



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCAmelCase_		 =					{
    '''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'''
        ),
    },
}

UpperCAmelCase_		 =					{
    '''moussaKam/mbarthez''': 1_0_2_4,
    '''moussaKam/barthez''': 1_0_2_4,
    '''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}

UpperCAmelCase_		 =					'''▁'''


class 	lowerCamelCase__	(       SCREAMING_SNAKE_CASE__):



 """simple docstring"""

 a__					: int =  VOCAB_FILES_NAMES
 a__					: List[str] =  PRETRAINED_VOCAB_FILES_MAP
 a__					: List[str] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: Any =  ["input_ids", "attention_mask"]

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Dict="<s>"			,						__lowerCAmelCase					:					Union[str, Any]="</s>"			,						__lowerCAmelCase					:					int="</s>"			,						__lowerCAmelCase					:					str="<s>"			,						__lowerCAmelCase					:					Optional[Any]="<unk>"			,						__lowerCAmelCase					:					Any="<pad>"			,						__lowerCAmelCase					:					Tuple="<mask>"			,						__lowerCAmelCase					:					Optional[Dict[str, Any]] = None			,						**__lowerCAmelCase					:					Optional[Any]			,						)   ->	Dict:
    _A      =       AddedToken(snake_case__			,						lstrip=snake_case__			,						rstrip=snake_case__      ) if isinstance(snake_case__			,						snake_case__      ) else mask_token

    _A      =       {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=snake_case__			,						eos_token=snake_case__			,						unk_token=snake_case__			,						sep_token=snake_case__			,						cls_token=snake_case__			,						pad_token=snake_case__			,						mask_token=snake_case__			,						sp_model_kwargs=self.sp_model_kwargs			,						**snake_case__			,						)

    _A      =       vocab_file
    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(str(snake_case__      )      )

    _A      =       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

    _A      =       len(self.sp_model      ) - 1
    _A      =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[Any]:

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

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None			,						__lowerCAmelCase					:					bool = False      )   ->	Union[str, Any]:

    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=snake_case__			,						token_ids_a=snake_case__			,						already_has_special_tokens=snake_case__      )

    if token_ids_a is None:
       return [1] + ([0] * len(snake_case__      )) + [1]
    return [1] + ([0] * len(snake_case__      )) + [1, 1] + ([0] * len(snake_case__      )) + [1]

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	Dict:
    _A      =       [self.sep_token_id]
    _A      =       [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]

 @property
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	List[Any]:
    return len(self.sp_model      )

 def     snake_case_      (    self					:					Dict      )   ->	Any:
    _A      =       {self.convert_ids_to_tokens(snake_case__      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					str      )   ->	Any:
    return self.sp_model.encode(snake_case__			,						out_type=snake_case__      )

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	str:

    if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
    _A      =       self.sp_model.PieceToId(snake_case__      )

    return spm_id if spm_id else self.unk_token_id

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					int      )   ->	Union[str, Any]:

    if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(snake_case__      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Tuple      )   ->	Any:
    _A      =       []
    _A      =       ''''''
    _A      =       False
    for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
          if not prev_is_special:
             out_string += " "
          out_string += self.sp_model.decode(snake_case__      ) + token
          _A      =       True
          _A      =       []
       else:
          current_sub_tokens.append(snake_case__      )
          _A      =       False
    out_string += self.sp_model.decode(snake_case__      )
    return out_string.strip()

 def __getstate__(    self					:					int      )   ->	List[str]:
    _A      =       self.__dict__.copy()
    _A      =       None
    return state

 def __setstate__(    self					:					int			,						__lowerCAmelCase					:					Optional[int]      )   ->	Optional[int]:
    _A      =       d

    # for backward compatibility
    if not hasattr(self			,						'''sp_model_kwargs'''      ):
       _A      =       {}

    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(self.vocab_file      )

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple:

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

    if os.path.abspath(self.vocab_file      ) != os.path.abspath(snake_case__      ) and os.path.isfile(self.vocab_file      ):
       copyfile(self.vocab_file			,						snake_case__      )
    elif not os.path.isfile(self.vocab_file      ):
       with open(snake_case__			,						'''wb'''      ) as fi:
          _A      =       self.sp_model.serialized_model_proto()
          fi.write(snake_case__      )

    return (out_vocab_file,)









from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
						from ...onnx.config import PatchingSpec
						from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE:     str									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="longformer"



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[List[int], int] = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   int = 1					,							snake_case__	:   int = 0					,							snake_case__	:   int = 2					,							snake_case__	:   int = 3_05_22					,							snake_case__	:   int = 7_68					,							snake_case__	:   int = 12					,							snake_case__	:   int = 12					,							snake_case__	:   int = 30_72					,							snake_case__	:   str = "gelu"					,							snake_case__	:   float = 0.1					,							snake_case__	:   float = 0.1					,							snake_case__	:   int = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   float = 0.02					,							snake_case__	:   float = 1e-12					,							snake_case__	:   bool = False					,							**snake_case__	:   Tuple					,							):

								"""simple docstring"""

								super().__init__(pad_token_id=snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							attention_window
								SCREAMING_SNAKE_CASE_					      =							sep_token_id
								SCREAMING_SNAKE_CASE_					      =							bos_token_id
								SCREAMING_SNAKE_CASE_					      =							eos_token_id
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							hidden_size
								SCREAMING_SNAKE_CASE_					      =							num_hidden_layers
								SCREAMING_SNAKE_CASE_					      =							num_attention_heads
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							intermediate_size
								SCREAMING_SNAKE_CASE_					      =							hidden_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							type_vocab_size
								SCREAMING_SNAKE_CASE_					      =							initializer_range
								SCREAMING_SNAKE_CASE_					      =							layer_norm_eps
								SCREAMING_SNAKE_CASE_					      =							onnx_export



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Dict					,							snake_case__	:   "PretrainedConfig"					,							snake_case__	:   str = "default"					,							snake_case__	:   "List[PatchingSpec]" = None  ):

								"""simple docstring"""

								super().__init__(snake_case__					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							True



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

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



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().outputs
								if self.task == "default":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
								return outputs



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return 1e-4



						@property
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								return max(super().default_onnx_opset					,							14  )






						def 	__a					(			self	:   List[str]					,							snake_case__	:   "PreTrainedTokenizerBase"					,							snake_case__	:   int = -1					,							snake_case__	:   int = -1					,							snake_case__	:   bool = False					,							snake_case__	:   Optional[TensorType] = None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().generate_dummy_inputs(
								    preprocessor=snake_case__					,							batch_size=snake_case__					,							seq_length=snake_case__					,							is_pair=snake_case__					,							framework=snake_case__  )
								import torch

								# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
								# makes the export fail randomly
								SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(inputs['input_ids']  )
								# make every second token global
								SCREAMING_SNAKE_CASE_					      =							1

								return inputs