

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


__A			:					Dict						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			'▁'

__A			:					Any						=			{'vocab_file': 'sentencepiece.bpe.model'}

__A			:					int						=			{
    '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'
        ),
    }
}

__A			:					str						=			{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      ["input_ids", "attention_mask"]





   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			List[Any]="<s>"     , __lowerCamelCase					:			str="</s>"     , __lowerCamelCase					:			Any="</s>"     , __lowerCamelCase					:			int="<s>"     , __lowerCamelCase					:			List[Any]="<unk>"     , __lowerCamelCase					:			Optional[Any]="<pad>"     , __lowerCamelCase					:			List[str]="<mask>"     , __lowerCamelCase					:			Optional[Dict[str, Any]] = None     , **__lowerCamelCase					:			List[str]     , ):
       # 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

       SCREAMING_SNAKE_CASE										=   {} if sp_model_kwargs is None else sp_model_kwargs

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

       SCREAMING_SNAKE_CASE										=   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(str(__lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   {"<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
       SCREAMING_SNAKE_CASE										=   1

       SCREAMING_SNAKE_CASE										=   len(self.sp_model			) + self.fairseq_offset
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}





   def __getstate__(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.__dict__.copy()
       SCREAMING_SNAKE_CASE										=   None
       SCREAMING_SNAKE_CASE										=   self.sp_model.serialized_model_proto()
       return state





   def __setstate__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   d

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

       SCREAMING_SNAKE_CASE										=   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)





   def      _snake_case		(       self					:			str     , __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      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = False			):

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

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





   def      _snake_case		(       self					:			Tuple     , __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]





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return len(self.sp_model			) + self.fairseq_offset + 1  # Add the <mask> token





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   {self.convert_ids_to_tokens(__lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			str			):
       return self.sp_model.encode(__lowerCamelCase     , out_type=__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Dict			):
       if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
       SCREAMING_SNAKE_CASE										=   self.sp_model.PieceToId(__lowerCamelCase			)

       # 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      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Tuple			):
       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      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			).replace(__lowerCamelCase     , " "			).strip()
       return out_string





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       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			) and os.path.isfile(self.vocab_file			):
           copyfile(self.vocab_file     , __lowerCamelCase			)
       elif not os.path.isfile(self.vocab_file			):
           with open(__lowerCamelCase     , "wb"			) as fi:
               SCREAMING_SNAKE_CASE										=   self.sp_model.serialized_model_proto()
               fi.write(__lowerCamelCase			)

       return (out_vocab_file,)


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__A			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d