




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_barthez import BarthezTokenizer
else:
			__SCREAMING_SNAKE_CASE 						=None

__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE 						={
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE 						={
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

__SCREAMING_SNAKE_CASE 						="""▁"""






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							int	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					["input_ids", "attention_mask"]
				SCREAMING_SNAKE_CASE__					:							int	      =					BarthezTokenizer


				def __init__(							self:   str		,					_lowerCamelCase:   Optional[int]=None		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   Optional[Any]="<s>"		,					_lowerCamelCase:   Any="</s>"		,					_lowerCamelCase:   Optional[Any]="</s>"		,					_lowerCamelCase:   Dict="<s>"		,					_lowerCamelCase:   str="<unk>"		,					_lowerCamelCase:   Optional[Any]="<pad>"		,					_lowerCamelCase:   Any="<mask>"		,					**_lowerCamelCase:   Any		,					):
											# Mask token behave like a normal word, i.e. include the space before it
											SCREAMING_SNAKE_CASE_   	=    AddedToken(_lowerCamelCase		,					lstrip=_lowerCamelCase		,					rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase		,					_lowerCamelCase		) else mask_token

											super().__init__(
											    _lowerCamelCase		,					tokenizer_file=_lowerCamelCase		,					bos_token=_lowerCamelCase		,					eos_token=_lowerCamelCase		,					unk_token=_lowerCamelCase		,					sep_token=_lowerCamelCase		,					cls_token=_lowerCamelCase		,					pad_token=_lowerCamelCase		,					mask_token=_lowerCamelCase		,					**_lowerCamelCase		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    False if not self.vocab_file else True


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		):

											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											SCREAMING_SNAKE_CASE_   	=    [self.cls_token_id]
											SCREAMING_SNAKE_CASE_   	=    [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


				def     _A							(							self:   int		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		):
											SCREAMING_SNAKE_CASE_   	=    [self.sep_token_id]
											SCREAMING_SNAKE_CASE_   	=    [self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep		) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[str] = None		):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''		)

											if not os.path.isdir(_lowerCamelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    os.path.join(
											    _lowerCamelCase		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		):
																		copyfile(self.vocab_file		,					_lowerCamelCase		)

											return (out_vocab_file,)








# 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  __magic_name__						(  TensorFormatter[Mapping, "torch.Tensor", Mapping]):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(features=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch_tensor_kwargs
											import torch  # noqa import torch at initialization


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											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:   Tuple		,					_lowerCamelCase:   List[str]		):
											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()

											SCREAMING_SNAKE_CASE_   	=    {}

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

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


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(_lowerCamelCase		,					'''__array__'''		) and not isinstance(_lowerCamelCase		,					torch.Tensor		):
																		SCREAMING_SNAKE_CASE_   	=    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:   str		,					_lowerCamelCase:   dict		):
											return map_nested(self._recursive_tensorize		,					_lowerCamelCase		,					map_list=_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_lowerCamelCase		)
											return self.recursive_tensorize(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_lowerCamelCase		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_lowerCamelCase		)
											return column





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch


