




"""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


__lowerCamelCase								=						logging.get_logger(__name__)

__lowerCamelCase								=						{
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowerCamelCase								=						{
    "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"
        )
    },
}

__lowerCamelCase								=						{"Helsinki-NLP/opus-mt-en-de": 5_12}
__lowerCamelCase								=						{}



class  _snake_case				(							A__       ):

				'''simple docstring'''




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





				def __init__(							self      :  Tuple					,							snake_case      :  Dict					,							snake_case      :  str					,							snake_case      :  Optional[Any]					,							snake_case      :  Any=None					,							snake_case      :  List[Any]=None					,							snake_case      :  Union[str, Any]=None					,							snake_case      :  int="<unk>"					,							snake_case      :  str="</s>"					,							snake_case      :  Any="<pad>"					,							snake_case      :  Tuple=512					,							snake_case      :  Optional[Dict[str, Any]] = None					,							snake_case      :  Tuple=False					,							**snake_case      :  List[Any]					,							):
								UpperCAmelCase_					:str      			=      {} 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=snake_case					,							target_lang=snake_case					,							unk_token=snake_case					,							eos_token=snake_case					,							pad_token=snake_case					,							model_max_length=snake_case					,							sp_model_kwargs=self.sp_model_kwargs					,							target_vocab_file=snake_case					,							separate_vocabs=snake_case					,							**snake_case					,							)
								assert Path(snake_case     ).exists(), f'cannot find spm source {source_spm}'

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

								UpperCAmelCase_					:int      			=      source_lang
								UpperCAmelCase_					:str      			=      target_lang
								UpperCAmelCase_					:Any      			=      [source_spm, target_spm]

								# load SentencePiece model for pre-processing
								UpperCAmelCase_					:Optional[Any]      			=      load_spm(snake_case					,							self.sp_model_kwargs     )
								UpperCAmelCase_					:Optional[int]      			=      load_spm(snake_case					,							self.sp_model_kwargs     )
								UpperCAmelCase_					:Any      			=      self.spm_source
								UpperCAmelCase_					:Optional[int]      			=      self.encoder

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

								self._setup_normalizer()





				def 				snake_case_ (							self      :  Optional[int]     ):
								try:
												from sacremoses import MosesPunctNormalizer

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





				def 				snake_case_ (							self      :  int					,							snake_case      :  str     ):
								return self.punc_normalizer(snake_case     ) if x else ""





				def 				snake_case_ (							self      :  int					,							snake_case      :  Any     ):
								return self.current_encoder.get(snake_case					,							self.current_encoder[self.unk_token]     )





				def 				snake_case_ (							self      :  Optional[int]					,							snake_case      :  str     ):
								UpperCAmelCase_					:Union[str, Any]      			=      self.language_code_re.match(snake_case     )
								UpperCAmelCase_					:list      			=      [match.group(0     )] if match else []
								return code, self.language_code_re.sub(''''''					,							snake_case     )





				def 				snake_case_ (							self      :  List[Any]					,							snake_case      :  str     ):
								UpperCAmelCase_			,UpperCAmelCase_					:List[Any]      			=      self.remove_language_code(snake_case     )
								UpperCAmelCase_					:int      			=      self.current_spm.encode(snake_case					,							out_type=snake_case     )
								return code + pieces





				def 				snake_case_ (							self      :  Any					,							snake_case      :  int     ):
								return self.decoder.get(snake_case					,							self.unk_token     )





				def 				snake_case_ (							self      :  int					,							snake_case      :  List[Any]					,							**snake_case      :  Dict     ):
								return super().batch_decode(snake_case					,							**snake_case     )





				def 				snake_case_ (							self      :  Optional[Any]					,							snake_case      :  List[str]					,							**snake_case      :  Dict     ):
								return super().decode(snake_case					,							**snake_case     )





				def 				snake_case_ (							self      :  Optional[int]					,							snake_case      :  List[str]     ):
								UpperCAmelCase_					:List[Any]      			=      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								UpperCAmelCase_					:Optional[Any]      			=      []
								UpperCAmelCase_					:Optional[int]      			=      ''''''
								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(snake_case     ) + token + " "
																UpperCAmelCase_					:int      			=      []
												else:
																current_sub_tokens.append(snake_case     )
								out_string += sp_model.decode_pieces(snake_case     )
								return out_string.strip()





				def 				snake_case_ (							self      :  Optional[Any]					,							snake_case      :  List[Any]					,							snake_case      :  List[Any]=None     ):
								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      :  Tuple     ):
								UpperCAmelCase_					:List[str]      			=      self.spm_source
								UpperCAmelCase_					:Dict      			=      self.encoder





				def 				snake_case_ (							self      :  Dict     ):
								UpperCAmelCase_					:Tuple      			=      self.spm_target
								if self.separate_vocabs:
												UpperCAmelCase_					:int      			=      self.target_encoder





