






'''simple docstring'''

import argparse
from pathlib import Path

import torch

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


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







def 					lowercase__				(   __UpperCamelCase					)->				Dict:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      if "model" in sd.keys():
            UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)["""model"""]

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

      UpperCamelCase       						=      {
          """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:
                  UpperCamelCase       						=      sd.pop(__UpperCamelCase					)

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

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

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

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

      return sd







@torch.no_grad()
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None					)->				Any:
      UpperCamelCase       						=      load_checkpoint(__UpperCamelCase					)

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

      UpperCamelCase       						=      OPTModel(__UpperCamelCase					).half().eval()
      model.load_state_dict(__UpperCamelCase					)

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


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








'''simple docstring'''

import fire

from utils import calculate_rouge, save_json







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		**__UpperCamelCase					)->				int:
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()]
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()][: len(__UpperCamelCase					)]
      UpperCamelCase       						=      calculate_rouge(__UpperCamelCase	,		__UpperCamelCase	,		**__UpperCamelCase					)
      if save_path is not None:
            save_json(__UpperCamelCase	,		__UpperCamelCase	,		indent=__UpperCamelCase					)
      return metrics  # these print nicely


if __name__ == "__main__":
     fire.Fire(calculate_rouge_path)
