

'''simple docstring'''
from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCamelCase			:				Any 			=					2_9_9_7_9_2_4_5_8

# Symbols
lowerCamelCase			,   lowerCamelCase			,   lowerCamelCase			,   lowerCamelCase			:				Tuple 			=					symbols("ct x y z")


def 	_lowerCAmelCase (						_UpperCamelCase						:					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 	_lowerCAmelCase (						_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					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 	_lowerCAmelCase (						_UpperCamelCase						:					float							,			_UpperCamelCase						:					np.ndarray | None = None    )				->      np.ndarray:
     """simple docstring"""




     if event is None:
          _SCREAMING_SNAKE_CASE							   =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:
     lowerCamelCase			:				Any 			=					transform(2_9_9_7_9_2_4_5)
     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
     lowerCamelCase			:				Optional[Any] 			=					{ct: c, x: 1, y: 1, z: 1}
     lowerCamelCase			:				Dict 			=					[four_vector[i].subs(sub_dict) for i in range(4)]

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



'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase			:				Optional[int] 			=					{
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					["EfficientNetImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "EfficientNetForImageClassification",
         "EfficientNetModel",
         "EfficientNetPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_efficientnet import (
         EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EfficientNetConfig,
         EfficientNetOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_efficientnet import EfficientNetImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_efficientnet import (
              EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              EfficientNetForImageClassification,
              EfficientNetModel,
              EfficientNetPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				int 			=					_LazyModule(__name__, globals()["__file__"], _import_structure)
