"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import _LazyModule


__UpperCamelCase									=			{'''tokenization_tapex''': ['''TapexTokenizer''']}


if TYPE_CHECKING:
    from .tokenization_tapex import TapexTokenizer


else:
    import sys

    __UpperCamelCase									=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




# 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 torch





class  __lowerCAmelCase     (					TensorFormatter[Mapping, """torch.Tensor""", Mapping]							):




  def __init__(				self							,   lowerCAmelCase=None							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        super().__init__(features=lowerCAmelCase					)
        _lowercase	       =torch_tensor_kwargs
        import torch  # noqa import torch at initialization




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






        import torch

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




  def   A__     (				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        import torch

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

        _lowercase	       ={}

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

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




  def   A__     (				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        import torch

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




  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Mapping:
        '''simple docstring'''






        _lowercase	       =self.numpy_arrow_extractor().extract_row(lowerCAmelCase					)
        _lowercase	       =self.python_features_decoder.decode_row(lowerCAmelCase					)
        return self.recursive_tensorize(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> "torch.Tensor":
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Mapping:
        '''simple docstring'''






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


