



"""simple docstring"""




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:
		_lowercase		:				Any      		=None

_lowercase		:				Optional[int]      		=logging.get_logger(__name__)
_lowercase		:				str      		={"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase		:				Tuple      		={
    "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",
    },
}

_lowercase		:				List[Any]      		={
    "google/rembert": 256,
}

_lowercase		:				Dict      		="▁"

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Union[str, Any]      =					VOCAB_FILES_NAMES
						__lowerCAmelCase					:Tuple      =					PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase					:str      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase					:List[Any]      =					RemBertTokenizer

						def __init__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=True ,       __lowercase=True ,       __lowercase=False ,       __lowercase="[CLS]" ,       __lowercase="[SEP]" ,       __lowercase="<unk>" ,       __lowercase="[SEP]" ,       __lowercase="<pad>" ,       __lowercase="[CLS]" ,       __lowercase="[MASK]" ,       **__lowercase ,       ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[Any]							=  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_ ,       )

									a__       :						Optional[int]							=  do_lower_case
									a__       :						Any							=  remove_space
									a__       :						Any							=  keep_accents
									a__       :						Dict							=  vocab_file
									a__       :						Optional[int]							=  False if not self.vocab_file else True

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				Any:
									"""simple docstring"""




									a__       :						Optional[Any]							=  [self.sep_token_id]
									a__       :						Dict							=  [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 ,       __lowercase ,       __lowercase = None ,       __lowercase = False  ) ->				Union[str, Any]:
									"""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 ,       __lowercase ,       __lowercase = None  ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[Any]							=  [self.sep_token_id]
									a__       :						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  ) * [0] + len(token_ids_a + sep  ) * [1]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				Any:
									"""simple docstring"""




									if not os.path.isdir(lowerCamelCase_  ):
												logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCamelCase_  )  )
												return
									a__       :						int							=  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_flax_available, is_torch_available


_lowercase		:				int      		={
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Optional[Any]      		=[
		    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "LongT5EncoderModel",
		    "LongT5ForConditionalGeneration",
		    "LongT5Model",
		    "LongT5PreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				List[Any]      		=[
		    "FlaxLongT5ForConditionalGeneration",
		    "FlaxLongT5Model",
		    "FlaxLongT5PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longta import (
				    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongTaEncoderModel,
				    LongTaForConditionalGeneration,
				    LongTaModel,
				    LongTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_longta import (
				    FlaxLongTaForConditionalGeneration,
				    FlaxLongTaModel,
				    FlaxLongTaPreTrainedModel,
				)


else:
		import sys

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




