





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowercase						:      int									= 299792458

# Symbols
lowercase						:      Dict									= symbols('ct x y z')




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    float)      ->					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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    float)      ->					float:






							'''simple docstring'''





							return 1 / sqrt(1 - beta(UpperCAmelCase__) ** 2)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    float)      ->					np.ndarray:






							'''simple docstring'''





							return np.array(
							    [
							        [gamma(UpperCAmelCase__), -gamma(UpperCAmelCase__) * beta(UpperCAmelCase__), 0, 0],
							        [-gamma(UpperCAmelCase__) * beta(UpperCAmelCase__), gamma(UpperCAmelCase__), 0, 0],
							        [0, 0, 1, 0],
							        [0, 0, 0, 1],
							    ])




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    float		,						_lowerCamelCase				:    np.ndarray | None = None)      ->					np.ndarray:






							'''simple docstring'''





							if event is None:
														__UpperCamelCase			:     Any         =      np.array([ct, x, y, z])  # Symbolic four vector
							else:
														event[0] *= c  # x0 is ct (speed of light * time)

							return transformation_matrix(UpperCAmelCase__) @ event


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Example of symbolic vector:
		lowercase						:      List[Any]									= transform(29979245)
		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
		lowercase						:      Optional[Any]									= {ct: c, x: 1, y: 1, z: 1}
		lowercase						:      int									= [four_vector[i].subs(sub_dict) for i in range(4)]

		print(f"\n{numerical_vector}")

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Tuple									= {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Union[str, Any]									= [
		    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Swinv2ForImageClassification',
		    'Swinv2ForMaskedImageModeling',
		    'Swinv2Model',
		    'Swinv2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swinva import (
				    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinvaForImageClassification,
				    SwinvaForMaskedImageModeling,
				    SwinvaModel,
				    SwinvaPreTrainedModel,
				)


else:
		import sys

		lowercase						:      Optional[Any]									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)