






import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
UpperCAmelCase__							  =						logging.get_logger(__name__)
def 		_a       ( a					:str					)				->     Optional[int]:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 if "model" in sd.keys():
  a						    = torch.load(a      ,				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(a					)

 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(a					)

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

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

 return sd
@torch.no_grad()
def 		_a       ( a					:Dict      ,				a					:List[Any]      ,				a					:Tuple=None					)				->     Any:
 a						    = load_checkpoint(a					)

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

 a						    = OPTModel(a					).half().eval()
 model.load_state_dict(a					)

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


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










from typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


