'''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 convert_to_rgb, 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


if is_vision_available():
			import PIL


lowerCAmelCase_	:		Any			=     logging.get_logger(__name__)




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							['''pixel_values''']





				def __init__(						self					:						Tuple       ,		lowercase__					:						Optional[int] = True       ,		lowercase__					:						Tuple = None       ,		lowercase__					:						List[str] = PILImageResampling.BICUBIC       ,		lowercase__					:						Union[str, Any] = True       ,		lowercase__					:						Optional[Any] = 1 / 255       ,		lowercase__					:						Optional[Any] = True       ,		lowercase__					:						Union[str, Any] = None       ,		lowercase__					:						Optional[Any] = None       ,		lowercase__					:						Any = True       ,		**lowercase__					:						str       ,		)							->List[Any]:


										'''simple docstring'''




										super().__init__(**_lowerCAmelCase					)
										_UpperCamelCase			:			Optional[int]   			=						size if size is not None else {"height": 384, "width": 384}
										_UpperCamelCase			:			Tuple   			=						get_size_dict(_lowerCAmelCase       ,		default_to_square=_lowerCAmelCase					)

										_UpperCamelCase			:			Tuple   			=						do_resize
										_UpperCamelCase			:			Dict   			=						size
										_UpperCamelCase			:			Optional[int]   			=						resample
										_UpperCamelCase			:			Tuple   			=						do_rescale
										_UpperCamelCase			:			List[Any]   			=						rescale_factor
										_UpperCamelCase			:			str   			=						do_normalize
										_UpperCamelCase			:			Union[str, Any]   			=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										_UpperCamelCase			:			List[str]   			=						image_std if image_std is not None else OPENAI_CLIP_STD
										_UpperCamelCase			:			Optional[int]   			=						do_convert_rgb





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						List[str]       ,		lowercase__					:						Any       ,		lowercase__					:						List[str] = PILImageResampling.BICUBIC       ,		lowercase__					:						Optional[Any] = None       ,		**lowercase__					:						Optional[int]       ,		)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						get_size_dict(_lowerCAmelCase       ,		default_to_square=_lowerCAmelCase					)
										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()}'''					)
										_UpperCamelCase			:			Dict   			=						(size["height"], size["width"])
										return resize(_lowerCAmelCase       ,		size=_lowerCAmelCase       ,		resample=_lowerCAmelCase       ,		data_format=_lowerCAmelCase       ,		**_lowerCAmelCase					)





				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						int       ,		lowercase__					:						List[Any]       ,		lowercase__					:						Dict = None       ,		**lowercase__					:						Optional[int]       ,		)							->Optional[Any]:


										'''simple docstring'''




										return rescale(_lowerCAmelCase       ,		scale=_lowerCAmelCase       ,		data_format=_lowerCAmelCase       ,		**_lowerCAmelCase					)





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						str       ,		lowercase__					:						List[Any]       ,		lowercase__					:						Any       ,		lowercase__					:						Optional[Any] = None       ,		**lowercase__					:						Any       ,		)							->int:


										'''simple docstring'''




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







				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						Dict       ,		lowercase__					:						Dict = None       ,		lowercase__					:						Union[str, Any] = None       ,		lowercase__					:						int = None       ,		lowercase__					:						Union[str, Any] = None       ,		lowercase__					:						int = None       ,		lowercase__					:						Tuple = None       ,		lowercase__					:						str = None       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						List[str] = None       ,		lowercase__					:						Tuple = None       ,		lowercase__					:						Union[str, Any] = ChannelDimension.FIRST       ,		**lowercase__					:						List[str]       ,		)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						do_resize if do_resize is not None else self.do_resize
										_UpperCamelCase			:			Optional[int]   			=						resample if resample is not None else self.resample
										_UpperCamelCase			:			Optional[Any]   			=						do_rescale if do_rescale is not None else self.do_rescale
										_UpperCamelCase			:			Dict   			=						rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCamelCase			:			Any   			=						do_normalize if do_normalize is not None else self.do_normalize
										_UpperCamelCase			:			Optional[int]   			=						image_mean if image_mean is not None else self.image_mean
										_UpperCamelCase			:			List[Any]   			=						image_std if image_std is not None else self.image_std
										_UpperCamelCase			:			Optional[int]   			=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										_UpperCamelCase			:			Tuple   			=						size if size is not None else self.size
										_UpperCamelCase			:			List[Any]   			=						get_size_dict(_lowerCAmelCase       ,		default_to_square=_lowerCAmelCase					)

										_UpperCamelCase			:			Dict   			=						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 or resample is None:
																raise ValueError("Size and resample 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."					)

										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:
																_UpperCamelCase			:			Optional[int]   			=						[convert_to_rgb(_lowerCAmelCase					) for image in images]

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

										if do_resize:
																_UpperCamelCase			:			Union[str, Any]   			=						[self.resize(image=_lowerCAmelCase       ,		size=_lowerCAmelCase       ,		resample=_lowerCAmelCase					) for image in images]

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

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

										_UpperCamelCase			:			Dict   			=						[to_channel_dimension_format(_lowerCAmelCase       ,		_lowerCAmelCase					) for image in images]

										_UpperCamelCase			:			int   			=						BatchFeature(data={"pixel_values": images}       ,		tensor_type=_lowerCAmelCase					)

										return encoded_outputs



'''simple docstring'''





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=False					)     ->			Optional[int]:




						'''simple docstring'''

						if isinstance(UpperCAmelCase     ,UpperCAmelCase					) and isinstance(UpperCAmelCase     ,UpperCAmelCase					):
												_UpperCamelCase			:			Any   			=						len(set_a.intersection(UpperCAmelCase					)					)

												if alternative_union:
																		_UpperCamelCase			:			List[str]   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
												else:
																		_UpperCamelCase			:			List[str]   			=						len(set_a.union(UpperCAmelCase					)					)

												return intersection / union

						if isinstance(UpperCAmelCase     ,(list, tuple)					) and isinstance(UpperCAmelCase     ,(list, tuple)					):
												_UpperCamelCase			:			List[Any]   			=						[element for element in set_a if element in set_b]

												if alternative_union:
																		_UpperCamelCase			:			str   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
																		return len(UpperCAmelCase					) / union
												else:
																		_UpperCamelCase			:			int   			=						set_a + [element for element in set_b if element not in set_a]
																		return len(UpperCAmelCase					) / len(UpperCAmelCase					)

												return len(UpperCAmelCase					) / len(UpperCAmelCase					)
						return None


if __name__ == "__main__":
			lowerCAmelCase_	:		List[str]			=     {"""a""", """b""", """c""", """d""", """e"""}
			lowerCAmelCase_	:		Dict			=     {"""c""", """d""", """e""", """f""", """h""", """i"""}
			print(jaccard_similarity(set_a, set_b))


