





import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		torch.load(__a				,	map_location='cpu'				)
	if "model" in sd.keys():
		snake_case_						:     List[Any]     =		torch.load(__a				,	map_location='cpu'				)['model']

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

	snake_case_						:     List[str]     =		{
	    '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:
			snake_case_						:     Optional[int]     =		sd.pop(__a				)

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

			snake_case_						:     Dict     =		key.replace('.qkv_proj.'				,	'.q_proj.'				)
			snake_case_						:     Dict     =		key.replace('.qkv_proj.'				,	'.k_proj.'				)
			snake_case_						:     Union[str, Any]     =		key.replace('.qkv_proj.'				,	'.v_proj.'				)

			snake_case_						:     Any     =		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
			snake_case_						:     Optional[Any]     =		torch.split(__a				,	depth // 3				,	dim=0				)

			snake_case_						:     Union[str, Any]     =		q
			snake_case_						:     List[str]     =		k
			snake_case_						:     Optional[int]     =		v
			del sd[key]

	return sd


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a=None				):
	snake_case_						:     Any     =		load_checkpoint(__a				)

	if config is not None:
		snake_case_						:     Any     =		OPTConfig.from_pretrained(__a				)
	else:
		snake_case_						:     Optional[int]     =		OPTConfig()

	snake_case_						:     List[Any]     =		OPTModel(__a				).half().eval()
	model.load_state_dict(__a				)

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


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE 		=						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.""")
					_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








def 							SCREAMING_SNAKE_CASE__  (      __a = 60_08_51_47_51_43				):

	try:
		snake_case_						:     Optional[Any]     =		int(__a				)
	except (TypeError, ValueError):
		raise TypeError('Parameter n must be int or castable to int.'				)
	if n <= 0:
		raise ValueError('Parameter n must be greater than or equal to one.'				)
	snake_case_						:     Any     =		2
	snake_case_						:     Any     =		0
	if n == 2:
		return 2
	while n > 2:
		while n % i != 0:
			i += 1
		snake_case_						:     Tuple     =		i
		while n % i == 0:
			snake_case_						:     List[str]     =		n // i
		i += 1
	return int(__a				)


if __name__ == "__main__":
						print(F'''{solution() = }''')

