



"""simple docstring"""






import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase					:					int 		=		logging.get_logger(__name__)

_lowercase					:					Union[str, Any] 		=		{
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase					:					List[str] 		=		{
    "source_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
    },
    "target_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
    },
    "vocab": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
    },
    "tokenizer_config_file": {
        "Helsinki-NLP/opus-mt-en-de": (
            "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
        )
    },
}

_lowercase					:					Optional[int] 		=		{"Helsinki-NLP/opus-mt-en-de": 5_1_2}
_lowercase					:					List[Any] 		=		{}

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				VOCAB_FILES_NAMES
					_a       =				PRETRAINED_VOCAB_FILES_MAP
					_a       =				PRETRAINED_INIT_CONFIGURATION
					_a       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a       =				['input_ids', 'attention_mask']
					_a       =				re.compile('>>.+<<'  )  # type: re.Pattern



					def __init__(			self				:    Optional[int],    lowerCamelCase				:    str,    lowerCamelCase				:    List[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    Tuple=None,    lowerCamelCase				:    Tuple=None,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Dict="<unk>",    lowerCamelCase				:    Optional[Any]="</s>",    lowerCamelCase				:    List[str]="<pad>",    lowerCamelCase				:    Any=512,    lowerCamelCase				:    Optional[Dict[str, Any]] = None,    lowerCamelCase				:    str=False,    **lowerCamelCase				:    List[str],    )->   None:
								lowerCamelCase__						:						Union[str, Any]						      ={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
								    source_lang=lowerCamelCase,    target_lang=lowerCamelCase,    unk_token=lowerCamelCase,    eos_token=lowerCamelCase,    pad_token=lowerCamelCase,    model_max_length=lowerCamelCase,    sp_model_kwargs=self.sp_model_kwargs,    target_vocab_file=lowerCamelCase,    separate_vocabs=lowerCamelCase,    **lowerCamelCase,    )
								assert Path(lowerCamelCase	).exists(), F'''cannot find spm source {source_spm}'''

								lowerCamelCase__						:						Union[str, Any]						      =separate_vocabs
								lowerCamelCase__						:						int						      =load_json(lowerCamelCase	)
								if self.unk_token not in self.encoder:
											raise KeyError('''<unk> token must be in vocab'''	)
								assert self.pad_token in self.encoder

								if separate_vocabs:
											lowerCamelCase__						:						List[str]						      =load_json(lowerCamelCase	)
											lowerCamelCase__						:						List[str]						      ={v: k for k, v in self.target_encoder.items()}
											lowerCamelCase__						:						List[str]						      =[]
								else:
											lowerCamelCase__						:						List[str]						      ={v: k for k, v in self.encoder.items()}
											lowerCamelCase__						:						list						      =[k for k in self.encoder if k.startswith('''>>'''	) and k.endswith('''<<'''	)]

								lowerCamelCase__						:						List[str]						      =source_lang
								lowerCamelCase__						:						Optional[int]						      =target_lang
								lowerCamelCase__						:						Union[str, Any]						      =[source_spm, target_spm]

								# load SentencePiece model for pre-processing
								lowerCamelCase__						:						Optional[Any]						      =load_spm(lowerCamelCase,    self.sp_model_kwargs	)
								lowerCamelCase__						:						Any						      =load_spm(lowerCamelCase,    self.sp_model_kwargs	)
								lowerCamelCase__						:						str						      =self.spm_source
								lowerCamelCase__						:						List[str]						      =self.encoder

								# Multilingual target side: default to using first supported language code.

								self._setup_normalizer()



					def 						snake_case			(			self				:    Dict	)->   str:
								try:
											from sacremoses import MosesPunctNormalizer

											lowerCamelCase__						:						List[Any]						      =MosesPunctNormalizer(self.source_lang	).normalize
								except (ImportError, FileNotFoundError):
											warnings.warn('''Recommended: pip install sacremoses.'''	)
											lowerCamelCase__						:						Any						      =lambda lowerCamelCase	: x



