




'''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							:   Union[str, Any]      		= logging.get_logger(__name__)


def      UpperCamelCase_(	snake_case							:       int     ):

							'''simple docstring'''






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

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

							snake_case_						=      {
							    "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:
																					snake_case_						=      sd.pop(snake_case     )

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

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

																					snake_case_						=      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
																					snake_case_					,      snake_case_					,      snake_case_						=      torch.split(snake_case							,				depth // 3							,				dim=0     )

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

							return sd


@torch.no_grad()
def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       List[str]							,				snake_case							:       List[Any]=None     ):

							'''simple docstring'''






							snake_case_						=      load_checkpoint(snake_case     )

							if config is not None:
														snake_case_						=      OPTConfig.from_pretrained(snake_case     )
							else:
														snake_case_						=      OPTConfig()

							snake_case_						=      OPTModel(snake_case     ).half().eval()
							model.load_state_dict(snake_case     )

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


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






import json
import sys





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 with open(lowerCamelCase,	encoding="""utf-8"""							) as f:
  lowerCamelCase		:	List[Any]							=      json.load(lowerCamelCase							)

 lowerCamelCase		:	Optional[Any]							=      ["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

 for benchmark_name in sorted(lowerCamelCase							):
  lowerCamelCase		:	List[Any]							=      results[benchmark_name]

  lowerCamelCase		:	Union[str, Any]							=      benchmark_name.split("""/"""							)[-1]
  output_md.append(F'''### Benchmark: {benchmark_file_name}'''							)

  lowerCamelCase		:	Any							=      """| metric |"""
  lowerCamelCase		:	str							=      """|--------|"""
  lowerCamelCase		:	List[Any]							=      """| new / old (diff) |"""
  for metric_name in sorted(lowerCamelCase							):
   lowerCamelCase		:	List[Any]							=      benchmark_res[metric_name]
   lowerCamelCase		:	Tuple							=      metric_vals["""new"""]
   lowerCamelCase		:	int							=      metric_vals.get("""old""",	lowerCamelCase							)
   lowerCamelCase		:	Dict							=      metric_vals.get("""diff""",	lowerCamelCase							)

   lowerCamelCase		:	Dict							=      F''' {new_val:f}''' if isinstance(lowerCamelCase,	(int, float)							) else """None"""

   if old_val is not None:
    val_str += F''' / {old_val:f}''' if isinstance(lowerCamelCase,	(int, float)							) else "None"
   if dif_val is not None:
    val_str += F''' ({dif_val:f})''' if isinstance(lowerCamelCase,	(int, float)							) else "None"

   title += " " + metric_name + " |"
   lines += "---|"
   value += val_str + " |"

  output_md += [title, lines, value, " "]

 output_md.append("""</details>"""							)

 with open(lowerCamelCase,	"""w""",	encoding="""utf-8"""							) as f:
  f.writelines("""\n""".join(lowerCamelCase							)							)


if __name__ == "__main__":
      _lowerCamelCase					   =sys.argv[1]
      _lowerCamelCase					   =sys.argv[2]

      format_json_to_md(input_json_file, output_md_file)




