
'''simple docstring'''



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowercase    =		299792458

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


def A     (__lowerCamelCase				:float		):
						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     (__lowerCamelCase				:float		):
						return 1 / sqrt(1 - beta(__lowerCamelCase		) ** 2		)


def A     (__lowerCamelCase				:float		):
						return np.array(
						    [
						        [gamma(__lowerCamelCase		), -gamma(__lowerCamelCase		) * beta(__lowerCamelCase		), 0, 0],
						        [-gamma(__lowerCamelCase		) * beta(__lowerCamelCase		), gamma(__lowerCamelCase		), 0, 0],
						        [0, 0, 1, 0],
						        [0, 0, 0, 1],
						    ]		)


def A     (__lowerCamelCase				:float			,     __lowerCamelCase				:np.ndarray | None = None		):
						# Ensure event is not empty
						if event is None:
												_lowerCAmelCase  						=			np.array([ct, x, y, z]		)  # Symbolic four vector
						else:
												event[0] *= c  # x0 is ct (speed of light * time)

						return transformation_matrix(__lowerCamelCase		) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						_lowercase    =		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    =		{ct: c, x: 1, y: 1, z: 1}
						_lowercase    =		[four_vector[i].subs(sub_dict) for i in range(4)]

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


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    =		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TableTransformerForObjectDetection""",
						    """TableTransformerModel""",
						    """TableTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_table_transformer import (
						    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TableTransformerConfig,
						    TableTransformerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_table_transformer import (
												    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TableTransformerForObjectDetection,
												    TableTransformerModel,
												    TableTransformerPreTrainedModel,
												)

else:
						import sys

						_lowercase    =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
