






def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  if not isinstance(lowercase    ,  lowercase					):
    raise TypeError("only integers accepted as input"					)
  else:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     str(abs(lowercase					)					)
    SCREAMING_SNAKE_CASE							:   str						    =     [list(lowercase					) for char in range(len(lowercase					)					)]
    for index in range(len(lowercase					)					):
      num_transpositions[index].pop(lowercase					)
    return max(
        int("".join(list(lowercase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
   __import__("""doctest""").testmod()








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   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				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(__lowerCAmelCase							)

			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(__lowerCAmelCase							)

			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(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

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

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

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

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

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


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)



