

'''simple docstring'''

import argparse
from pathlib import Path

import torch

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


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





def       lowercase					(     __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							int   					=   torch.load(__snake_case ,    map_location="cpu"							)
					if "model" in sd.keys():
										UpperCAmelCase		:							Dict   					=   torch.load(__snake_case ,    map_location="cpu"							)["model"]

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

					UpperCAmelCase		:							Optional[int]   					=   {
					    "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		:							str   					=   sd.pop(__snake_case							)

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

															UpperCAmelCase		:							int   					=   key.replace(".qkv_proj." ,    ".q_proj."							)
															UpperCAmelCase		:							str   					=   key.replace(".qkv_proj." ,    ".k_proj."							)
															UpperCAmelCase		:							Union[str, Any]   					=   key.replace(".qkv_proj." ,    ".v_proj."							)

															UpperCAmelCase		:							str   					=   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		:							Any   					=   torch.split(__snake_case ,    depth // 3 ,    dim=0							)

															UpperCAmelCase		:							List[str]   					=   q
															UpperCAmelCase		:							int   					=   k
															UpperCAmelCase		:							Dict   					=   v
															del sd[key]

					return sd





@torch.no_grad()
def       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__=None							):



					'''simple docstring'''







					UpperCAmelCase		:							Optional[Any]   					=   load_checkpoint(__snake_case							)

					if config is not None:
										UpperCAmelCase		:							Union[str, Any]   					=   OPTConfig.from_pretrained(__snake_case							)
					else:
										UpperCAmelCase		:							int   					=   OPTConfig()

					UpperCAmelCase		:							List[Any]   					=   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__":
				a   :			List[str]         = 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   :			Tuple         = parser.parse_args()
				convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       ArgumentParser('Diffusers CLI tool'		,					usage='diffusers-cli <command> [<args>]'    )
  lowercase							    =       parser.add_subparsers(help='diffusers-cli command helpers'    )

  # Register commands
  EnvironmentCommand.register_subcommand(__snake_case    )

  # Let's go
  lowercase							    =       parser.parse_args()

  if not hasattr(__snake_case		,					'func'    ):
    parser.print_help()
    exit(1    )

  # Run
  lowercase							    =       args.func(__snake_case    )
  service.run()


if __name__ == "__main__":
    main()


