCAmelCase_ :							Union[str, ChannelDimension] = ChannelDimension.FIRST       ,      **UpperCAmelCase_ :							Dict       ,      )->  BatchFeature:







              """simple docstring"""
              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					 =		do_center_crop if do_center_crop is not None else self.do_center_crop
              UpperCamelCase					 =		crop_size if crop_size is not None else self.crop_size
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      param_name="crop_size"       ,      default_to_square=UpperCAmelCase_			)
              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(UpperCAmelCase_			)

              if not is_batched(UpperCAmelCase_			):
                     UpperCamelCase					 =		[images]

              if not valid_images(UpperCAmelCase_			):
                     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."			)

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

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

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

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

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

              UpperCamelCase					 =		[to_channel_dimension_format(UpperCAmelCase_       ,      UpperCAmelCase_			) for image in images]

              UpperCamelCase					 =		{"pixel_values": images}
              return BatchFeature(data=UpperCAmelCase_       ,      tensor_type=UpperCAmelCase_			)







"""simple docstring"""


from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE      =     TypeVar("""T""")



class   __a  (      Generic[T]	):






       def __init__(  self :							List[Any]       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              UpperCamelCase					 =		data
              UpperCamelCase					 =		self
              UpperCamelCase					 =		0





class   __a  (      Generic[T]	):






       def __init__(  self :							int			)->  None:







              """simple docstring"""
              # map from node name to the node object
              UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # create a new set with x as its member
              UpperCamelCase					 =		DisjointSetTreeNode(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							T			)->  DisjointSetTreeNode[T]:







              """simple docstring"""
              # find the set x belongs to (with path-compression)
              UpperCamelCase					 =		self.map[data]
              if elem_ref != elem_ref.parent:
                     UpperCamelCase					 =		self.find_set(elem_ref.parent.data			)
              return elem_ref.parent






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							DisjointSetTreeNode[T]       ,      UpperCAmelCase_ :							DisjointSetTreeNode[T]			)->  None:







              """simple docstring"""
              # helper function for union operation
              if nodea.rank > nodea.rank:
                     UpperCamelCase					 =		nodea
              else:
                     UpperCamelCase					 =		nodea
                     if nodea.rank == nodea.rank:
                            nodea.rank += 1






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # merge 2 disjoint sets
              self.link(self.find_set(UpperCAmelCase_			)       ,      self.find_set(UpperCAmelCase_			)			)





class   __a  (      Generic[T]	):






       def __init__(  self :							Dict			)->  None:







              """simple docstring"""
              # connections: map from the node to the neighbouring nodes (with weights)
              UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # add a node ONLY if its not present in the graph
              if node not in self.connections:
                     UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							int			)->  None:







              """simple docstring"""
              # add an edge with the given weight
              self.add_node(UpperCAmelCase_			)
              self.add_node(UpperCAmelCase_			)
              UpperCamelCase					 =		weight
              UpperCamelCase					 =		weight






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  GraphUndirectedWeighted[T]:







              """simple docstring"""
              UpperCamelCase					 =		[]
              UpperCamelCase					 =		set()
              for start in self.connections:
                     for end in self.connections[start]:
                            if (start, end) not in seen:
                                   seen.add((end, start)			)
                                   edges.append((start, end, self.connections[start][end])			)
              edges.sort(key=lambda UpperCAmelCase_			: x[2]			)

              # creating the disjoint set
              UpperCamelCase					 =		DisjointSetTree[T]()
              for node in self.connections:
                     disjoint_set.make_set(UpperCAmelCase_			)

              # MST generation
              UpperCamelCase					 =		0
              UpperCamelCase					 =		0
              UpperCamelCase					 =		GraphUndirectedWeighted[T]()
              while num_edges < len(self.connections			) - 1:
                     UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		edges[index]
                     index += 1
                     UpperCamelCase					 =		disjoint_set.find_set(UpperCAmelCase_			)
                     UpperCamelCase					 =		disjoint_set.find_set(UpperCAmelCase_			)
                     if parent_u != parent_v:
                            num_edges += 1
                            graph.add_edge(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
                            disjoint_set.union(UpperCAmelCase_       ,      UpperCAmelCase_			)
              return graph





