




"""simple docstring"""

import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						json.load(open(_UpperCAmelCase					, '''r'''		)		)
	A_					:			Optional[int]						=						GPTNeoConfig(
	    hidden_size=config_json['''n_embd''']					, num_layers=config_json['''n_layer''']					, num_heads=config_json['''n_head''']					, attention_types=config_json['''attention_types''']					, max_position_embeddings=config_json['''n_positions''']					, resid_dropout=config_json['''res_dropout''']					, embed_dropout=config_json['''embed_dropout''']					, attention_dropout=config_json['''attn_dropout''']					, )
	print(f"""Building PyTorch model from configuration: {config}"""		)
	A_					:			Any						=						GPTNeoForCausalLM(_UpperCAmelCase		)

	# Load weights from tf checkpoint
	load_tf_weights_in_gpt_neo(_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
	model.save_pretrained(_UpperCAmelCase		)


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 mesh-tf 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					:     int				       =    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)











"""simple docstring"""

from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





