
"""simple docstring"""




import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


A__ :       str				= logging.get_logger(__name__)


class       __magic_name__							:
       def __init__(  self			,					A_			,					A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      List[str]   			=  question_encoder
         _lowercase:      Tuple   			=  generator
         _lowercase:      Optional[Any]   			=  self.question_encoder
       def 				lowercase_				(  self			,					A_							)  ->						Any:





         """simple docstring"""




         if os.path.isfile(A_							):
           raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''							)
         os.makedirs(A_			,					exist_ok=A_							)
         _lowercase:      Tuple   			=  os.path.join(A_			,					'''question_encoder_tokenizer'''							)
         _lowercase:      Optional[Any]   			=  os.path.join(A_			,					'''generator_tokenizer'''							)
         self.question_encoder.save_pretrained(A_							)
         self.generator.save_pretrained(A_							)
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						Optional[int]:





         """simple docstring"""




         from ..auto.tokenization_auto import AutoTokenizer

         _lowercase:      List[Any]   			=  kwargs.pop('''config'''			,					A_							)

         if config is None:
           _lowercase:      Optional[Any]   			=  RagConfig.from_pretrained(A_							)

         _lowercase:      str   			=  AutoTokenizer.from_pretrained(
             A_			,					config=config.question_encoder			,					subfolder='''question_encoder_tokenizer'''							)
         _lowercase:      int   			=  AutoTokenizer.from_pretrained(
             A_			,					config=config.generator			,					subfolder='''generator_tokenizer'''							)
         return cls(question_encoder=A_			,					generator=A_							)
       def __call__(  self			,					*A_			,					**A_							)  ->						List[Any]:





         """simple docstring"""




         return self.current_tokenizer(*A_			,					**A_							)
       def 				lowercase_				(  self			,					*A_			,					**A_							)  ->						List[str]:





         """simple docstring"""




         return self.generator.batch_decode(*A_			,					**A_							)
       def 				lowercase_				(  self			,					*A_			,					**A_							)  ->						Tuple:





         """simple docstring"""




         return self.generator.decode(*A_			,					**A_							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.question_encoder
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.generator



       def 				lowercase_				(  self			,					A_			,					A_ = None			,					A_ = None			,					A_ = None			,					A_ = "longest"			,					A_ = None			,					A_ = True			,					**A_			,					)  ->						BatchEncoding:





         """simple docstring"""




         warnings.warn(
             '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
             '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
             '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
             '''details'''			,					A_			,					)
         if max_length is None:
           _lowercase:      int   			=  self.current_tokenizer.model_max_length
         _lowercase:      Dict   			=  self(
             A_			,					add_special_tokens=A_			,					return_tensors=A_			,					max_length=A_			,					padding=A_			,					truncation=A_			,					**A_			,					)
         if tgt_texts is None:
           return model_inputs
         # Process tgt_texts
         if max_target_length is None:
           _lowercase:      Union[str, Any]   			=  self.current_tokenizer.model_max_length
         _lowercase:      Dict   			=  self(
             text_target=A_			,					add_special_tokens=A_			,					return_tensors=A_			,					padding=A_			,					max_length=A_			,					truncation=A_			,					**A_			,					)
         _lowercase:      Dict   			=  labels['''input_ids''']
         return model_inputs


"""simple docstring"""




from typing import TYPE_CHECKING

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


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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      '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

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
