


from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class     UpperCAmelCase__       :



    """simple docstring"""






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

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=HfArgumentParser((ModelArguments,)				)
     ((__UpperCamelCase)							,      )    							=parser.parse_args_into_dataclasses()

     # Load pretrained model and tokenizer

     # Use explicit specified encoder config
     if model_args.encoder_config_name:
          __UpperCamelCase    							=AutoConfig.from_pretrained(model_args.encoder_config_name				)
     # Use pretrained encoder model's config
     else:
          __UpperCamelCase    							=AutoConfig.from_pretrained(model_args.encoder_model_name_or_path				)

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

     # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
     __UpperCamelCase    							=True
     __UpperCamelCase    							=True

     __UpperCamelCase    							=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=SCREAMING_SNAKE_CASE__			,     decoder_config=SCREAMING_SNAKE_CASE__			,     )

     # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
     __UpperCamelCase    							=decoder_config.decoder_start_token_id
     __UpperCamelCase    							=decoder_config.pad_token_id
     if decoder_start_token_id is None:
          __UpperCamelCase    							=decoder_config.bos_token_id
     if pad_token_id is None:
          __UpperCamelCase    							=decoder_config.eos_token_id

     # This is necessary to make Flax's generate() work
     __UpperCamelCase    							=decoder_config.eos_token_id
     __UpperCamelCase    							=decoder_start_token_id
     __UpperCamelCase    							=pad_token_id

     __UpperCamelCase    							=AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

     __UpperCamelCase    							=AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
     __UpperCamelCase    							=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()









from typing import TYPE_CHECKING

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


_A		       =						{
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['ConvBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ConvBertForMaskedLM',
       'ConvBertForMultipleChoice',
       'ConvBertForQuestionAnswering',
       'ConvBertForSequenceClassification',
       'ConvBertForTokenClassification',
       'ConvBertLayer',
       'ConvBertModel',
       'ConvBertPreTrainedModel',
       'load_tf_weights_in_convbert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFConvBertForMaskedLM',
       'TFConvBertForMultipleChoice',
       'TFConvBertForQuestionAnswering',
       'TFConvBertForSequenceClassification',
       'TFConvBertForTokenClassification',
       'TFConvBertLayer',
       'TFConvBertModel',
       'TFConvBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





