
"""simple docstring"""





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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class 						SCREAMING_SNAKE_CASE_ (						__A ):




		"""simple docstring"""





		__lowercase							:  str						   =		'mra'




		def __init__(     self							,  lowerCAmelCase__=5_0_2_6_5							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=3_0_7_2							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1E-5							,  lowerCAmelCase__="absolute"							,  lowerCAmelCase__=4							,  lowerCAmelCase__="full"							,  lowerCAmelCase__=0							,  lowerCAmelCase__=0							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  lowerCAmelCase__=2							,  **lowerCAmelCase__							,  ):
				super().__init__(pad_token_id=__UpperCAmelCase							,  bos_token_id=__UpperCAmelCase							,  eos_token_id=__UpperCAmelCase							,  **__UpperCAmelCase)

				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = type_vocab_size
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = position_embedding_type
				__SCREAMING_SNAKE_CASE            = block_per_row
				__SCREAMING_SNAKE_CASE            = approx_mode
				__SCREAMING_SNAKE_CASE            = initial_prior_first_n_blocks
				__SCREAMING_SNAKE_CASE            = initial_prior_diagonal_n_blocks





"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

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


if __name__ == "__main__":
				__lowerCamelCase							 =     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(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT 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."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





