
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


lowercase__          =logging.get_logger(__name__)

lowercase__          ='▁'

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

lowercase__          ={
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowercase__          ={
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							int     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Dict     	=				["input_ids", "attention_mask"]
   def __init__(self			: List[str] ,    snake_case_			: Tuple ,    snake_case_			: Union[str, Any]="<s>" ,    snake_case_			: Dict="</s>" ,    snake_case_			: List[str]="</s>" ,    snake_case_			: Any="<s>" ,    snake_case_			: int="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Any="<mask>" ,    snake_case_			: Optional[Dict[str, Any]] = None ,    **snake_case_			: str ,    ):
    # Mask token behave like a normal word, i.e. include the space before it
    __a			:  Any		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else mask_token

    __a			:  List[str]		    =  {} 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			:  List[Any]		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
    self.sp_model.Load(str(snake_case_       )       )
    __a			:  Tuple		    =  vocab_file

    # Original fairseq vocab and spm vocab must be "aligned":
    # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
    # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
    # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
    # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

    # Mimic fairseq token-to-id alignment for the first 4 token
    __a			:  Optional[int]		    =  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

    # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
    __a			:  Optional[Any]		    =  1

    __a			:  List[str]		    =  len(self.sp_model       ) + self.fairseq_offset
    __a			:  str		    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
   def __getstate__(self			: Union[str, Any]       ):
    __a			:  Dict		    =  self.__dict__.copy()
    __a			:  List[str]		    =  None
    __a			:  List[Any]		    =  self.sp_model.serialized_model_proto()
    return state
   def __setstate__(self			: Tuple ,    snake_case_			: str       ):
    __a			:  List[Any]		    =  d

    # for backward compatibility
    if not hasattr(self ,    '''sp_model_kwargs'''       ):
     __a			:  Optional[int]		    =  {}

    __a			:  List[Any]		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):

    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __a			:  Any		    =  [self.cls_token_id]
    __a			:  Optional[int]		    =  [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):

    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 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Tuple		    =  [self.sep_token_id]
    __a			:  List[str]		    =  [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 				lowerCAmelCase      (self			: str       ):
    return len(self.sp_model       ) + self.fairseq_offset + 1  # Add the <mask> token
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  int		    =  {self.convert_ids_to_tokens(snake_case_       ): i for i in range(self.vocab_size       )}
    vocab.update(self.added_tokens_encoder       )
    return vocab
   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: str       ):
    return self.sp_model.encode(snake_case_ ,    out_type=snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str       ):
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    __a			:  List[str]		    =  self.sp_model.PieceToId(snake_case_       )

    # Need to return unknown token if the SP model returned 0
    return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: str       ):
    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset       )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: str       ):
    __a			:  Optional[int]		    =  ''''''.join(snake_case_       ).replace(snake_case_ ,    ''' '''       ).strip()
    return out_string







   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    if not os.path.isdir(snake_case_       ):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
     return
    __a			:  Tuple		    =  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			:  int		    =  self.sp_model.serialized_model_proto()
      fi.write(snake_case_       )

    return (out_vocab_file,)


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  self.tokenizer.pad(
        snake_case_ ,    padding=self.padding ,    max_length=self.max_length ,    pad_to_multiple_of=self.pad_to_multiple_of ,    return_tensors='''pt''' if labels is None else None ,    )

    if labels is None:
     return batch

    __a			:  List[str]		    =  torch.tensor(batch['''entity_ids''']       ).shape[1]
    __a			:  Tuple		    =  self.tokenizer.padding_side
    if padding_side == "right":
     __a			:  Union[str, Any]		    =  [
         list(snake_case_       ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) for label in labels
     ]
    else:
     __a			:  Dict		    =  [
         [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) + list(snake_case_       ) for label in labels
     ]

    __a			:  Dict		    =  [feature['''ner_tags'''] for feature in features]
    __a			:  Optional[Any]		    =  padding_tensor(snake_case_ ,    -1 ,    snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  [feature['''original_entity_spans'''] for feature in features]
    __a			:  Optional[int]		    =  padding_tensor(snake_case_ ,    (-1, -1) ,    snake_case_ ,    snake_case_       )
    __a			:  List[str]		    =  {k: torch.tensor(snake_case_ ,    dtype=torch.intaa       ) for k, v in batch.items()}

    return batch
