


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


SCREAMING_SNAKE_CASE			: Tuple				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: str				   =					{
    "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(		_a  ):
       lowercase_					:      Tuple       					=     """realm"""
       def __init__(   self, lowerCamelCase=3_05_22, lowerCamelCase=7_68, lowerCamelCase=1_28, lowerCamelCase=12, lowerCamelCase=12, lowerCamelCase=8, lowerCamelCase=30_72, lowerCamelCase="gelu_new", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=5_12, lowerCamelCase=2, lowerCamelCase=0.0_2, lowerCamelCase=1E-12, lowerCamelCase=2_56, lowerCamelCase=10, lowerCamelCase=1E-3, lowerCamelCase=5, lowerCamelCase=3_20, lowerCamelCase=13_35_37_18, lowerCamelCase=50_00, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=2, **lowerCamelCase, )			->  Tuple:




           """simple docstring"""

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

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

           # Reader config
           _lowercase			:				Optional[int] 						=						span_hidden_size
           _lowercase			:				Optional[Any] 						=						max_span_width
           _lowercase			:				Any 						=						reader_layer_norm_eps
           _lowercase			:				Tuple 						=						reader_beam_size
           _lowercase			:				Tuple 						=						reader_seq_len

           # Retrieval config
           _lowercase			:				Tuple 						=						num_block_records
           _lowercase			:				Tuple 						=						searcher_beam_size






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


