




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     :		Tuple								=       logging.get_logger(__name__)


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     int ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Any)						->Union[str, Any]:





        '''simple docstring'''

        A__							=   question_encoder
        A__							=   generator
        A__							=   self.question_encoder


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     List[Any])						->Tuple:





        '''simple docstring'''

        if os.path.isfile(__lowerCamelCase):
            raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""")
        os.makedirs(__lowerCamelCase ,			exist_ok=__lowerCamelCase)
        A__							=   os.path.join(__lowerCamelCase ,			'''question_encoder_tokenizer''')
        A__							=   os.path.join(__lowerCamelCase ,			'''generator_tokenizer''')
        self.question_encoder.save_pretrained(__lowerCamelCase)
        self.generator.save_pretrained(__lowerCamelCase)


    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     List[Any] ,			UpperCAmelCase__				:     str ,			**UpperCAmelCase__				:     Tuple)						->Optional[int]:





        '''simple docstring'''

        from ..auto.tokenization_auto import AutoTokenizer

        A__							=   kwargs.pop('''config''' ,			__lowerCamelCase)

        if config is None:
            A__							=   RagConfig.from_pretrained(__lowerCamelCase)

        A__							=   AutoTokenizer.from_pretrained(
            __lowerCamelCase ,			config=config.question_encoder ,			subfolder='''question_encoder_tokenizer''')
        A__							=   AutoTokenizer.from_pretrained(
            __lowerCamelCase ,			config=config.generator ,			subfolder='''generator_tokenizer''')
        return cls(question_encoder=__lowerCamelCase ,			generator=__lowerCamelCase)


    def __call__(      self				:     Any ,			*UpperCAmelCase__				:     Tuple ,			**UpperCAmelCase__				:     str)						->Tuple:





        '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			*UpperCAmelCase__				:     List[Any] ,			**UpperCAmelCase__				:     Tuple)						->int:





        '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			*UpperCAmelCase__				:     Optional[int] ,			**UpperCAmelCase__				:     List[Any])						->Optional[int]:





        '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->str:





        '''simple docstring'''

        A__							=   self.question_encoder


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Dict:





        '''simple docstring'''

        A__							=   self.generator





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     List[Any] = None ,			UpperCAmelCase__				:     Union[str, Any] = None ,			UpperCAmelCase__				:     Optional[Any] = None ,			UpperCAmelCase__				:     Tuple = "longest" ,			UpperCAmelCase__				:     Dict = None ,			UpperCAmelCase__				:     Tuple = True ,			**UpperCAmelCase__				:     Any ,			)						->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''' ,			__lowerCamelCase ,			)
        if max_length is None:
            A__							=   self.current_tokenizer.model_max_length
        A__							=   self(
            __lowerCamelCase ,			add_special_tokens=__lowerCamelCase ,			return_tensors=__lowerCamelCase ,			max_length=__lowerCamelCase ,			padding=__lowerCamelCase ,			truncation=__lowerCamelCase ,			**__lowerCamelCase ,			)
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            A__							=   self.current_tokenizer.model_max_length
        A__							=   self(
            text_target=__lowerCamelCase ,			add_special_tokens=__lowerCamelCase ,			return_tensors=__lowerCamelCase ,			padding=__lowerCamelCase ,			max_length=__lowerCamelCase ,			truncation=__lowerCamelCase ,			**__lowerCamelCase ,			)
        A__							=   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


_lowerCamelCase     :		str								=       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Dict								=       [
      """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FocalNetForImageClassification""",
      """FocalNetForMaskedImageModeling""",
      """FocalNetBackbone""",
      """FocalNetModel""",
      """FocalNetPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_focalnet import (
        FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
        FocalNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Tuple								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


