


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


SCREAMING_SNAKE_CASE       :		Any         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		str         =				"""▁"""

SCREAMING_SNAKE_CASE       :		Dict         =				{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

SCREAMING_SNAKE_CASE       :		Dict         =				{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

SCREAMING_SNAKE_CASE       :		Optional[int]         =				{"""vinai/bartpho-syllable""": 1_024}






class      UpperCamelCase					(		lowercase__     ):


			'''simple docstring'''







			lowercase					:	Tuple									=VOCAB_FILES_NAMES
			lowercase					:	Tuple									=PRETRAINED_VOCAB_FILES_MAP
			lowercase					:	Union[str, Any]									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase					:	List[Any]									=["input_ids", "attention_mask"]
			def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_="<s>"   ,							UpperCamelCase_="</s>"   ,							UpperCamelCase_="</s>"   ,							UpperCamelCase_="<s>"   ,							UpperCamelCase_="<unk>"   ,							UpperCamelCase_="<pad>"   ,							UpperCamelCase_="<mask>"   ,							UpperCamelCase_ = None   ,							**UpperCamelCase_   ,							):
								lowercase_       :Any     =       AddedToken(UpperCamelCase_   ,							lstrip=UpperCamelCase_   ,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_   ,							UpperCamelCase_		) else mask_token

								lowercase_       :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_   ,							)

								lowercase_       :Optional[int]     =       vocab_file
								lowercase_       :int     =       monolingual_vocab_file
								lowercase_       :Dict     =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(UpperCamelCase_		)		)

								# Load the reduced vocab

								# Keep order of special tokens for backward compatibility
								lowercase_       :Optional[int]     =       {}
								lowercase_       :Tuple     =       0
								for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
													if str(UpperCamelCase_		) not in self.fairseq_tokens_to_ids:
																		lowercase_       :List[Any]     =       cnt
																		cnt += 1
								with open(UpperCamelCase_   ,							'''r'''   ,							encoding='''utf-8'''		) as f:
													for line in f.readlines():
																		lowercase_       :Optional[int]     =       line.strip().split()[0]
																		lowercase_       :Any     =       len(self.fairseq_tokens_to_ids		)
								if str(UpperCamelCase_		) not in self.fairseq_tokens_to_ids:
													lowercase_       :Optional[int]     =       len(self.fairseq_tokens_to_ids		)

								lowercase_       :Tuple     =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
			def __getstate__(     self		):
								lowercase_       :str     =       self.__dict__.copy()
								lowercase_       :List[str]     =       None
								lowercase_       :Tuple     =       self.sp_model.serialized_model_proto()
								return state
			def __setstate__(     self   ,							UpperCamelCase_		):
								lowercase_       :List[str]     =       d

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

								lowercase_       :Tuple     =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):




								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowercase_       :int     =       [self.cls_token_id]
								lowercase_       :Tuple     =       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = False		):




								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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
								lowercase_       :Dict     =       [self.sep_token_id]
								lowercase_       :Union[str, 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 		UpperCamelCase  (     self		):
								return len(self.fairseq_ids_to_tokens		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :List[Any]     =       {self.convert_ids_to_tokens(UpperCamelCase_		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
								return self.sp_model.encode(UpperCamelCase_   ,							out_type=UpperCamelCase_		)
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):




								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								else:
													return self.unk_token_id
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
								return self.fairseq_ids_to_tokens[index]
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
								lowercase_       :Optional[int]     =       ''''''.join(UpperCamelCase_		).replace(UpperCamelCase_   ,							''' '''		).strip()
								return out_string




			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):




								if not os.path.isdir(UpperCamelCase_		):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
													return
								lowercase_       :Optional[Any]     =       os.path.join(
								    UpperCamelCase_   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
								lowercase_       :Union[str, Any]     =       os.path.join(
								    UpperCamelCase_   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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:
																		lowercase_       :Optional[Any]     =       self.sp_model.serialized_model_proto()
																		fi.write(UpperCamelCase_		)

								if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
								    UpperCamelCase_		) and os.path.isfile(self.monolingual_vocab_file		):
													copyfile(self.monolingual_vocab_file   ,							UpperCamelCase_		)
								elif not os.path.isfile(self.monolingual_vocab_file		):
													with open(UpperCamelCase_   ,							'''w'''   ,							encoding='''utf-8'''		) as fp:
																		for token in self.fairseq_tokens_to_ids:
																							if token not in self.all_special_tokens:
																												fp.write(f"{str(UpperCamelCase_		)} \n"		)

								return out_vocab_file, out_monolingual_vocab_file





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

