


"""simple docstring"""







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 				lowerCamelCase							(      TensorFormatter[Mapping, np.ndarray, Mapping]			):


							"""simple docstring"""






							def __init__(    self			:  Tuple		,			lowerCamelCase_			:  str=None		,			**lowerCamelCase_			:  int					):
													super().__init__(features=lowerCamelCase_					)
													UpperCAmelCase__												=     np_array_kwargs






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






							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  Union[str, Any]					):
													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
													elif isinstance(lowerCamelCase_		,			np.number					):
																			return value

													UpperCAmelCase__												=     {}

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

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

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






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Optional[Any]					):
													# support for torch, tf, jax etc.
													if config.TORCH_AVAILABLE and "torch" in sys.modules:
																			import torch

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






							def        lowerCAmelCase  (    self			:  str		,			lowerCamelCase_			:  dict					):
													return map_nested(self._recursive_tensorize		,			lowerCamelCase_		,			map_list=lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int		,			lowerCamelCase_			:  pa.Table					):
													UpperCAmelCase__												=     self.numpy_arrow_extractor().extract_row(lowerCamelCase_					)
													UpperCAmelCase__												=     self.python_features_decoder.decode_row(lowerCamelCase_					)
													return self.recursive_tensorize(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  pa.Table					):
													UpperCAmelCase__												=     self.numpy_arrow_extractor().extract_column(lowerCamelCase_					)
													UpperCAmelCase__												=     self.python_features_decoder.decode_column(lowerCamelCase_		,			pa_table.column_names[0]					)
													UpperCAmelCase__												=     self.recursive_tensorize(lowerCamelCase_					)
													UpperCAmelCase__												=     self._consolidate(lowerCamelCase_					)
													return column






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  pa.Table					):
													UpperCAmelCase__												=     self.numpy_arrow_extractor().extract_batch(lowerCamelCase_					)
													UpperCAmelCase__												=     self.python_features_decoder.decode_batch(lowerCamelCase_					)
													UpperCAmelCase__												=     self.recursive_tensorize(lowerCamelCase_					)
													for column_name in batch:
																			UpperCAmelCase__												=     self._consolidate(batch[column_name]					)
													return batch



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizerFast"]


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

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez_fast import BarthezTokenizerFast

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)