






import argparse
from pathlib import Path

import torch

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


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

def _UpperCAmelCase (						a    :						Union[str, Any]			):
							snake_case__							=							torch.load(a__	,       map_location="""cpu"""			)
							if "model" in sd.keys():
														snake_case__							=							torch.load(a__	,       map_location="""cpu"""			)["""model"""]

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

							snake_case__							=							{
							    """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__							=							sd.pop(a__			)

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

																					snake_case__							=							key.replace(""".qkv_proj."""	,       """.q_proj."""			)
																					snake_case__							=							key.replace(""".qkv_proj."""	,       """.k_proj."""			)
																					snake_case__							=							key.replace(""".qkv_proj."""	,       """.v_proj."""			)

																					snake_case__							=							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__	,			snake_case__	,			snake_case__							=							torch.split(a__	,       depth // 3	,       dim=0			)

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

							return sd

@torch.no_grad()
def _UpperCAmelCase (						a    :						Optional[int]	,       a    :						Any	,       a    :						str=None			):
							snake_case__							=							load_checkpoint(a__			)

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

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

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


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




def 			lowerCAmelCase__					(     a__:						int     ,    a__:						int			)    ->						int:


							'''simple docstring'''





							return x if y == 0 else greatest_common_divisor(a__     ,    x % y			)

def 			lowerCAmelCase__					(     a__:						int     ,    a__:						int			)    ->						int:


							'''simple docstring'''





							return (x * y) // greatest_common_divisor(a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						int = 2_0			)    ->						int:


							'''simple docstring'''





							_UpperCAmelCase											=     1
							for i in range(1     ,    n + 1			):
														_UpperCAmelCase											=     lcm(a__     ,    a__			)
							return g


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