






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							:   int							=  logging.get_logger(__name__)

__UpperCAmelCase							:   List[Any]							=  "▁"

__UpperCAmelCase							:   Optional[Any]							=  {"vocab_file": "sentencepiece.bpe.model"}

__UpperCAmelCase							:   str							=  {
    "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"
        ),
    }
}

__UpperCAmelCase							:   Optional[Any]							=  {
    "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 					_snake_case						(			_A ):
	_A          =   VOCAB_FILES_NAMES
	_A          =   PRETRAINED_VOCAB_FILES_MAP
	_A          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A          =   ['input_ids', 'attention_mask']






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

					snake_case__      :Optional[Any]         =   {} if sp_model_kwargs is None else sp_model_kwargs

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

					snake_case__      :Any         =   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(str(UpperCamelCase       )       )
					snake_case__      :List[str]         =   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
					snake_case__      :List[str]         =   {"<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
					snake_case__      :Tuple         =   1

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






	def __getstate__(		self       )						->    Optional[int]:
					snake_case__      :Optional[int]         =   self.__dict__.copy()
					snake_case__      :str         =   None
					snake_case__      :List[Any]         =   self.sp_model.serialized_model_proto()
					return state






	def __setstate__(		self					,UpperCamelCase       )						->    Optional[Any]:
					snake_case__      :Tuple         =   d

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

					snake_case__      :str         =   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:

					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__      :Tuple         =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = False       )						->    List[int]:

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

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :str         =   [self.sep_token_id]
					snake_case__      :List[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]






	@property
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					return len(self.sp_model       ) + self.fairseq_offset + 1  # Add the <mask> token






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Dict         =   {self.convert_ids_to_tokens(UpperCamelCase       ): i for i in range(self.vocab_size       )}
					vocab.update(self.added_tokens_encoder       )
					return vocab






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    List[str]:
					return self.sp_model.encode(UpperCamelCase					,out_type=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Tuple:
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					snake_case__      :List[str]         =   self.sp_model.PieceToId(UpperCamelCase       )

					# 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					,UpperCamelCase       )						->    List[Any]:
					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					,UpperCamelCase       )						->    Optional[Any]:
					snake_case__      :Dict         =   "".join(UpperCamelCase       ).replace(UpperCamelCase					," "       ).strip()
					return out_string






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    Tuple[str]:
					if not os.path.isdir(UpperCamelCase       ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
									return
					snake_case__      :List[Any]         =   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       ) and os.path.isfile(self.vocab_file       ):
									copyfile(self.vocab_file					,UpperCamelCase       )
					elif not os.path.isfile(self.vocab_file       ):
									with open(UpperCamelCase					,"wb"       ) as fi:
													snake_case__      :List[str]         =   self.sp_model.serialized_model_proto()
													fi.write(UpperCamelCase       )

					return (out_vocab_file,)









import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )