'''simple docstring'''




# Lint as: python3
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 tensorflow as tf




class 					a__   (     TensorFormatter[Mapping, "tf.Tensor", Mapping]				):






     def __init__(				self	:	List[Any]						,       a	:	List[str]=None						,       **a	:	Dict					):





         """simple docstring"""



         super().__init__(features=a					)
         __lowerCamelCase						=						tf_tensor_kwargs
         import tensorflow as tf  # noqa: import tf at initialization






     def      SCREAMING_SNAKE_CASE__     (				self	:	str						,       a	:	int					):





         """simple docstring"""



         import tensorflow as tf

         if isinstance(a						,       a					) and column:
             if all(
                 isinstance(a						,       tf.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
                 return tf.stack(a					)
             elif all(
                 isinstance(a						,       (tf.Tensor, tf.RaggedTensor)					) and x.ndim == 1 and x.dtype == column[0].dtype
                 for x in column					):
                 # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                 return tf.ragged.stack(a					)

         return column






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[int]						,       a	:	int					):





         """simple docstring"""



         import tensorflow as tf

         if value is None:
             return value

         __lowerCamelCase						=						{}

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

             if isinstance(a						,       PIL.Image.Image					):
                 __lowerCamelCase						=						np.asarray(a					)

         return tf.convert_to_tensor(a						,       **{**default_dtype, **self.tf_tensor_kwargs}					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	int						,       a	:	str					):





         """simple docstring"""



         import tensorflow as tf

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






     def      SCREAMING_SNAKE_CASE__     (				self	:	Union[str, Any]						,       a	:	dict					):





         """simple docstring"""



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






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict						,       a	:	pa.Table					):





         """simple docstring"""



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






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]						,       a	:	pa.Table					):





         """simple docstring"""



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






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	pa.Table					):





         """simple docstring"""



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

'''simple docstring'''




def __lowerCAmelCase				(							UpperCamelCase__       )			->					bool:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    if len(UpperCamelCase__       ) == 1 and series[0] != 0:
        return True
    __lowerCamelCase						=						[]
    __lowerCamelCase						=						len(UpperCamelCase__       )
    for i in range(0       ,						UpperCamelCase__       ):
        if series[i] == 0:
            raise ValueError('''Input series cannot have 0 as an element'''       )
        rec_series.append(1 / series[i]       )
    __lowerCamelCase						=						rec_series[1] - rec_series[0]
    for index in range(2       ,						UpperCamelCase__       ):
        if rec_series[index] - rec_series[index - 1] != common_diff:
            return False
    return True






def __lowerCAmelCase				(							UpperCamelCase__       )			->					float:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    __lowerCamelCase						=						0
    for val in series:
        answer += 1 / val
    return len(UpperCamelCase__       ) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()
