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""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

_UpperCAmelCase            =		{
    """camembert-base""": 512,
}

_UpperCAmelCase            =		"""▁"""





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']
				def __init__(						self		,     lowercase		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=["<s>NOTUSED", "</s>NOTUSED"]		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[str]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else mask_token

						A_		:    Dict         =				{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=lowercase		,     eos_token=lowercase		,     unk_token=lowercase		,     sep_token=lowercase		,     cls_token=lowercase		,     pad_token=lowercase		,     mask_token=lowercase		,     additional_special_tokens=lowercase		,     sp_model_kwargs=self.sp_model_kwargs		,     **lowercase		,     )
						A_		:    Tuple         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(str(lowercase ) )
						A_		:    Optional[Any]         =				vocab_file
						# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
						# sentencepiece vocabulary (this is the case for <s> and </s>
						A_		:    Union[str, Any]         =				{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
						A_		:    str         =				len(self.fairseq_tokens_to_ids )
						A_		:    Optional[Any]         =				len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
						A_		:    Optional[Any]         =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A_		:    int         =				[self.cls_token_id]
						A_		:    str         =				[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





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

						if token_ids_a is None:
								return [1] + ([0] * len(lowercase )) + [1]
						return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    List[Any]         =				[self.sep_token_id]
						A_		:    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 ):



						"""simple docstring"""





						return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				{self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
						vocab.update(self.added_tokens_encoder )
						return vocab
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.sp_model.encode(lowercase		,     out_type=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
						elif self.sp_model.PieceToId(lowercase ) == 0:
								# Convert sentence piece unk token to fairseq unk token index
								return self.unk_token_id
						return self.fairseq_offset + self.sp_model.PieceToId(lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						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		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[]
						A_		:    List[Any]         =				''
						A_		:    Tuple         =				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(lowercase ) + token
										A_		:    Optional[int]         =				True
										A_		:    Dict         =				[]
								else:
										current_sub_tokens.append(lowercase )
										A_		:    Dict         =				False
						out_string += self.sp_model.decode(lowercase )
						return out_string.strip()
				def __getstate__(						self ):



						"""simple docstring"""





						A_		:    Dict         =				self.__dict__.copy()
						A_		:    str         =				None
						return state
				def __setstate__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    int         =				d

						# for backward compatibility
						if not hasattr(self		,     'sp_model_kwargs' ):
								A_		:    List[str]         =				{}

						A_		:    Optional[Any]         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(self.vocab_file )







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





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

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

						return (out_vocab_file,)







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






