


"""simple docstring"""







import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import torch


class 				SCREAMING_SNAKE_CASE__    (		TensorFormatter[Mapping, "torch.Tensor", Mapping]    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         super().__init__(features=snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							torch_tensor_kwargs
         import torch  # noqa import torch at initialization

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import torch

         if isinstance(snake_case__		,					snake_case__						) and column:
           if all(
               isinstance(snake_case__		,					torch.Tensor						) and x.shape == column[0].shape and x.dtype == column[0].dtype
               for x in column						):
             return torch.stack(snake_case__						)
         return column

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import torch

         if isinstance(snake_case__		,					(str, bytes, type(snake_case__						))						):
           return value
         elif isinstance(snake_case__		,					(np.character, np.ndarray)						) and np.issubdtype(value.dtype		,					np.character						):
           return value.tolist()

         lowerCAmelCase					:					Dict								=							{}

         if isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.integer						):
           lowerCAmelCase					:					List[Any]								=							{"dtype": torch.intaa}
         elif isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.floating						):
           lowerCAmelCase					:					Union[str, Any]								=							{"dtype": torch.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(snake_case__		,					PIL.Image.Image						):
             lowerCAmelCase					:					Tuple								=							np.asarray(snake_case__						)
         return torch.tensor(snake_case__		,					**{**default_dtype, **self.torch_tensor_kwargs}						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import torch

         # support for torch, tf, jax etc.
         if hasattr(snake_case__		,					"__array__"						) and not isinstance(snake_case__		,					torch.Tensor						):
           lowerCAmelCase					:					Dict								=							data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(snake_case__		,					np.ndarray						):
           if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         elif isinstance(snake_case__		,					(list, tuple)						):
           return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         return self._tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return map_nested(self._recursive_tensorize		,					snake_case__		,					map_list=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.numpy_arrow_extractor().extract_row(snake_case__						)
         lowerCAmelCase					:					str								=							self.python_features_decoder.decode_row(snake_case__						)
         return self.recursive_tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							self.numpy_arrow_extractor().extract_column(snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							self.python_features_decoder.decode_column(snake_case__		,					pa_table.column_names[0]						)
         lowerCAmelCase					:					int								=							self.recursive_tensorize(snake_case__						)
         lowerCAmelCase					:					str								=							self._consolidate(snake_case__						)
         return column







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.numpy_arrow_extractor().extract_batch(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							self.python_features_decoder.decode_batch(snake_case__						)
         lowerCAmelCase					:					int								=							self.recursive_tensorize(snake_case__						)
         for column_name in batch:
           lowerCAmelCase					:					List[str]								=							self._consolidate(batch[column_name]						)
         return batch









"""simple docstring"""







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
lowerCAmelCase__				    =       1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

lowerCAmelCase__				    =       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:
    lowerCAmelCase					:					Any								=							(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
        2_4_0 * (distance) ** 4
    )
    return {"force": force}
  elif area == 0:
    lowerCAmelCase					:					Any								=							(2_4_0 * force * (distance) ** 4) / (
        REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
    )
    return {"area": area}
  elif distance == 0:
    lowerCAmelCase					:					Dict								=							(
        (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * 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()





