code: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase							:     List[str]								=			logging.get_logger(__name__)
  
  lowerCamelCase							:     Any								=			{
      '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		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  Optional[int]						      =							"""cpmant"""
  
  
  
        def __init__(self					:					Tuple     ,		UpperCamelCase					:					int = 30720     ,		UpperCamelCase					:					int = 4096     ,		UpperCamelCase					:					int = 32     ,		UpperCamelCase					:					int = 128     ,		UpperCamelCase					:					int = 10240     ,		UpperCamelCase					:					int = 48     ,		UpperCamelCase					:					int = 0.0     ,		UpperCamelCase					:					int = 512     ,		UpperCamelCase					:					int = 2048     ,		UpperCamelCase					:					int = 1E-6     ,		UpperCamelCase					:					float = 1.0     ,		UpperCamelCase					:					int = 32     ,		UpperCamelCase					:					int = 32     ,		UpperCamelCase					:					int = 32     ,		UpperCamelCase					:					bool = True     ,		**UpperCamelCase					:					int     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(**UpperCamelCase				)
               lowercase__       =				prompt_types
               lowercase__       =				prompt_length
               lowercase__       =				segment_types
               lowercase__       =				hidden_size
               lowercase__       =				num_attention_heads
               lowercase__       =				dim_head
               lowercase__       =				dim_ff
               lowercase__       =				num_hidden_layers
               lowercase__       =				position_bias_num_buckets
               lowercase__       =				position_bias_max_distance
               lowercase__       =				dropout_p
               lowercase__       =				eps
               lowercase__       =				use_cache
               lowercase__       =				vocab_size
               lowercase__       =				init_std
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  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 _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A ) -> Dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				ElectraConfig.from_json_file(A )
         print(f"Building PyTorch model from configuration: {config}" )
  
         if discriminator_or_generator == "discriminator":
                lowercase__       =				ElectraForPreTraining(A )
         elif discriminator_or_generator == "generator":
                lowercase__       =				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__":
   lowerCamelCase							:     Tuple								=			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\'.'
       ),
   )
   lowerCamelCase							:     Optional[int]								=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
   )
  
  
  
  
  