


"""simple docstring"""



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:
  __snake_case         =					None

__snake_case         =					logging.get_logger(__name__)

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

__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"""
        ),
    },
}

__snake_case         =					{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

__snake_case         =					"""▁"""







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: int													=						VOCAB_FILES_NAMES
   __UpperCAmelCase				: int													=						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase				: Optional[Any]													=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase				: Union[str, Any]													=						['''input_ids''', '''attention_mask''']
   __UpperCAmelCase				: Dict													=						BarthezTokenizer







   def __init__(			self		,			UpperCamelCase__=None		,			UpperCamelCase__=None		,			UpperCamelCase__="<s>"		,			UpperCamelCase__="</s>"		,			UpperCamelCase__="</s>"		,			UpperCamelCase__="<s>"		,			UpperCamelCase__="<unk>"		,			UpperCamelCase__="<pad>"		,			UpperCamelCase__="<mask>"		,			**UpperCamelCase__		,			)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Any         =					AddedToken(UpperCamelCase__		,			lstrip=UpperCamelCase__		,			rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,			UpperCamelCase__		) else mask_token

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

       snake_case					:   int         =					vocab_file
       snake_case					:   Tuple         =					False if not self.vocab_file else True







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	List[int]:

       '''simple docstring'''




       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case					:   Dict         =					[self.cls_token_id]
       snake_case					:   List[str]         =					[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	List[int]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[self.sep_token_id]
       snake_case					:   Any         =					[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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	Tuple[str]:

       '''simple docstring'''




       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(UpperCamelCase__		):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
           return
       snake_case					:   Dict         =					os.path.join(
           UpperCamelCase__		,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

       return (out_vocab_file,)









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Optional[Any]													=						'''big_bird'''







   def __init__(			self		,			UpperCamelCase__=5_0358		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu_new"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=4096		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=True		,			UpperCamelCase__=0		,			UpperCamelCase__=1		,			UpperCamelCase__=2		,			UpperCamelCase__=66		,			UpperCamelCase__="block_sparse"		,			UpperCamelCase__=True		,			UpperCamelCase__=False		,			UpperCamelCase__=64		,			UpperCamelCase__=3		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Tuple:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			sep_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)

       snake_case					:   Union[str, Any]         =					vocab_size
       snake_case					:   List[Any]         =					max_position_embeddings
       snake_case					:   int         =					hidden_size
       snake_case					:   str         =					num_hidden_layers
       snake_case					:   Any         =					num_attention_heads
       snake_case					:   int         =					intermediate_size
       snake_case					:   Union[str, Any]         =					hidden_act
       snake_case					:   Optional[Any]         =					hidden_dropout_prob
       snake_case					:   List[Any]         =					attention_probs_dropout_prob
       snake_case					:   int         =					initializer_range
       snake_case					:   List[str]         =					type_vocab_size
       snake_case					:   Optional[Any]         =					layer_norm_eps
       snake_case					:   Optional[Any]         =					use_cache

       snake_case					:   List[Any]         =					rescale_embeddings
       snake_case					:   Any         =					attention_type
       snake_case					:   List[Any]         =					use_bias
       snake_case					:   int         =					block_size
       snake_case					:   int         =					num_random_blocks
       snake_case					:   Optional[int]         =					classifier_dropout







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   int         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Optional[Any]         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





