



"""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


__lowerCAmelCase							:       Dict          =       logging.get_logger(__name__)



class  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      question_encoder
								snake_case_ :			List[str]            =      generator
								snake_case_ :			Optional[Any]            =      self.question_encoder





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    List[Any]:







								'''simple docstring'''

								if os.path.isfile(_lowercase ):
														raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
								os.makedirs(_lowercase				,     exist_ok=_lowercase )
								snake_case_ :			Optional[Any]            =      os.path.join(_lowercase				,     """question_encoder_tokenizer""" )
								snake_case_ :			List[str]            =      os.path.join(_lowercase				,     """generator_tokenizer""" )
								self.question_encoder.save_pretrained(_lowercase )
								self.generator.save_pretrained(_lowercase )





		@classmethod
		def  UpperCAmelCase__				(       cls				,     _lowercase				,     **_lowercase )			->    Optional[Any]:







								'''simple docstring'''

								from ..auto.tokenization_auto import AutoTokenizer

								snake_case_ :			Any            =      kwargs.pop("""config"""				,     _lowercase )

								if config is None:
														snake_case_ :			int            =      RagConfig.from_pretrained(_lowercase )

								snake_case_ :			str            =      AutoTokenizer.from_pretrained(
								    _lowercase				,     config=config.question_encoder				,     subfolder="""question_encoder_tokenizer""" )
								snake_case_ :			Optional[int]            =      AutoTokenizer.from_pretrained(
								    _lowercase				,     config=config.generator				,     subfolder="""generator_tokenizer""" )
								return cls(question_encoder=_lowercase				,     generator=_lowercase )





		def __call__(       self				,     *_lowercase				,     **_lowercase )			->    int:







								'''simple docstring'''

								return self.current_tokenizer(*_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     *_lowercase				,     **_lowercase )			->    List[Any]:







								'''simple docstring'''

								return self.generator.batch_decode(*_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     *_lowercase				,     **_lowercase )			->    Optional[int]:







								'''simple docstring'''

								return self.generator.decode(*_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.question_encoder





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Tuple            =      self.generator





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = "longest"				,     _lowercase = None				,     _lowercase = True				,     **_lowercase				,     )			->    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"""				,     _lowercase				,     )
								if max_length is None:
														snake_case_ :			List[Any]            =      self.current_tokenizer.model_max_length
								snake_case_ :			List[Any]            =      self(
								    _lowercase				,     add_special_tokens=_lowercase				,     return_tensors=_lowercase				,     max_length=_lowercase				,     padding=_lowercase				,     truncation=_lowercase				,     **_lowercase				,     )
								if tgt_texts is None:
														return model_inputs
								# Process tgt_texts
								if max_target_length is None:
														snake_case_ :			str            =      self.current_tokenizer.model_max_length
								snake_case_ :			Any            =      self(
								    text_target=_lowercase				,     add_special_tokens=_lowercase				,     return_tensors=_lowercase				,     padding=_lowercase				,     max_length=_lowercase				,     truncation=_lowercase				,     **_lowercase				,     )
								snake_case_ :			str            =      labels["""input_ids"""]
								return model_inputs







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InformerForPrediction""",
          """InformerModel""",
          """InformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

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

