
'''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 	__lowercase				(  TensorFormatter[Mapping, "torch.Tensor", Mapping]    ):

			def __init__(self			,   A=None			,   **A      ):
								super().__init__(features=A      )
								lowerCamelCase_					:      Optional[int]     =		torch_tensor_kwargs
								import torch  # noqa import torch at initialization

			def        UpperCAmelCase__ (self			,   A      ):
								import torch

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

			def        UpperCAmelCase__ (self			,   A      ):
								import torch

								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.tolist()

								lowerCamelCase_					:      Optional[int]     =		{}

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

													if isinstance(A			,   PIL.Image.Image      ):
																		lowerCamelCase_					:      Optional[int]     =		np.asarray(A      )
								return torch.tensor(A			,   **{**default_dtype, **self.torch_tensor_kwargs}      )

			def        UpperCAmelCase__ (self			,   A      ):
								import torch

								# support for torch, tf, jax etc.
								if hasattr(A			,   '''__array__'''      ) and not isinstance(A			,   torch.Tensor      ):
													lowerCamelCase_					:      str     =		data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(A			,   np.ndarray      ):
													if data_struct.dtype == object:  # torch 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        UpperCAmelCase__ (self			,   A      ):
								return map_nested(self._recursive_tensorize			,   A			,   map_list=A      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      List[Any]     =		self.numpy_arrow_extractor().extract_row(A      )
								lowerCamelCase_					:      List[Any]     =		self.python_features_decoder.decode_row(A      )
								return self.recursive_tensorize(A      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[Any]     =		self.numpy_arrow_extractor().extract_column(A      )
								lowerCamelCase_					:      List[Any]     =		self.python_features_decoder.decode_column(A			,   pa_table.column_names[0]      )
								lowerCamelCase_					:      Union[str, Any]     =		self.recursive_tensorize(A      )
								lowerCamelCase_					:      int     =		self._consolidate(A      )
								return column






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





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   List[Any]					=     {}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   List[Any]					=     ['''MLukeTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mluke import MLukeTokenizer


else:
					import sys

					__lowercase							:   List[str]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


