





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]       ):


    """simple docstring"""


    def __init__(      self					, _A=None					, **_A					):
         super().__init__(features=UpperCamelCase__					)
         __A     :						List[Any]          =    np_array_kwargs



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



    def  UpperCAmelCase_		(      self					, _A					):
         if isinstance(UpperCamelCase__					, (str, bytes, type(UpperCamelCase__					))					):
              return value
         elif isinstance(UpperCamelCase__					, (np.character, np.ndarray)					) and np.issubdtype(value.dtype					, np.character					):
              return value
         elif isinstance(UpperCamelCase__					, np.number					):
              return value

         __A     :						Union[str, Any]          =    {}

         if isinstance(UpperCamelCase__					, np.ndarray					) and np.issubdtype(value.dtype					, np.integer					):
              __A     :						int          =    {"dtype": np.intaa}
         elif isinstance(UpperCamelCase__					, np.ndarray					) and np.issubdtype(value.dtype					, np.floating					):
              __A     :						List[Any]          =    {"dtype": np.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

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

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



    def  UpperCAmelCase_		(      self					, _A					):
         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

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



    def  UpperCAmelCase_		(      self					, _A					):
         return map_nested(self._recursive_tensorize					, UpperCamelCase__					, map_list=UpperCamelCase__					)



    def  UpperCAmelCase_		(      self					, _A					):
         __A     :						Dict          =    self.numpy_arrow_extractor().extract_row(UpperCamelCase__					)
         __A     :						Dict          =    self.python_features_decoder.decode_row(UpperCamelCase__					)
         return self.recursive_tensorize(UpperCamelCase__					)



    def  UpperCAmelCase_		(      self					, _A					):
         __A     :						List[Any]          =    self.numpy_arrow_extractor().extract_column(UpperCamelCase__					)
         __A     :						Union[str, Any]          =    self.python_features_decoder.decode_column(UpperCamelCase__					, pa_table.column_names[0]					)
         __A     :						str          =    self.recursive_tensorize(UpperCamelCase__					)
         __A     :						Optional[int]          =    self._consolidate(UpperCamelCase__					)
         return column





    def  UpperCAmelCase_		(      self					, _A					):
         __A     :						Optional[Any]          =    self.numpy_arrow_extractor().extract_batch(UpperCamelCase__					)
         __A     :						Dict          =    self.python_features_decoder.decode_batch(UpperCamelCase__					)
         __A     :						int          =    self.recursive_tensorize(UpperCamelCase__					)
         for column_name in batch:
              __A     :						Dict          =    self._consolidate(batch[column_name]					)
         return batch











from __future__ import annotations

import math
import random







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list[int]:
					__A     :						int          =    d * e - 1
					__A     :						List[str]          =    0
					__A     :						Optional[Any]          =    0
					while p == 0:
										__A     :						List[Any]          =    random.randint(2 ,						n - 1  )
										__A     :						Optional[int]          =    k
										while True:
															if t % 2 == 0:
																				__A     :						Tuple          =    t // 2
																				__A     :						List[str]          =    (g**t) % n
																				__A     :						List[Any]          =    math.gcd(x - 1 ,						a  )
																				if x > 1 and y > 1:
																									__A     :						List[str]          =    y
																									__A     :						str          =    n // y
																									break  # find the correct factors
															else:
																				break  # t is not divisible by 2, break and choose another g
					return sorted([p, q]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




