


'''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


__UpperCAmelCase    :Dict				       =     logging.get_logger(__name__)



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Any							,				snake_case  :	Optional[Any]							,				snake_case  :	int    )		->							Dict:
          __UpperCAmelCase			:				str													=    question_encoder
          __UpperCAmelCase			:				Optional[int]													=    generator
          __UpperCAmelCase			:				Optional[int]													=    self.question_encoder

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	Any    )		->							str:
          if os.path.isfile(UpperCAmelCase_    ):
               raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'    )
          os.makedirs(UpperCAmelCase_							,				exist_ok=UpperCAmelCase_    )
          __UpperCAmelCase			:				int													=    os.path.join(UpperCAmelCase_							,				'''question_encoder_tokenizer'''    )
          __UpperCAmelCase			:				Any													=    os.path.join(UpperCAmelCase_							,				'''generator_tokenizer'''    )
          self.question_encoder.save_pretrained(UpperCAmelCase_    )
          self.generator.save_pretrained(UpperCAmelCase_    )

     @classmethod
     def 				lowerCamelCase__				(       cls  :	str							,				snake_case  :	Any							,				**snake_case  :	List[Any]    )		->							Optional[Any]:
          # dynamically import AutoTokenizer
          from ..auto.tokenization_auto import AutoTokenizer

          __UpperCAmelCase			:				Tuple													=    kwargs.pop('''config'''							,				UpperCAmelCase_    )

          if config is None:
               __UpperCAmelCase			:				int													=    RagConfig.from_pretrained(UpperCAmelCase_    )

          __UpperCAmelCase			:				int													=    AutoTokenizer.from_pretrained(
              UpperCAmelCase_							,				config=config.question_encoder							,				subfolder='''question_encoder_tokenizer'''    )
          __UpperCAmelCase			:				Dict													=    AutoTokenizer.from_pretrained(
              UpperCAmelCase_							,				config=config.generator							,				subfolder='''generator_tokenizer'''    )
          return cls(question_encoder=UpperCAmelCase_							,				generator=UpperCAmelCase_    )

     def __call__(       self  :	str							,				*snake_case  :	Optional[Any]							,				**snake_case  :	Any    )		->							int:
          return self.current_tokenizer(*UpperCAmelCase_							,				**UpperCAmelCase_    )

     def 				lowerCamelCase__				(       self  :	str							,				*snake_case  :	List[Any]							,				**snake_case  :	str    )		->							List[str]:
          return self.generator.batch_decode(*UpperCAmelCase_							,				**UpperCAmelCase_    )

     def 				lowerCamelCase__				(       self  :	Tuple							,				*snake_case  :	Optional[Any]							,				**snake_case  :	Dict    )		->							Tuple:
          return self.generator.decode(*UpperCAmelCase_							,				**UpperCAmelCase_    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							Tuple:
          __UpperCAmelCase			:				List[Any]													=    self.question_encoder

     def 				lowerCamelCase__				(       self  :	str    )		->							Any:
          __UpperCAmelCase			:				str													=    self.generator






     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	List[str]							,				snake_case  :	Optional[List[str]] = None							,				snake_case  :	Optional[int] = None							,				snake_case  :	Optional[int] = None							,				snake_case  :	str = "longest"							,				snake_case  :	str = None							,				snake_case  :	bool = True							,				**snake_case  :	Tuple							,				)		->							Optional[Any]:
          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'''							,				UpperCAmelCase_							,				)
          if max_length is None:
               __UpperCAmelCase			:				int													=    self.current_tokenizer.model_max_length
          __UpperCAmelCase			:				List[Any]													=    self(
              UpperCAmelCase_							,				add_special_tokens=UpperCAmelCase_							,				return_tensors=UpperCAmelCase_							,				max_length=UpperCAmelCase_							,				padding=UpperCAmelCase_							,				truncation=UpperCAmelCase_							,				**UpperCAmelCase_							,				)
          if tgt_texts is None:
               return model_inputs
          # Process tgt_texts
          if max_target_length is None:
               __UpperCAmelCase			:				List[Any]													=    self.current_tokenizer.model_max_length
          __UpperCAmelCase			:				Tuple													=    self(
              text_target=UpperCAmelCase_							,				add_special_tokens=UpperCAmelCase_							,				return_tensors=UpperCAmelCase_							,				padding=UpperCAmelCase_							,				max_length=UpperCAmelCase_							,				truncation=UpperCAmelCase_							,				**UpperCAmelCase_							,				)
          __UpperCAmelCase			:				List[str]													=    labels['input_ids']
          return model_inputs



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase    :Optional[Any]				       =     {
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :Any				       =     ["LayoutLMv2TokenizerFast"]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :int				       =     ["LayoutLMv2FeatureExtractor"]
      __UpperCAmelCase    :Optional[int]				       =     ["LayoutLMv2ImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :List[Any]				       =     [
          "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMv2ForQuestionAnswering",
          "LayoutLMv2ForSequenceClassification",
          "LayoutLMv2ForTokenClassification",
          "LayoutLMv2Layer",
          "LayoutLMv2Model",
          "LayoutLMv2PreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

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