




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

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




class     a__ (      TensorFormatter[Mapping, np.ndarray, Mapping]   ):







     def __init__(			self						,	A=None						,	**A				)						->					Tuple:



          '''simple docstring'''



          super().__init__(features=A				)
          a											=   np_array_kwargs







     def     lowerCAmelCase_       (			self						,	A				)						->					Optional[int]:



          '''simple docstring'''



          if isinstance(A						,	A				):
               if column and all(
                   isinstance(A						,	np.ndarray				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
                    return np.stack(A				)
               else:
                    # don't use np.array(column, dtype=object)
                    # since it fails in certain cases
                    # see https://stackoverflow.com/q/51005699
                    a											=   np.empty(len(A				)						,	dtype=A				)
                    a											=   column
                    return out
          return column







     def     lowerCAmelCase_       (			self						,	A				)						->					Any:



          '''simple docstring'''



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

          a											=   {}

          if isinstance(A						,	np.ndarray				) and np.issubdtype(value.dtype						,	np.integer				):
               a											=   {"dtype": np.intaa}
          elif isinstance(A						,	np.ndarray				) and np.issubdtype(value.dtype						,	np.floating				):
               a											=   {"dtype": np.floataa}
          elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(A						,	PIL.Image.Image				):
                    return np.asarray(A						,	**self.np_array_kwargs				)

          return np.asarray(A						,	**{**default_dtype, **self.np_array_kwargs}				)







     def     lowerCAmelCase_       (			self						,	A				)						->					Optional[Any]:



          '''simple docstring'''



          if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch

               if isinstance(A						,	torch.Tensor				):
                    return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
          if hasattr(A						,	"__array__"				) and not isinstance(A						,	(np.ndarray, np.character, np.number)				):
               a											=   data_struct.__array__()
          # support for nested types like struct of list of struct
          if isinstance(A						,	np.ndarray				):
               if data_struct.dtype == object:
                    return self._consolidate([self.recursive_tensorize(A				) for substruct in data_struct]				)
          if isinstance(A						,	(list, tuple)				):
               return self._consolidate([self.recursive_tensorize(A				) for substruct in data_struct]				)
          return self._tensorize(A				)







     def     lowerCAmelCase_       (			self						,	A				)						->					List[Any]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A				)						->					Mapping:



          '''simple docstring'''



          a											=   self.numpy_arrow_extractor().extract_row(A				)
          a											=   self.python_features_decoder.decode_row(A				)
          return self.recursive_tensorize(A				)







     def     lowerCAmelCase_       (			self						,	A				)						->					np.ndarray:



          '''simple docstring'''



          a											=   self.numpy_arrow_extractor().extract_column(A				)
          a											=   self.python_features_decoder.decode_column(A						,	pa_table.column_names[0]				)
          a											=   self.recursive_tensorize(A				)
          a											=   self._consolidate(A				)
          return column







     def     lowerCAmelCase_       (			self						,	A				)						->					Mapping:



          '''simple docstring'''



          a											=   self.numpy_arrow_extractor().extract_batch(A				)
          a											=   self.python_features_decoder.decode_batch(A				)
          a											=   self.recursive_tensorize(A				)
          for column_name in batch:
               a											=   self._consolidate(batch[column_name]				)
          return batch







from __future__ import annotations

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  int | None:
     for r in range(1	,      __UpperCamelCase):
          a											=   []
          for x in range(n - 1):
               a											=   pow(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)
               if val in li:
                    break
               li.append(__UpperCamelCase)
          else:
               return r
     return None


if __name__ == "__main__":
   lowercase__   :    Tuple 					=      int(input("Enter a prime number q: "))
   lowercase__   :    List[Any] 					=      find_primitive(q)
   if a is None:
      print(F'Cannot find the primitive for the value: {a!r}')
   else:
      lowercase__   :    Any 					=      int(input("Enter private key of A: "))
      lowercase__   :    Optional[int] 					=      pow(a, a_private, q)
      lowercase__   :    Optional[Any] 					=      int(input("Enter private key of B: "))
      lowercase__   :    Any 					=      pow(a, b_private, q)

      lowercase__   :    Optional[Any] 					=      pow(b_public, a_private, q)
      lowercase__   :    List[str] 					=      pow(a_public, b_private, q)

      print("The key value generated by A is: ", a_secret)
      print("The key value generated by B is: ", b_secret)

