






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        _a  (     UpperCamelCase_					:				int   ) ->   Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						torch.load(UpperCamelCase_    ,   map_location="cpu"   )
						if "model" in sd.keys():
												lowerCAmelCase__       =						torch.load(UpperCamelCase_    ,   map_location="cpu"   )["model"]

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

						lowerCAmelCase__       =						{
						    "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:
																		lowerCAmelCase__       =						sd.pop(UpperCamelCase_   )

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

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

																		lowerCAmelCase__       =						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
																		lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(UpperCamelCase_    ,   depth // 3    ,   dim=0   )

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

						return sd


@torch.no_grad()
def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				List[Any]=None   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						load_checkpoint(UpperCamelCase_   )

						if config is not None:
												lowerCAmelCase__       =						OPTConfig.from_pretrained(UpperCamelCase_   )
						else:
												lowerCAmelCase__       =						OPTConfig()

						lowerCAmelCase__       =						OPTModel(UpperCamelCase_   ).half().eval()
						model.load_state_dict(UpperCamelCase_   )

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


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)














from __future__ import annotations

from collections.abc import Generator


def        _a  (     ) ->   Generator[int, None, None]:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						2
						while True:
												lowerCAmelCase__       =						factor_map.pop(UpperCamelCase_    ,   UpperCamelCase_   )
												if factor:
																		lowerCAmelCase__       =						factor + prime
																		while x in factor_map:
																								x += factor
																		lowerCAmelCase__       =						factor
												else:
																		lowerCAmelCase__       =						prime
																		yield prime
												prime += 1


def        _a  (     UpperCamelCase_					:				float = 1e1_0   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						sieve()

						lowerCAmelCase__       =						1
						while True:
												lowerCAmelCase__       =						next(UpperCamelCase_   )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(UpperCamelCase_   )
												n += 2


if __name__ == "__main__":
	print(solution())