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    str	)->   str:
								return self.punc_normalizer(lowerCamelCase	) if x else ""



					def 						snake_case			(			self				:    str,    lowerCamelCase				:    Union[str, Any]	)->   Union[str, Any]:
								return self.current_encoder.get(lowerCamelCase,    self.current_encoder[self.unk_token]	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    str	)->   Optional[int]:
								lowerCamelCase__						:						int						      =self.language_code_re.match(lowerCamelCase	)
								lowerCamelCase__						:						list						      =[match.group(0	)] if match else []
								return code, self.language_code_re.sub('''''',    lowerCamelCase	)



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    str	)->   List[str]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Optional[int]						      =self.remove_language_code(lowerCamelCase	)
								lowerCamelCase__						:						int						      =self.current_spm.encode(lowerCamelCase,    out_type=lowerCamelCase	)
								return code + pieces



					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    int	)->   str:
								return self.decoder.get(lowerCamelCase,    self.unk_token	)



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Optional[int],    **lowerCamelCase				:    int	)->   Dict:
								return super().batch_decode(lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    int,    **lowerCamelCase				:    Optional[Any]	)->   Tuple:
								return super().decode(lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    List[str]	)->   str:
								lowerCamelCase__						:						Optional[Any]						      =self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								lowerCamelCase__						:						str						      =[]
								lowerCamelCase__						:						List[str]						      =''''''
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														out_string += sp_model.decode_pieces(lowerCamelCase	) + token + " "
														lowerCamelCase__						:						Optional[int]						      =[]
											else:
														current_sub_tokens.append(lowerCamelCase	)
								out_string += sp_model.decode_pieces(lowerCamelCase	)
								return out_string.strip()



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    List[Any],    lowerCamelCase				:    int=None	)->   List[int]:
								if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]



					def 						snake_case			(			self				:    Optional[Any]	)->   Optional[Any]:
								lowerCamelCase__						:						List[str]						      =self.spm_source
								lowerCamelCase__						:						Optional[int]						      =self.encoder



					def 						snake_case			(			self				:    Dict	)->   Optional[int]:
								lowerCamelCase__						:						Optional[int]						      =self.spm_target
								if self.separate_vocabs:
											lowerCamelCase__						:						Union[str, Any]						      =self.target_encoder



					@property
					def 						snake_case			(			self				:    Optional[Any]	)->   int:
								return len(self.encoder	)



					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    str,    lowerCamelCase				:    Optional[str] = None	)->   Tuple[str]:
								if not os.path.isdir(lowerCamelCase	):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
											return
								lowerCamelCase__						:						Union[str, Any]						      =[]

								if self.separate_vocabs:
											lowerCamelCase__						:						Optional[int]						      =os.path.join(
											    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab'''],    )
											lowerCamelCase__						:						Union[str, Any]						      =os.path.join(
											    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file'''],    )
											save_json(self.encoder,    lowerCamelCase	)
											save_json(self.target_encoder,    lowerCamelCase	)
											saved_files.append(lowerCamelCase	)
											saved_files.append(lowerCamelCase	)
								else:
											lowerCamelCase__						:						Dict						      =os.path.join(
											    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']	)
											save_json(self.encoder,    lowerCamelCase	)
											saved_files.append(lowerCamelCase	)

								for spm_save_filename, spm_orig_path, spm_model in zip(
								    [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']],    self.spm_files,    [self.spm_source, self.spm_target],    ):
											lowerCamelCase__						:						str						      =os.path.join(
											    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename	)
											if os.path.abspath(lowerCamelCase	) != os.path.abspath(lowerCamelCase	) and os.path.isfile(lowerCamelCase	):
														copyfile(lowerCamelCase,    lowerCamelCase	)
														saved_files.append(lowerCamelCase	)
											elif not os.path.isfile(lowerCamelCase	):
														with open(lowerCamelCase,    '''wb'''	) as fi:
																	lowerCamelCase__						:						Union[str, Any]						      =spm_model.serialized_model_proto()
																	fi.write(lowerCamelCase	)
														saved_files.append(lowerCamelCase	)

