





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     (   _SCREAMING_SNAKE_CASE			)				->     str:


						'''simple docstring'''


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

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

						SCREAMING_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:
																		SCREAMING_SNAKE_CASE	=		sd.pop(_lowerCAmelCase			)

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

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

																		SCREAMING_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
																		SCREAMING_SNAKE_CASE	=		torch.split(_lowerCAmelCase				,		depth // 3				,		dim=0			)

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

						return sd



@torch.no_grad()
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=None			)				->     Optional[int]:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE	=		load_checkpoint(_lowerCAmelCase			)

						if config is not None:
												SCREAMING_SNAKE_CASE	=		OPTConfig.from_pretrained(_lowerCAmelCase			)
						else:
												SCREAMING_SNAKE_CASE	=		OPTConfig()

						SCREAMING_SNAKE_CASE	=		OPTModel(_lowerCAmelCase			).half().eval()
						model.load_state_dict(_lowerCAmelCase			)

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


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)









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(a - b			) for a, b in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     None:


      '''simple docstring'''


      if point:
            if isinstance(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			):
                  for item in point:
                        if not isinstance(_SCREAMING_SNAKE_CASE				,		(int, float)			):
                              SCREAMING_SNAKE_CASE	=		(
                                  """Expected a list of numbers as input, found """
                                  F"""{type(_SCREAMING_SNAKE_CASE			).__name__}"""
                              )
                              raise TypeError(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		F"""Expected a list of numbers as input, found {type(_SCREAMING_SNAKE_CASE			).__name__}"""
                  raise TypeError(_SCREAMING_SNAKE_CASE			)
      else:
            raise ValueError("""Missing an input"""			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(x - y			) for x, y in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