				@property
				def 				snake_case_ (							self      :  List[Any]     ):
								return len(self.encoder     )





				def 				snake_case_ (							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
								UpperCAmelCase_					:Dict      			=      []

								if self.separate_vocabs:
												UpperCAmelCase_					:Any      			=      os.path.join(
												    snake_case					,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']					,							)
												UpperCAmelCase_					:Any      			=      os.path.join(
												    snake_case					,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']					,							)
												save_json(self.encoder					,							snake_case     )
												save_json(self.target_encoder					,							snake_case     )
												saved_files.append(snake_case     )
												saved_files.append(snake_case     )
								else:
												UpperCAmelCase_					:Any      			=      os.path.join(
												    snake_case					,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']     )
												save_json(self.encoder					,							snake_case     )
												saved_files.append(snake_case     )

								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]					,							):
												UpperCAmelCase_					:List[Any]      			=      os.path.join(
												    snake_case					,							(filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename     )
												if os.path.abspath(snake_case     ) != os.path.abspath(snake_case     ) and os.path.isfile(snake_case     ):
																copyfile(snake_case					,							snake_case     )
																saved_files.append(snake_case     )
												elif not os.path.isfile(snake_case     ):
																with open(snake_case					,							'''wb'''     ) as fi:
																				UpperCAmelCase_					:int      			=      spm_model.serialized_model_proto()
																				fi.write(snake_case     )
																saved_files.append(snake_case     )

								return tuple(snake_case     )





				def 				snake_case_ (							self      :  Any     ):
								return self.get_src_vocab()





				def 				snake_case_ (							self      :  Tuple     ):
								return dict(self.encoder					,							**self.added_tokens_encoder     )





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





				def __getstate__(							self      :  Optional[Any]     ):
								UpperCAmelCase_					:Any      			=      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      :  List[str]					,							snake_case      :  Dict     ):
								UpperCAmelCase_					:str      			=      d

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

								UpperCAmelCase_			,UpperCAmelCase_					:List[Any]      			=      (load_spm(snake_case					,							self.sp_model_kwargs     ) for f in self.spm_files)
								UpperCAmelCase_					:int      			=      self.spm_source
								self._setup_normalizer()





				def 				snake_case_ (							self      :  List[Any]					,							*snake_case      :  Union[str, Any]					,							**snake_case      :  Optional[int]     ):
								return 1





				def 				snake_case_ (							self      :  Any					,							snake_case      :  str     ):
								UpperCAmelCase_					:Tuple      			=      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]					,							snake_case      :  List					,							snake_case      :  Optional[List] = None					,							snake_case      :  bool = False     ):
								if already_has_special_tokens:
												return self._special_token_mask(snake_case     )
								elif token_ids_a is None:
												return self._special_token_mask(snake_case     ) + [1]
								else:
												return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]



def 				a (						__snake_case     :				str,    __snake_case     :				Dict[str, Any]  ):



				'''simple docstring'''





				UpperCAmelCase_					:Any      			=      sentencepiece.SentencePieceProcessor(**__snake_case  )
				spm.Load(__snake_case  )
				return spm



def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				str  ):



				'''simple docstring'''





				with open(__snake_case,    '''w'''  ) as f:
								json.dump(__snake_case,    __snake_case,    indent=2  )





def 				a (						__snake_case     :				str  ):



				'''simple docstring'''





				with open(__snake_case,    '''r'''  ) as f:
								return json.load(__snake_case  )








"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase								=						{
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						["BartTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BartForCausalLM",
						    "BartForConditionalGeneration",
						    "BartForQuestionAnswering",
						    "BartForSequenceClassification",
						    "BartModel",
						    "BartPretrainedModel",
						    "PretrainedBartModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "TFBartForConditionalGeneration",
						    "TFBartForSequenceClassification",
						    "TFBartModel",
						    "TFBartPretrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "FlaxBartDecoderPreTrainedModel",
						    "FlaxBartForCausalLM",
						    "FlaxBartForConditionalGeneration",
						    "FlaxBartForQuestionAnswering",
						    "FlaxBartForSequenceClassification",
						    "FlaxBartModel",
						    "FlaxBartPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase								=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


