



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


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   __UpperCAmelCase):







							'''simple docstring'''






							UpperCamelCase__				:      Dict         =					["""pixel_values"""]


							def __init__(							self  ,  a_ = True  ,  a_ = None  ,  a_ = PILImageResampling.BILINEAR  ,  a_ = True  ,  a_ = 1 / 255  ,  a_ = True  ,  a_ = None  ,  a_ = True  ,  **a_  ,  ):
											super().__init__(**lowerCAmelCase_		)
											a__				      =		size if size is not None else {"""shortest_edge""": 224}
											a__				      =		get_size_dict(lowerCAmelCase_  ,  default_to_square=lowerCAmelCase_		)
											a__				      =		crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
											a__				      =		get_size_dict(lowerCAmelCase_  ,  param_name="""crop_size"""		)

											a__				      =		do_resize
											a__				      =		size
											a__				      =		resample
											a__				      =		do_rescale
											a__				      =		rescale_factor
											a__				      =		do_center_crop
											a__				      =		crop_size
											a__				      =		do_flip_channel_order


							def _a      (							self  ,  a_  ,  a_  ,  a_ = PIL.Image.BILINEAR  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(lowerCAmelCase_  ,  default_to_square=lowerCAmelCase_		)
											if "shortest_edge" not in size:
															raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''		)
											a__				      =		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 _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(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()}'''		)
											return center_crop(lowerCAmelCase_  ,  size=(size["""height"""], size["""width"""])  ,  data_format=lowerCAmelCase_  ,  **lowerCAmelCase_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											return rescale(lowerCAmelCase_  ,  scale=lowerCAmelCase_  ,  data_format=lowerCAmelCase_  ,  **lowerCAmelCase_		)


							def _a      (							self  ,  a_  ,  a_ = None		):
											return flip_channel_order(lowerCAmelCase_  ,  data_format=lowerCAmelCase_		)


							def _a      (							self  ,  a_  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = ChannelDimension.FIRST  ,  **a_  ,  ):
											a__				      =		do_resize if do_resize is not None else self.do_resize
											a__				      =		resample if resample is not None else self.resample
											a__				      =		do_rescale if do_rescale is not None else self.do_rescale
											a__				      =		rescale_factor if rescale_factor is not None else self.rescale_factor
											a__				      =		do_center_crop if do_center_crop is not None else self.do_center_crop
											a__				      =		(
											    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
											)

											a__				      =		size if size is not None else self.size
											a__				      =		get_size_dict(lowerCAmelCase_  ,  default_to_square=lowerCAmelCase_		)
											a__				      =		crop_size if crop_size is not None else self.crop_size
											a__				      =		get_size_dict(lowerCAmelCase_  ,  param_name="""crop_size"""		)

											a__				      =		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_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.
											a__				      =		[to_numpy_array(lowerCAmelCase_		) for image in images]

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

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

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

											# the pretrained checkpoints assume images are BGR, not RGB
											if do_flip_channel_order:
															a__				      =		[self.flip_channel_order(image=lowerCAmelCase_		) for image in images]

											a__				      =		[to_channel_dimension_format(lowerCAmelCase_  ,  lowerCAmelCase_		) for image in images]

											a__				      =		{"""pixel_values""": images}
											return BatchFeature(data=lowerCAmelCase_  ,  tensor_type=lowerCAmelCase_		)



							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		outputs.logits

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

															if is_torch_tensor(lowerCAmelCase_		):
																			a__				      =		target_sizes.numpy()

															a__				      =		[]

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

											return semantic_segmentation






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				if velocity > c:
								raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""		)
				elif velocity < 1:
								# Usually the speed should be much higher than 1 (c order of magnitude)
								raise ValueError("""Speed must be greater than or equal to 1!"""		)

				return velocity / c

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

				"""simple docstring"""
				# Ensure event is not empty
				if event is None:
								a__				      =		np.array([ct, x, y, z]		)  # Symbolic four vector
				else:
								event[0] *= c  # x0 is ct (speed of light * time)

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	print("""Example of four vector: """)
	print(f"""ct' = {four_vector[0]}""")
	print(f"""x' = {four_vector[1]}""")
	print(f"""y' = {four_vector[2]}""")
	print(f"""z' = {four_vector[3]}""")

	# Substitute symbols with numerical values
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

	print(f"""\n{numerical_vector}""")

