






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase			  =    logging.get_logger(__name__)

lowercase			  =    {
    "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 		UpperCAmelCase_						(	__lowerCAmelCase					):


						'''simple docstring'''




						A     :   int						=					"realm"






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=3_0522		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=8		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE="gelu_new"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-12		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=1e-3		,				_SCREAMING_SNAKE_CASE=5		,				_SCREAMING_SNAKE_CASE=320		,				_SCREAMING_SNAKE_CASE=1335_3718		,				_SCREAMING_SNAKE_CASE=5000		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=2		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(pad_token_id=lowerCamelCase__		,				bos_token_id=lowerCamelCase__		,				eos_token_id=lowerCamelCase__		,				**lowerCamelCase__				)

								# Common config
								snake_case_				:							int						 =       vocab_size
								snake_case_				:							Optional[int]						 =       max_position_embeddings
								snake_case_				:							Optional[int]						 =       hidden_size
								snake_case_				:							Union[str, Any]						 =       retriever_proj_size
								snake_case_				:							Dict						 =       num_hidden_layers
								snake_case_				:							str						 =       num_attention_heads
								snake_case_				:							int						 =       num_candidates
								snake_case_				:							Any						 =       intermediate_size
								snake_case_				:							Tuple						 =       hidden_act
								snake_case_				:							List[str]						 =       hidden_dropout_prob
								snake_case_				:							str						 =       attention_probs_dropout_prob
								snake_case_				:							Union[str, Any]						 =       initializer_range
								snake_case_				:							List[str]						 =       type_vocab_size
								snake_case_				:							str						 =       layer_norm_eps

								# Reader config
								snake_case_				:							Dict						 =       span_hidden_size
								snake_case_				:							int						 =       max_span_width
								snake_case_				:							str						 =       reader_layer_norm_eps
								snake_case_				:							int						 =       reader_beam_size
								snake_case_				:							Optional[Any]						 =       reader_seq_len

								# Retrieval config
								snake_case_				:							Dict						 =       num_block_records
								snake_case_				:							Optional[Any]						 =       searcher_beam_size








import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str  ,      **_a       :			Optional[int]    ):
		snake_case_				:							Any						 =       AutoConfig.from_pretrained(_a  ,      **_a    )
		snake_case_				:							str						 =       AutoModelForSeqaSeqLM.from_config(_a    )
		model.save_pretrained(_a    )
		AutoTokenizer.from_pretrained(_a    ).save_pretrained(_a    )
		return model


if __name__ == "__main__":
							fire.Fire(save_randomly_initialized_version)
