




"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__A       						=	{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A       						=	['''DeiTFeatureExtractor''']
							__A       						=	['''DeiTImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A       						=	[
							    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DeiTForImageClassification''',
							    '''DeiTForImageClassificationWithTeacher''',
							    '''DeiTForMaskedImageModeling''',
							    '''DeiTModel''',
							    '''DeiTPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A       						=	[
							    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFDeiTForImageClassification''',
							    '''TFDeiTForImageClassificationWithTeacher''',
							    '''TFDeiTForMaskedImageModeling''',
							    '''TFDeiTModel''',
							    '''TFDeiTPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_deit import DeiTFeatureExtractor
														from .image_processing_deit import DeiTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deit import (
														    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DeiTForImageClassification,
														    DeiTForImageClassificationWithTeacher,
														    DeiTForMaskedImageModeling,
														    DeiTModel,
														    DeiTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deit import (
														    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDeiTForImageClassification,
														    TFDeiTForImageClassificationWithTeacher,
														    TFDeiTForMaskedImageModeling,
														    TFDeiTModel,
														    TFDeiTPreTrainedModel,
														)


else:
							import sys

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


'''simple docstring'''

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_A			:      List[Any]					      =				299792458

# Symbols
_A      ,     _A      ,     _A      ,     _A			:      Union[str, Any]					      =				symbols('''ct x y z''')







def      UpperCamelCase_						(					snake_case_       :       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      UpperCamelCase_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       float      )					->     np.ndarray:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       float						,		snake_case_       :       np.ndarray | None = None      )					->     np.ndarray:






 '''simple docstring'''

 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(snake_case_      ) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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

 print(f'\n{numerical_vector}')






