





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_rembert import RemBertTokenizer
else:
						SCREAMING_SNAKE_CASE_						       =	None

SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_						       =	{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_						       =	{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE_						       =	{
    """google/rembert""": 2_5_6,
}

SCREAMING_SNAKE_CASE_						       =	"""▁"""

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



					'''simple docstring'''

					__snake_case   :     Dict		     =      VOCAB_FILES_NAMES
					__snake_case   :     Any		     =      PRETRAINED_VOCAB_FILES_MAP
					__snake_case   :     List[Any]		     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__snake_case   :     Dict		     =      RemBertTokenizer






					def __init__(							self						:   Any						,lowerCamelCase__						:   Any=None						,lowerCamelCase__						:   Optional[int]=None						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Optional[Any]=False						,lowerCamelCase__						:   Dict="[CLS]"						,lowerCamelCase__						:   Tuple="[SEP]"						,lowerCamelCase__						:   Optional[int]="<unk>"						,lowerCamelCase__						:   Union[str, Any]="[SEP]"						,lowerCamelCase__						:   Dict="<pad>"						,lowerCamelCase__						:   int="[CLS]"						,lowerCamelCase__						:   int="[MASK]"						,**lowerCamelCase__						:   Union[str, Any]						,)					->		Tuple:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else mask_token

											super().__init__(
											    lowerCamelCase__						,tokenizer_file=lowerCamelCase__						,do_lower_case=lowerCamelCase__						,remove_space=lowerCamelCase__						,keep_accents=lowerCamelCase__						,bos_token=lowerCamelCase__						,eos_token=lowerCamelCase__						,unk_token=lowerCamelCase__						,sep_token=lowerCamelCase__						,pad_token=lowerCamelCase__						,cls_token=lowerCamelCase__						,mask_token=lowerCamelCase__						,**lowerCamelCase__						,)

											SCREAMING_SNAKE_CASE		=						do_lower_case
											SCREAMING_SNAKE_CASE		=						remove_space
											SCREAMING_SNAKE_CASE		=						keep_accents
											SCREAMING_SNAKE_CASE		=						vocab_file
											SCREAMING_SNAKE_CASE		=						False if not self.vocab_file else True






					def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						[self.sep_token_id]
											SCREAMING_SNAKE_CASE		=						[self.cls_token_id]
											if token_ids_a is None:
																	return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep






					def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None						,lowerCamelCase__						:   bool = False	)					->		List[int]:




											'''simple docstring'''


											if already_has_special_tokens:
																	if token_ids_a is not None:
																							raise ValueError(
																							    """You should not supply a second sequence if the provided sequence of """
																							    """ids is already formatted with special tokens for the model."""	)
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

											if token_ids_a is not None:
																	return [1] + ([0] * len(lowerCamelCase__	)) + [1] + ([0] * len(lowerCamelCase__	)) + [1]
											return [1] + ([0] * len(lowerCamelCase__	)) + [1]






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						[self.sep_token_id]
											SCREAMING_SNAKE_CASE		=						[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	) * [0] + len(token_ids_a + sep	) * [1]






					def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Optional[str] = None	)					->		Tuple[str]:




											'''simple docstring'''


											if not os.path.isdir(lowerCamelCase__	):
																	logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCamelCase__	)	)
																	return
											SCREAMING_SNAKE_CASE		=						os.path.join(
											    lowerCamelCase__						,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

											if os.path.abspath(self.vocab_file	) != os.path.abspath(lowerCamelCase__	):
																	copyfile(self.vocab_file						,lowerCamelCase__	)

											return (out_vocab_file,)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MegatronBertForCausalLM""",
          """MegatronBertForMaskedLM""",
          """MegatronBertForMultipleChoice""",
          """MegatronBertForNextSentencePrediction""",
          """MegatronBertForPreTraining""",
          """MegatronBertForQuestionAnswering""",
          """MegatronBertForSequenceClassification""",
          """MegatronBertForTokenClassification""",
          """MegatronBertModel""",
          """MegatronBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_megatron_bert import (
                MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegatronBertForCausalLM,
                MegatronBertForMaskedLM,
                MegatronBertForMultipleChoice,
                MegatronBertForNextSentencePrediction,
                MegatronBertForPreTraining,
                MegatronBertForQuestionAnswering,
                MegatronBertForSequenceClassification,
                MegatronBertForTokenClassification,
                MegatronBertModel,
                MegatronBertPreTrainedModel,
            )

else:
      import sys

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


