
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()



def 		lowercase_     (  A__					,						A__					,						A__					,						A__						)				->				str:
    """simple docstring"""

    snake_case						=   ElectraConfig.from_json_file(A__						)
    print(F'Building PyTorch model from configuration: {config}'						)

    if discriminator_or_generator == "discriminator":
        snake_case						=   ElectraForPreTraining(A__						)
    elif discriminator_or_generator == "generator":
        snake_case						=   ElectraForMaskedLM(A__						)
    else:
        raise ValueError("The discriminator_or_generator argument should be either 'discriminator' or 'generator'"						)

    # Load weights from tf checkpoint
    load_tf_weights_in_electra(
        A__					,						A__					,						A__					,						discriminator_or_generator=A__						)

    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'						)
    torch.save(model.state_dict()					,						A__						)


if __name__ == "__main__":
   _A       =					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 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."
   )
   parser.add_argument(
       "--discriminator_or_generator",
       default=None,
       type=str,
       required=True,
       help=(
           "Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or "
           "'generator'."
       ),
   )
   _A       =					parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
   )



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


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

