





# 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


UpperCAmelCase     :     Any							=	{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase     :     int							=	["""VivitImageProcessor"""]


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase     :     Optional[Any]							=	[
       """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """VivitModel""",
       """VivitPreTrainedModel""",
       """VivitForVideoClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase     :     List[Any]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase     :     Union[str, Any]							=	1.054571817E-34  # unit of ℏ : J * s

UpperCAmelCase     :     Union[str, Any]							=	3E8  # unit of c : m * s^-1
def 					_A	(		SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float						):





  """simple docstring"""



  if (force, area, distance).count(0						) != 1:
    raise ValueError("One and only one argument must be 0"						)
  if force < 0:
    raise ValueError("Magnitude of force can not be negative"						)
  if distance < 0:
    raise ValueError("Distance can not be negative"						)
  if area < 0:
    raise ValueError("Area can not be negative"						)
  if force == 0:
    a__     : Tuple  =(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
        240 * (distance) ** 4
    )
    return {"force": force}
  elif area == 0:
    a__     : Any  =(240 * force * (distance) ** 4) / (
        REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
    )
    return {"area": area}
  elif distance == 0:
    a__     : List[str]  =(
        (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
    ) ** (1 / 4)
    return {"distance": distance}
  raise ValueError("One and only one argument must be 0"						)


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()






