






import os
import re
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


lowercase  :							str			  =    logging.get_logger(__name__)

lowercase  :							int			  =    {'''vocab_file''': '''spiece.model'''}

lowercase  :							Union[str, Any]			  =    {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowercase  :							Tuple			  =    {
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}




class 		UpperCAmelCase_						(	UpperCamelCase_					):


						'''simple docstring'''




						A     :   Union[str, Any]						=					VOCAB_FILES_NAMES
						A     :   Optional[int]						=					PRETRAINED_VOCAB_FILES_MAP
						A     :   int						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A     :   Optional[int]						=					['input_ids', 'attention_mask']
						A     :   Tuple						=					[]






						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE="<unk>"		,				_SCREAMING_SNAKE_CASE="<s>"		,				_SCREAMING_SNAKE_CASE="</s>"		,				_SCREAMING_SNAKE_CASE="<pad>"		,				_SCREAMING_SNAKE_CASE="[SEP]"		,				_SCREAMING_SNAKE_CASE="[MASK]"		,				_SCREAMING_SNAKE_CASE="[CLS]"		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				None:
								snake_case_				:							Tuple						 =       AddedToken(__a		,				lstrip=__a		,				rstrip=__a				) if isinstance(__a		,				__a				) else bos_token
								snake_case_				:							List[str]						 =       AddedToken(__a		,				lstrip=__a		,				rstrip=__a				) if isinstance(__a		,				__a				) else eos_token
								snake_case_				:							Optional[int]						 =       AddedToken(__a		,				lstrip=__a		,				rstrip=__a				) if isinstance(__a		,				__a				) else unk_token
								snake_case_				:							List[str]						 =       AddedToken(__a		,				lstrip=__a		,				rstrip=__a				) if isinstance(__a		,				__a				) else pad_token
								snake_case_				:							Tuple						 =       AddedToken(__a		,				lstrip=__a		,				rstrip=__a				) if isinstance(__a		,				__a				) else cls_token
								snake_case_				:							Tuple						 =       AddedToken(__a		,				lstrip=__a		,				rstrip=__a				) if isinstance(__a		,				__a				) else sep_token

								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_				:							Tuple						 =       AddedToken(__a		,				lstrip=__a		,				rstrip=__a				) if isinstance(__a		,				__a				) else mask_token

								snake_case_				:							str						 =       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__a		,				eos_token=__a		,				unk_token=__a		,				pad_token=__a		,				sep_token=__a		,				mask_token=__a		,				cls_token=__a		,				sp_model_kwargs=self.sp_model_kwargs		,				**__a		,				)

								snake_case_				:							Optional[int]						 =       vocab_file

								snake_case_				:							Dict						 =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(__a				)






						@property
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								return self.sp_model.get_piece_size()






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Union[str, Any]						 =       {self.convert_ids_to_tokens(__a				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab






						def __getstate__(	self				) ->				Tuple:
								snake_case_				:							str						 =       self.__dict__.copy()
								snake_case_				:							Optional[Any]						 =       None
								return state






						def __setstate__(	self		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								snake_case_				:							int						 =       d

								# for backward compatibility
								if not hasattr(self		,				"sp_model_kwargs"				):
										snake_case_				:							int						 =       {}

								snake_case_				:							Optional[int]						 =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(self.vocab_file				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.sp_model.encode(__a		,				out_type=__a				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.sp_model.piece_to_id(__a				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								snake_case_				:							Optional[int]						 =       self.sp_model.IdToPiece(__a				)
								return token






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								snake_case_				:							str						 =       []
								snake_case_				:							Dict						 =       ''
								snake_case_				:							Union[str, Any]						 =       False
								for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
												if not prev_is_special:
														out_string += " "
												out_string += self.sp_model.decode(__a				) + token
												snake_case_				:							int						 =       True
												snake_case_				:							str						 =       []
										else:
												current_sub_tokens.append(__a				)
												snake_case_				:							Any						 =       False
								out_string += self.sp_model.decode(__a				)
								return out_string.strip()






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				**_SCREAMING_SNAKE_CASE		,				) ->				str:
								snake_case_				:							Optional[Any]						 =       kwargs.pop("use_source_tokenizer"		,				__a				)

								snake_case_				:							List[Any]						 =       self.convert_ids_to_tokens(__a		,				skip_special_tokens=__a				)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								snake_case_				:							Tuple						 =       []
								snake_case_				:							str						 =       []
								for token in filtered_tokens:
										if skip_special_tokens and token in self.all_special_ids:
												continue
										if token in self.added_tokens_encoder:
												if current_sub_text:
														sub_texts.append(self.convert_tokens_to_string(__a				)				)
														snake_case_				:							Optional[int]						 =       []
												sub_texts.append(__a				)
										else:
												current_sub_text.append(__a				)
								if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(__a				)				)

								# Mimic the behavior of the Rust tokenizer:
								# No space before [MASK] and [SEP]
								if spaces_between_special_tokens:
										snake_case_				:							List[str]						 =       re.sub(r" (\[(MASK|SEP)\])"		,				r"\1"		,				" ".join(__a				)				)
								else:
										snake_case_				:							Optional[Any]						 =       ''.join(__a				)

								snake_case_				:							Optional[int]						 =       (
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
										snake_case_				:							Union[str, Any]						 =       self.clean_up_tokenization(__a				)
										return clean_text
								else:
										return text






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				Tuple[str]:






								if not os.path.isdir(__a				):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
										return
								snake_case_				:							Any						 =       os.path.join(
								    __a		,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

								if os.path.abspath(self.vocab_file				) != os.path.abspath(__a				) and os.path.isfile(self.vocab_file				):
										copyfile(self.vocab_file		,				__a				)
								elif not os.path.isfile(self.vocab_file				):
										with open(__a		,				"wb"				) as fi:
												snake_case_				:							int						 =       self.sp_model.serialized_model_proto()
												fi.write(__a				)

								return (out_vocab_file,)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:






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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False				) ->				List[int]:






								if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__a		,				token_ids_a=__a		,				already_has_special_tokens=__a				)

								if token_ids_a is None:
										return [1] + ([0] * len(__a				)) + [1]
								return [1] + ([0] * len(__a				)) + [1] + ([0] * len(__a				)) + [1]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:
								snake_case_				:							Dict						 =       [self.sep_token_id]
								snake_case_				:							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				) * [0] + len(token_ids_a + sep				) * [1]








from typing import TYPE_CHECKING

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


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
