




'''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     snake_case__     (			lowerCamelCase__					: int      ,		lowerCamelCase__					: List[Any]      ,		lowerCamelCase__					: Optional[int]      ,		lowerCamelCase__					: int      ) -> Union[str, Any]:
					# Initialise PyTorch model
					A_			:  Tuple			=				ElectraConfig.from_json_file(lowerCamelCase__      )
					print(f'Building PyTorch model from configuration: {config}'      )

					if discriminator_or_generator == "discriminator":
										A_			:  int			=				ElectraForPreTraining(lowerCamelCase__      )
					elif discriminator_or_generator == "generator":
										A_			:  Dict			=				ElectraForMaskedLM(lowerCamelCase__      )
					else:
										raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''      )

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

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


if __name__ == "__main__":
							snake_case__ 							=    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'."""
							    ),
							)
							snake_case__ 							=    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
							)









'''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     )