								return tuple(lowerCamelCase	)



					def 						snake_case			(			self				:    Optional[Any]	)->   Dict:
								return self.get_src_vocab()



					def 						snake_case			(			self				:    Dict	)->   List[str]:
								return dict(self.encoder,    **self.added_tokens_encoder	)



					def 						snake_case			(			self				:    Union[str, Any]	)->   str:
								return dict(self.target_encoder,    **self.added_tokens_decoder	)



					def __getstate__(			self				:    Union[str, Any]	)->   Dict:
								lowerCamelCase__						:						int						      =self.__dict__.copy()
								state.update(
								    {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}	)
								return state



					def __setstate__(			self				:    Union[str, Any],    lowerCamelCase				:    Dict	)->   None:
								lowerCamelCase__						:						Dict						      =d

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

								lowerCamelCase__      ,  lowerCamelCase__						:						Optional[Any]						      =(load_spm(lowerCamelCase,    self.sp_model_kwargs	) for f in self.spm_files)
								lowerCamelCase__						:						str						      =self.spm_source
								self._setup_normalizer()



					def 						snake_case			(			self				:    List[Any],    *lowerCamelCase				:    Dict,    **lowerCamelCase				:    Tuple	)->   Dict:
								return 1



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    Any	)->   Tuple:
								lowerCamelCase__						:						List[str]						      =set(self.all_special_ids	)  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special
								return [1 if x in all_special_ids else 0 for x in seq]




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    List,    lowerCamelCase				:    Optional[List] = None,    lowerCamelCase				:    bool = False	)->   List[int]:
								if already_has_special_tokens:
											return self._special_token_mask(lowerCamelCase	)
								elif token_ids_a is None:
											return self._special_token_mask(lowerCamelCase	) + [1]
								else:
											return self._special_token_mask(token_ids_a + token_ids_a	) + [1]






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Dict[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =sentencepiece.SentencePieceProcessor(**__lowerCamelCase      )
			spm.Load(__lowerCamelCase      )
			return spm






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       str      ):
			"""simple docstring"""
			with open(__lowerCamelCase				,						'''w'''      ) as f:
						json.dump(__lowerCamelCase				,						__lowerCamelCase				,						indent=2      )






def 	snake_case__  (						__lowerCamelCase							:       str      ):
			"""simple docstring"""
			with open(__lowerCamelCase				,						'''r'''      ) as f:
						return json.load(__lowerCamelCase      )









"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_lowercase					:					str 		=		logging.get_logger(__name__)

_lowercase					:					List[str] 		=		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase					:					Tuple 		=		{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

_lowercase					:					str 		=		{
    "allenai/longformer-base-4096": 4_0_9_6,
    "allenai/longformer-large-4096": 4_0_9_6,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4_0_9_6,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4_0_9_6,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4_0_9_6,
}

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				VOCAB_FILES_NAMES
					_a       =				PRETRAINED_VOCAB_FILES_MAP
					_a       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a       =				['input_ids', 'attention_mask']
					_a       =				LongformerTokenizer



					def __init__(			self				:    Dict,    lowerCamelCase				:    Any=None,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    Optional[Any]="replace",    lowerCamelCase				:    List[str]="<s>",    lowerCamelCase				:    Tuple="</s>",    lowerCamelCase				:    Union[str, Any]="</s>",    lowerCamelCase				:    str="<s>",    lowerCamelCase				:    Union[str, Any]="<unk>",    lowerCamelCase				:    Optional[Any]="<pad>",    lowerCamelCase				:    Optional[Any]="<mask>",    lowerCamelCase				:    int=False,    lowerCamelCase				:    Optional[Any]=True,    **lowerCamelCase				:    Union[str, Any],    )->   List[str]:
								super().__init__(
								    lowerCamelCase,    lowerCamelCase,    tokenizer_file=lowerCamelCase,    errors=lowerCamelCase,    bos_token=lowerCamelCase,    eos_token=lowerCamelCase,    sep_token=lowerCamelCase,    cls_token=lowerCamelCase,    unk_token=lowerCamelCase,    pad_token=lowerCamelCase,    mask_token=lowerCamelCase,    add_prefix_space=lowerCamelCase,    trim_offsets=lowerCamelCase,    **lowerCamelCase,    )

