
import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase			:     Dict           =   logging.get_logger(__name__)

def a__    (       A_					):






  '''simple docstring'''





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

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

  __magic_name__				     =       {
      """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__				     =       sd.pop(A_					)

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

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

      __magic_name__				     =       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__				     =       torch.split(A_,     depth // 3,     dim=0					)

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

  return sd

@torch.no_grad()
def a__    (       A_,     A_,     A_=None					):






  '''simple docstring'''





  __magic_name__				     =       load_checkpoint(A_					)

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

  __magic_name__				     =       OPTModel(A_					).half().eval()
  model.load_state_dict(A_					)

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


if __name__ == "__main__":
    __lowerCAmelCase			:     List[str]           =   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			:     Optional[int]           =   parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu




@require_cpu
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       torch.nn.Linear(10			,							10    )
    __magic_name__				     =       torch.optim.SGD(model.parameters()			,							0.1    )
    __magic_name__				     =       Accelerator()
    __magic_name__				     =       accelerator.prepare(UpperCamelCase__    )
    try:
      pickle.loads(pickle.dumps(UpperCamelCase__    )    )
    except Exception as e:
      self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
    AcceleratorState._reset_state()

