


'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class   __lowerCAmelCase						:





      '''simple docstring'''






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






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






       """simple docstring"""







       lowercase__      			= HfArgumentParser((ModelArguments,)       )
       ((lowercase__)			,)      			= parser.parse_args_into_dataclasses()

       # Load pretrained model and tokenizer

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

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

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

       lowercase__      			= 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
       lowercase__      			= decoder_config.decoder_start_token_id
       lowercase__      			= decoder_config.pad_token_id
       if decoder_start_token_id is None:
              lowercase__      			= decoder_config.bos_token_id
       if pad_token_id is None:
              lowercase__      			= decoder_config.eos_token_id

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

       lowercase__      			= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

       lowercase__      			= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
       lowercase__      			= 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:   int  						=	logging.get_logger(__name__)

lowerCamelCase							:   List[Any]  						=	{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							"""realm"""



      def __init__(self    :    str   ,						UpperCamelCase    :    List[Any]=30522   ,						UpperCamelCase    :    List[Any]=768   ,						UpperCamelCase    :    int=128   ,						UpperCamelCase    :    Any=12   ,						UpperCamelCase    :    Tuple=12   ,						UpperCamelCase    :    List[Any]=8   ,						UpperCamelCase    :    Union[str, Any]=3072   ,						UpperCamelCase    :    List[str]="gelu_new"   ,						UpperCamelCase    :    Any=0.1   ,						UpperCamelCase    :    List[str]=0.1   ,						UpperCamelCase    :    Dict=512   ,						UpperCamelCase    :    Dict=2   ,						UpperCamelCase    :    List[Any]=0.02   ,						UpperCamelCase    :    List[Any]=1E-12   ,						UpperCamelCase    :    Dict=256   ,						UpperCamelCase    :    Union[str, Any]=10   ,						UpperCamelCase    :    Optional[int]=1E-3   ,						UpperCamelCase    :    Tuple=5   ,						UpperCamelCase    :    Optional[int]=320   ,						UpperCamelCase    :    List[str]=13353718   ,						UpperCamelCase    :    Optional[Any]=5000   ,						UpperCamelCase    :    str=1   ,						UpperCamelCase    :    Union[str, Any]=0   ,						UpperCamelCase    :    List[Any]=2   ,						**UpperCamelCase    :    int   ,						):

             '''simple docstring'''







             super().__init__(pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						**UpperCamelCase )

             # Common config
             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= hidden_size
             lowercase__      			= retriever_proj_size
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= num_candidates
             lowercase__      			= intermediate_size
             lowercase__      			= hidden_act
             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= initializer_range
             lowercase__      			= type_vocab_size
             lowercase__      			= layer_norm_eps

             # Reader config
             lowercase__      			= span_hidden_size
             lowercase__      			= max_span_width
             lowercase__      			= reader_layer_norm_eps
             lowercase__      			= reader_beam_size
             lowercase__      			= reader_seq_len

             # Retrieval config
             lowercase__      			= num_block_records
             lowercase__      			= searcher_beam_size