								lowerCamelCase__						:						List[str]						      =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
								if pre_tok_state.get('''add_prefix_space''',    lowerCamelCase	) != add_prefix_space:
											lowerCamelCase__						:						Dict						      =getattr(lowerCamelCase,    pre_tok_state.pop('''type'''	)	)
											lowerCamelCase__						:						int						      =add_prefix_space
											lowerCamelCase__						:						int						      =pre_tok_class(**lowerCamelCase	)

								lowerCamelCase__						:						Optional[int]						      =add_prefix_space

								lowerCamelCase__						:						str						      ='''post_processor'''
								lowerCamelCase__						:						int						      =getattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase	)
								if tokenizer_component_instance:
											lowerCamelCase__						:						Union[str, Any]						      =json.loads(tokenizer_component_instance.__getstate__()	)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
														lowerCamelCase__						:						List[str]						      =tuple(state['''sep''']	)
											if "cls" in state:
														lowerCamelCase__						:						List[str]						      =tuple(state['''cls''']	)

											lowerCamelCase__						:						Dict						      =False

											if state.get('''add_prefix_space''',    lowerCamelCase	) != add_prefix_space:
														lowerCamelCase__						:						int						      =add_prefix_space
														lowerCamelCase__						:						int						      =True

											if state.get('''trim_offsets''',    lowerCamelCase	) != trim_offsets:
														lowerCamelCase__						:						Optional[int]						      =trim_offsets
														lowerCamelCase__						:						Union[str, Any]						      =True

											if changes_to_apply:
														lowerCamelCase__						:						Dict						      =getattr(lowerCamelCase,    state.pop('''type'''	)	)
														lowerCamelCase__						:						Dict						      =component_class(**lowerCamelCase	)
														setattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase	)



					@property
					def 						snake_case			(			self				:    str	)->   str:
								if self._mask_token is None:
											if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''	)
											return None
								return str(self._mask_token	)



					@mask_token.setter
					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    List[Any]	)->   List[str]:
								lowerCamelCase__						:						List[str]						      =AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else value
								lowerCamelCase__						:						Any						      =value



					def 						snake_case			(			self				:    Optional[Any],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    Dict	)->   BatchEncoding:
								lowerCamelCase__						:						Union[str, Any]						      =kwargs.get('''is_split_into_words''',    lowerCamelCase	)
								assert self.add_prefix_space or not is_split_into_words, (
								    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    List[str],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    List[Any]	)->   BatchEncoding:
								lowerCamelCase__						:						List[Any]						      =kwargs.get('''is_split_into_words''',    lowerCamelCase	)

								assert self.add_prefix_space or not is_split_into_words, (
								    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    str,    lowerCamelCase				:    str,    lowerCamelCase				:    Optional[str] = None	)->   Tuple[str]:
								lowerCamelCase__						:						str						      =self._tokenizer.model.save(lowerCamelCase,    name=lowerCamelCase	)
								return tuple(lowerCamelCase	)



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    int,    lowerCamelCase				:    int=None	)->   str:
								lowerCamelCase__						:						int						      =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
											return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    List[int],    lowerCamelCase				:    Optional[List[int]] = None	)->   List[int]:
								lowerCamelCase__						:						Tuple						      =[self.sep_token_id]
								lowerCamelCase__						:						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]




