import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Dict				)	->							str:
      '''simple docstring'''


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

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

      lowercase_     =      {
          "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:
                  lowercase_     =      sd.pop(lowercase__				)

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

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

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

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

      return sd


@torch.no_grad()
def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[int]=None				)	->							int:
      '''simple docstring'''


      lowercase_     =      load_checkpoint(lowercase__				)

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

      lowercase_     =      OPTModel(lowercase__				).half().eval()
      model.load_state_dict(lowercase__				)

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


if __name__ == "__main__":
       lowerCAmelCase_ : int						  =      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[Any]						  =      parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







'''simple docstring'''

def __a (	__lowerCamelCase			:						list[int] ,		__lowerCamelCase			:						int				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      lowercase_     =      [[False] * (required_sum + 1) for _ in range(arr_len + 1				)]

      # for each arr value, a sum of zero(0) can be formed by not taking any element
      # hence True/1
      for i in range(arr_len + 1				):
            lowercase_     =      True

      # sum is not zero and set is empty then false
      for i in range(1 ,		required_sum + 1				):
            lowercase_     =      False

      for i in range(1 ,		arr_len + 1				):
            for j in range(1 ,		required_sum + 1				):
                  if arr[i - 1] > j:
                        lowercase_     =      subset[i - 1][j]
                  if arr[i - 1] <= j:
                        lowercase_     =      subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

      return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()






