


"""simple docstring"""







import argparse
from pathlib import Path

import torch

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


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


def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







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

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

  a						:		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:
      a						:		int											=      sd.pop(A_	)

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

      a						:		Dict											=      key.replace(".qkv_proj."  ,       ".q_proj."	)
      a						:		Tuple											=      key.replace(".qkv_proj."  ,       ".k_proj."	)
      a						:		int											=      key.replace(".qkv_proj."  ,       ".v_proj."	)

      a						:		Dict											=      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						:		int											=      torch.split(A_  ,       depth // 3  ,       dim=0	)

      a						:		str											=      q
      a						:		str											=      k
      a						:		List[str]											=      v
      del sd[key]

  return sd


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_=None	)->				int:
  '''simple docstring'''







  a						:		Dict											=      load_checkpoint(A_	)

  if config is not None:
    a						:		int											=      OPTConfig.from_pretrained(A_	)
  else:
    a						:		Optional[int]											=      OPTConfig()

  a						:		Tuple											=      OPTModel(A_	).half().eval()
  model.load_state_dict(A_	)

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


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



"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







  assert masked_input.count("<mask>"	) == 1
  a						:		List[str]											=      torch.tensor(tokenizer.encode(A_  ,       add_special_tokens=A_	)	).unsqueeze(0	)  # Batch size 1
  a						:		Dict											=      model(A_	)[0]  # The last hidden-state is the first element of the output tuple
  a						:		int											=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a						:		Optional[Any]											=      logits[0, masked_index, :]
  a						:		Dict											=      logits.softmax(dim=0	)
  a							,		a						:		Any											=      prob.topk(k=A_  ,       dim=0	)
  a						:		Optional[Any]											=      " ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A_	)	)]	)
  a						:		str											=      tokenizer.mask_token
  a						:		Any											=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "	)	):
    a						:		Dict											=      predicted_token_bpe.replace("\u2581"  ,       " "	)
    if " {0}".format(A_	) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(A_	)  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(A_  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
  return topk_filled_outputs


__lowercase    =				CamembertTokenizer.from_pretrained("""camembert-base""")
__lowercase    =				CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__lowercase    =				"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))






