
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
							import PIL

if is_torch_available():
							import torch


lowerCamelCase_  :	int 				=					logging.get_logger(__name__)

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	["""pixel_values"""]

			def __init__(    self	:    Any  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    PILImageResampling = PILImageResampling.BILINEAR  ,		snake_case_	:    bool = True  ,		snake_case_	:    Union[int, float] = 1 / 255  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    bool = True  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(**snake_case_ )
							UpperCamelCase_:							List[str]		=  size if size is not None else {"""shortest_edge""": 224}
							UpperCamelCase_:							List[str]		=  get_size_dict(snake_case_  ,		default_to_square=snake_case_ )
							UpperCamelCase_:							List[Any]		=  crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
							UpperCamelCase_:							Optional[int]		=  get_size_dict(snake_case_  ,		param_name="""crop_size""" )

							UpperCamelCase_:							Tuple		=  do_resize
							UpperCamelCase_:							str		=  size
							UpperCamelCase_:							int		=  resample
							UpperCamelCase_:							Tuple		=  do_rescale
							UpperCamelCase_:							str		=  rescale_factor
							UpperCamelCase_:							Tuple		=  do_center_crop
							UpperCamelCase_:							int		=  crop_size
							UpperCamelCase_:							List[Any]		=  do_flip_channel_order

			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Dict[str, int]  ,		snake_case_	:    PILImageResampling = PIL.Image.BILINEAR  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Optional[int]  ,		):
							UpperCamelCase_:							int		=  get_size_dict(snake_case_  ,		default_to_square=snake_case_ )
							if "shortest_edge" not in size:
											raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
							UpperCamelCase_:							Union[str, Any]		=  get_resize_output_image_size(snake_case_  ,		size=size["""shortest_edge"""]  ,		default_to_square=snake_case_ )
							return resize(snake_case_  ,		size=snake_case_  ,		resample=snake_case_  ,		data_format=snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Dict[str, int]  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Optional[Any]  ,		):
							UpperCamelCase_:							Union[str, Any]		=  get_size_dict(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 center_crop(snake_case_  ,		size=(size["""height"""], size["""width"""])  ,		data_format=snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Union[int, float]  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Optional[int]  ,		):
							return rescale(snake_case_  ,		scale=snake_case_  ,		data_format=snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None ):
							return flip_channel_order(snake_case_  ,		data_format=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    ImageInput  ,		snake_case_	:    bool = None  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    PILImageResampling = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    float = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		snake_case_	:    ChannelDimension = ChannelDimension.FIRST  ,		**snake_case_	:    Dict  ,		):
							UpperCamelCase_:							Tuple		=  do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_:							Optional[Any]		=  resample if resample is not None else self.resample
							UpperCamelCase_:							List[str]		=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase_:							Union[str, Any]		=  rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase_:							Optional[Any]		=  do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase_:							Union[str, Any]		=  (
							    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
							)

							UpperCamelCase_:							List[str]		=  size if size is not None else self.size
							UpperCamelCase_:							List[str]		=  get_size_dict(snake_case_  ,		default_to_square=snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  crop_size if crop_size is not None else self.crop_size
							UpperCamelCase_:							Optional[Any]		=  get_size_dict(snake_case_  ,		param_name="""crop_size""" )

							UpperCamelCase_:							Tuple		=  make_list_of_images(snake_case_ )

							if not valid_images(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.""" )

							if do_center_crop and crop_size is None:
											raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

							# All transformations expect numpy arrays.
							UpperCamelCase_:							str		=  [to_numpy_array(snake_case_ ) for image in images]

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

							if do_center_crop:
											UpperCamelCase_:							str		=  [self.center_crop(image=snake_case_  ,		size=snake_case_ ) for image in images]

							if do_rescale:
											UpperCamelCase_:							int		=  [self.rescale(image=snake_case_  ,		scale=snake_case_ ) for image in images]

							# the pretrained checkpoints assume images are BGR, not RGB
							if do_flip_channel_order:
											UpperCamelCase_:							Union[str, Any]		=  [self.flip_channel_order(image=snake_case_ ) for image in images]

							UpperCamelCase_:							Union[str, Any]		=  [to_channel_dimension_format(snake_case_  ,		snake_case_ ) for image in images]

							UpperCamelCase_:							Any		=  {"""pixel_values""": images}
							return BatchFeature(data=snake_case_  ,		tensor_type=snake_case_ )




			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    str  ,		snake_case_	:    List[Tuple] = None ):
							UpperCamelCase_:							Dict		=  outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
											if len(snake_case_ ) != len(snake_case_ ):
															raise ValueError(
															    """Make sure that you pass in as many target sizes as the batch dimension of the logits""" )

											if is_torch_tensor(snake_case_ ):
															UpperCamelCase_:							Union[str, Any]		=  target_sizes.numpy()

											UpperCamelCase_:							List[Any]		=  []

											for idx in range(len(snake_case_ ) ):
															UpperCamelCase_:							str		=  torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0 )  ,		size=target_sizes[idx]  ,		mode="""bilinear"""  ,		align_corners=snake_case_ )
															UpperCamelCase_:							str		=  resized_logits[0].argmax(dim=0 )
															semantic_segmentation.append(snake_case_ )
							else:
											UpperCamelCase_:							Any		=  logits.argmax(dim=1 )
											UpperCamelCase_:							Dict		=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

							return semantic_segmentation








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		float:
				if mass < 0:
								raise ValueError("""The mass of a body cannot be negative"""  )
				return 0.5 * mass * abs(lowerCamelCase  ) * abs(lowerCamelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






