
'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





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







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: 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(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












'''simple docstring'''




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


snake_case__ 							=    logging.get_logger(__name__)

snake_case__ 							=    {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class      UpperCamelCase_  (a__  ):







						"""simple docstring"""


						_lowerCAmelCase         =       'nystromformer'







						def __init__(  self   :							Optional[Any]				,			_lowerCamelCase   :							str=30000				,			_lowerCamelCase   :							Optional[Any]=768				,			_lowerCamelCase   :							Optional[Any]=12				,			_lowerCamelCase   :							str=12				,			_lowerCamelCase   :							List[str]=3072				,			_lowerCamelCase   :							Any="gelu_new"				,			_lowerCamelCase   :							List[Any]=0.1				,			_lowerCamelCase   :							List[str]=0.1				,			_lowerCamelCase   :							List[str]=510				,			_lowerCamelCase   :							Optional[Any]=2				,			_lowerCamelCase   :							List[Any]=64				,			_lowerCamelCase   :							str=64				,			_lowerCamelCase   :							str=65				,			_lowerCamelCase   :							List[Any]=False				,			_lowerCamelCase   :							List[Any]=0.02				,			_lowerCamelCase   :							List[Any]=1E-5				,			_lowerCamelCase   :							Dict=1				,			_lowerCamelCase   :							Tuple=0				,			_lowerCamelCase   :							List[str]=2				,			**_lowerCamelCase   :							List[str]				,			):





											"""simple docstring"""







											A_			:  List[Any]			=				vocab_size
											A_			:  Tuple			=				max_position_embeddings
											A_			:  Dict			=				hidden_size
											A_			:  Optional[Any]			=				num_hidden_layers
											A_			:  Dict			=				num_attention_heads
											A_			:  Tuple			=				intermediate_size
											A_			:  Dict			=				hidden_act
											A_			:  List[Any]			=				hidden_dropout_prob
											A_			:  str			=				attention_probs_dropout_prob
											A_			:  Optional[Any]			=				initializer_range
											A_			:  List[str]			=				type_vocab_size
											A_			:  List[Any]			=				segment_means_seq_len
											A_			:  Dict			=				num_landmarks
											A_			:  int			=				conv_kernel_size
											A_			:  Optional[Any]			=				inv_coeff_init_option
											A_			:  str			=				layer_norm_eps
											super().__init__(pad_token_id=_lowerCamelCase				,			bos_token_id=_lowerCamelCase				,			eos_token_id=_lowerCamelCase				,			**_lowerCamelCase     )



