






"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class      _lowercase :
      _a   :     str								=   field(
          metadata={'''help''': '''The output directory where the model will be written.'''}		,  )
      _a   :     str								=   field(
          metadata={
              '''help''': (
                  '''The encoder model checkpoint for weights initialization.'''
                  '''Don\'t set if you want to train an encoder model from scratch.'''
              )
          }		,  )
      _a   :     str								=   field(
          metadata={
              '''help''': (
                  '''The decoder model checkpoint for weights initialization.'''
                  '''Don\'t set if you want to train a decoder model from scratch.'''
              )
          }		,  )
      _a   :     Optional[str]								=   field(
          default=lowerCAmelCase		,  metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}						)
      _a   :     Optional[str]								=   field(
          default=lowerCAmelCase		,  metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}						)



def     __lowercase					(    )					->					Tuple:
      __snake_case      :    Dict  			=HfArgumentParser((ModelArguments,) )
      ((__snake_case)			,				)      :    List[str]  			=parser.parse_args_into_dataclasses()

      # Load pretrained model and tokenizer

      # Use explicit specified encoder config
      if model_args.encoder_config_name:
            __snake_case      :    Optional[Any]  			=AutoConfig.from_pretrained(model_args.encoder_config_name )
      # Use pretrained encoder model's config
      else:
            __snake_case      :    int  			=AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )

      # Use explicit specified decoder config
      if model_args.decoder_config_name:
            __snake_case      :    Dict  			=AutoConfig.from_pretrained(model_args.decoder_config_name )
      # Use pretrained decoder model's config
      else:
            __snake_case      :    int  			=AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )

      # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
      __snake_case      :    Union[str, Any]  			=True
      __snake_case      :    int  			=True

      __snake_case      :    Tuple  			=FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
          encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path       ,		decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path       ,		encoder_config=a       ,		decoder_config=a       ,		)

      # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
      __snake_case      :    Any  			=decoder_config.decoder_start_token_id
      __snake_case      :    Tuple  			=decoder_config.pad_token_id
      if decoder_start_token_id is None:
            __snake_case      :    Union[str, Any]  			=decoder_config.bos_token_id
      if pad_token_id is None:
            __snake_case      :    str  			=decoder_config.eos_token_id

      # This is necessary to make Flax's generate() work
      __snake_case      :    str  			=decoder_config.eos_token_id
      __snake_case      :    Any  			=decoder_start_token_id
      __snake_case      :    Dict  			=pad_token_id

      __snake_case      :    List[str]  			=AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

      __snake_case      :    Any  			=AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
      __snake_case      :    int  			=tokenizer.convert_ids_to_tokens(model.config.pad_token_id )

      model.save_pretrained(model_args.output_dir )
      image_processor.save_pretrained(model_args.output_dir )
      tokenizer.save_pretrained(model_args.output_dir )


if __name__ == "__main__":
    main()










"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_	:				Any										= {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= [
        """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ReformerAttention""",
        """ReformerForMaskedLM""",
        """ReformerForQuestionAnswering""",
        """ReformerForSequenceClassification""",
        """ReformerLayer""",
        """ReformerModel""",
        """ReformerModelWithLMHead""",
        """ReformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer import ReformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer_fast import ReformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_reformer import (
            REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            ReformerAttention,
            ReformerForMaskedLM,
            ReformerForQuestionAnswering,
            ReformerForSequenceClassification,
            ReformerLayer,
            ReformerModel,
            ReformerModelWithLMHead,
            ReformerPreTrainedModel,
        )

else:
    import sys

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


