






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a       :	List[str]             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  torch.load(_lowerCamelCase ,     map_location='''cpu''')
					if "model" in sd.keys():
										a__          =  torch.load(_lowerCamelCase ,     map_location='''cpu''')['model']

					# pop unnecessary weights
					a__          =  [
					    'decoder.version',
					    'decoder.output_projection.weight',
					]
					for key in keys_to_delete:
										if key in sd:
															sd.pop(_lowerCamelCase)

					a__          =  {
					    'decoder.project_in_dim.weight': 'decoder.project_in.weight',
					    'decoder.project_out_dim.weight': 'decoder.project_out.weight',
					    'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
					    'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
					}
					for old_key, new_key in keys_to_rename.items():
										if old_key in sd:
															a__          =  sd.pop(_lowerCamelCase)

					a__          =  list(sd.keys())
					for key in keys:
										if ".qkv_proj." in key:
															a__          =  sd[key]
															# We split QKV in separate Q,K,V

															a__          =  key.replace('''.qkv_proj.''' ,     '''.q_proj.''')
															a__          =  key.replace('''.qkv_proj.''' ,     '''.k_proj.''')
															a__          =  key.replace('''.qkv_proj.''' ,     '''.v_proj.''')

															a__          =  value.shape[0]
															assert depth % 3 == 0
															# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
															# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
															a__          =  torch.split(_lowerCamelCase ,     depth // 3 ,     dim=0)

															a__          =  q
															a__          =  k
															a__          =  v
															del sd[key]

					return sd





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=None):
					a__          =  load_checkpoint(_lowerCamelCase)

					if config is not None:
										a__          =  OPTConfig.from_pretrained(_lowerCamelCase)
					else:
										a__          =  OPTConfig()

					a__          =  OPTModel(_lowerCamelCase).half().eval()
					model.load_state_dict(_lowerCamelCase)

					# Check results
					Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
					model.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
	__a       :	Optional[Any]             = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--fairseq_path',
	    type=str,
	    help=(
	        'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
	        ' https://huggingface.co/models?other=opt_metasq'
	    ),
	)
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
	__a       :	Dict             = parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










"""simple docstring"""







def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					assert column_title.isupper()
					a__          =  0
					a__          =  len(lowerCamelCase_) - 1
					a__          =  0

					while index >= 0:
										a__          =  (ord(column_title[index]) - 64) * pow(26 ,     lowerCamelCase_)
										answer += value
										power += 1
										index -= 1

					return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()


