



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




			'''simple docstring'''






			def __init__(  self				:					str			,			a_				:					Any=None			,			**a_				:					List[Any]					):
					'''simple docstring'''





					super().__init__(features=a_					)
					__UpperCAmelCase       :	int						=      np_array_kwargs




			def 					snake_case__	(  self				:					List[str]			,			a_				:					Union[str, Any]					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					str			,			a_				:					Tuple					):
					'''simple docstring'''





					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
					elif isinstance(a_			,			np.number					):
							return value

					__UpperCAmelCase       :	List[str]						=      {}

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

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

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




			def 					snake_case__	(  self				:					Dict			,			a_				:					int					):
					'''simple docstring'''





					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_			,			(np.ndarray, np.character, np.number)					):
							__UpperCAmelCase       :	Any						=      data_struct.__array__()
					# support for nested types like struct of list of struct
					if isinstance(a_			,			np.ndarray					):
							if data_struct.dtype == object:
									return self._consolidate([self.recursive_tensorize(a_					) for substruct in data_struct]					)
					if isinstance(a_			,			(list, tuple)					):
							return self._consolidate([self.recursive_tensorize(a_					) for substruct in data_struct]					)
					return self._tensorize(a_					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					dict					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					Tuple			,			a_				:					pa.Table					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.numpy_arrow_extractor().extract_row(a_					)
					__UpperCAmelCase       :	List[Any]						=      self.python_features_decoder.decode_row(a_					)
					return self.recursive_tensorize(a_					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					pa.Table					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      self.numpy_arrow_extractor().extract_column(a_					)
					__UpperCAmelCase       :	str						=      self.python_features_decoder.decode_column(a_			,			pa_table.column_names[0]					)
					__UpperCAmelCase       :	List[Any]						=      self.recursive_tensorize(a_					)
					__UpperCAmelCase       :	List[Any]						=      self._consolidate(a_					)
					return column




			def 					snake_case__	(  self				:					str			,			a_				:					pa.Table					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.numpy_arrow_extractor().extract_batch(a_					)
					__UpperCAmelCase       :	int						=      self.python_features_decoder.decode_batch(a_					)
					__UpperCAmelCase       :	Tuple						=      self.recursive_tensorize(a_					)
					for column_name in batch:
							__UpperCAmelCase       :	Tuple						=      self._consolidate(batch[column_name]					)
					return batch










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__)





