






"""simple docstring"""





import argparse
from pathlib import Path

import torch

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


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



def   a    (     __UpperCAmelCase      :    Dict  )    ->							Dict:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )
						if "model" in sd.keys():
												__magic_name__:	str							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )["""model"""]

						# pop unnecessary weights
						__magic_name__:	Any							=       [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						]
						for key in keys_to_delete:
												if key in sd:
																		sd.pop(__UpperCAmelCase  )

						__magic_name__:	List[Any]							=       {
						    """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:
																		__magic_name__:	Tuple							=       sd.pop(__UpperCAmelCase  )

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

																		__magic_name__:	Union[str, Any]							=       key.replace(""".qkv_proj."""      ,   """.q_proj."""  )
																		__magic_name__:	int							=       key.replace(""".qkv_proj."""      ,   """.k_proj."""  )
																		__magic_name__:	List[str]							=       key.replace(""".qkv_proj."""      ,   """.v_proj."""  )

																		__magic_name__:	str							=       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
																		__magic_name__,			__magic_name__,			__magic_name__:	int							=       torch.split(__UpperCAmelCase      ,   depth // 3      ,   dim=0  )

																		__magic_name__:	int							=       q
																		__magic_name__:	Optional[Any]							=       k
																		__magic_name__:	str							=       v
																		del sd[key]

						return sd



@torch.no_grad()
def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int=None  )    ->							Any:
						__magic_name__:	str							=       load_checkpoint(__UpperCAmelCase  )

						if config is not None:
												__magic_name__:	Tuple							=       OPTConfig.from_pretrained(__UpperCAmelCase  )
						else:
												__magic_name__:	List[Any]							=       OPTConfig()

						__magic_name__:	Tuple							=       OPTModel(__UpperCAmelCase  ).half().eval()
						model.load_state_dict(__UpperCAmelCase  )

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


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











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int  )    ->							int:
						while b:
												__magic_name__,			__magic_name__:	Optional[int]							=       b, a % b
						return a



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int  )    ->							int:
						return a if b == 0 else euclidean_gcd_recursive(__UpperCAmelCase      ,   a % b  )



def   a    (     )    ->							List[Any]:
						print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3      ,   5  )}'  )
						print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5      ,   3  )}'  )
						print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1      ,   3  )}'  )
						print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3      ,   6  )}'  )
						print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6      ,   3  )}'  )

						print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3      ,   5  )}'  )
						print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5      ,   3  )}'  )
						print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1      ,   3  )}'  )
						print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3      ,   6  )}'  )
						print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6      ,   3  )}'  )


if __name__ == "__main__":
							main()



