



'''simple docstring'''







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

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


if is_sentencepiece_available():
				from .tokenization_pegasus import PegasusTokenizer
else:
				_UpperCamelCase					:  Optional[int]        =None


_UpperCamelCase					:  List[str]        =logging.get_logger(__name__)


_UpperCamelCase					:  Optional[Any]        ="▁"

_UpperCamelCase					:  Optional[Any]        ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_UpperCamelCase					:  str        ={
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

_UpperCamelCase					:  int        ={
    "google/pegasus-xsum": 5_12,
}





class 						_SCREAMING_SNAKE_CASE      ( UpperCamelCase						):






			"""simple docstring"""







			SCREAMING_SNAKE_CASE_   			=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE_   			=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE_   			=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE_   			=				PegasusTokenizer
			SCREAMING_SNAKE_CASE_   			=				['input_ids', 'attention_mask']


			def __init__(   self       , _snake_case=None       , _snake_case=None       , _snake_case="<pad>"       , _snake_case="</s>"       , _snake_case="<unk>"       , _snake_case="<mask_2>"       , _snake_case="<mask_1>"       , _snake_case=None       , _snake_case=1_03       , **_snake_case       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		offset

				if additional_special_tokens is not None:
					if not isinstance(__UpperCAmelCase       , __UpperCAmelCase    ):
						raise TypeError(
						    F'''additional_special_tokens should be of type {type(__UpperCAmelCase    )}, but is'''
						    F''' {type(__UpperCAmelCase    )}'''    )

					__lowerCamelCase		       =		(
					    ([mask_token_sent] + additional_special_tokens)
					    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
					    else additional_special_tokens
					)
					# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
					additional_special_tokens_extended += [
					    F'''<unk_{i}>''' for i in range(len(__UpperCAmelCase    )       , self.offset - 1    )
					]

					if len(set(__UpperCAmelCase    )    ) != len(__UpperCAmelCase    ):
						raise ValueError(
						    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
						    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''    )
					__lowerCamelCase		       =		additional_special_tokens_extended
				else:
					__lowerCamelCase		       =		[mask_token_sent] if mask_token_sent is not None else []
					additional_special_tokens += [F'''<unk_{i}>''' for i in range(2       , self.offset    )]

				super().__init__(
				    __UpperCAmelCase       , tokenizer_file=__UpperCAmelCase       , pad_token=__UpperCAmelCase       , eos_token=__UpperCAmelCase       , unk_token=__UpperCAmelCase       , mask_token=__UpperCAmelCase       , mask_token_sent=__UpperCAmelCase       , offset=__UpperCAmelCase       , additional_special_tokens=__UpperCAmelCase       , **__UpperCAmelCase       , )
				__lowerCamelCase		       =		vocab_file
				__lowerCamelCase		       =		False if not self.vocab_file else True


			def 						_lowerCamelCase       (   self       , _snake_case    ):







				"""simple docstring"""



				__lowerCamelCase		       =		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

				if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
					raise ValueError(
					    '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
					    F''' {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}'''    )

				return [1 if x in all_special_ids else 0 for x in seq]


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case = None       , _snake_case = False    ):







				"""simple docstring"""



				if already_has_special_tokens:
					return self._special_token_mask(__UpperCAmelCase    )
				elif token_ids_a is None:
					return self._special_token_mask(__UpperCAmelCase    ) + [1]
				else:
					return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case=None    ):







				"""simple docstring"""



				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 						_lowerCamelCase       (   self       , _snake_case       , _snake_case = None    ):







				"""simple docstring"""



				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
					    '''tokenizer.'''    )

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

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

				return (out_vocab_file,)









from typing import TYPE_CHECKING

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


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "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

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


