



"""simple docstring"""






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


__snake_case							:     Dict   =					logging.get_logger(__name__)

__snake_case							:     List[Any]   =					{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'ctrl'
	SCREAMING_SNAKE_CASE					   =			['past_key_values']
	SCREAMING_SNAKE_CASE					   =			{
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}

	def __init__(  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	str=24_6534 ,    _SCREAMING_SNAKE_CASE:	List[Any]=256 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=1280 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=8192 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=48 ,    _SCREAMING_SNAKE_CASE:	List[str]=16 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=0.1 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.1 ,    _SCREAMING_SNAKE_CASE:	str=1e-6 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=0.02 ,    _SCREAMING_SNAKE_CASE:	List[str]=True ,    **_SCREAMING_SNAKE_CASE:	int ,    )       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= vocab_size
				__lowerCAmelCase   :						Dict				= n_positions
				__lowerCAmelCase   :						Optional[Any]				= n_embd
				__lowerCAmelCase   :						Optional[Any]				= n_layer
				__lowerCAmelCase   :						str				= n_head
				__lowerCAmelCase   :						Dict				= dff
				__lowerCAmelCase   :						str				= resid_pdrop
				__lowerCAmelCase   :						Union[str, Any]				= embd_pdrop
				__lowerCAmelCase   :						Optional[Any]				= layer_norm_epsilon
				__lowerCAmelCase   :						Any				= initializer_range

				__lowerCAmelCase   :						List[Any]				= use_cache

				super().__init__(**_SCREAMING_SNAKE_CASE)




"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

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

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


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					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.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)