

'''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__    :       Dict              =logging.get_logger(__name__)
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


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

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

      _lowerCAmelCase												=							{
          """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:
                  _lowerCAmelCase												=							sd.pop(lowerCAmelCase  )

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

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

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

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

      return sd
@torch.no_grad()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None  ):





      """simple docstring"""


      _lowerCAmelCase												=							load_checkpoint(lowerCAmelCase  )

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

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

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


if __name__ == "__main__":
      A__    :       Union[str, Any]              =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'''






# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A__    :       List[str]              =TypeVar('''T''')




class 			UpperCAmelCase					(      Generic[T]       ):




      def __init__(      self      :			Tuple      ,      __snake_case      :			bool = True  )    ->				None:
            _lowerCAmelCase												=							{}  # dictionary of lists
            _lowerCAmelCase												=							directed




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			T      ,      __snake_case      :			T  )    ->				GraphAdjacencyList[T]:

            if not self.directed:  # For undirected graphs
                  # if both source vertex and destination vertex are both present in the
                  # adjacency list, add destination vertex to source vertex list of adjacent
                  # vertices and add source vertex to destination vertex list of adjacent
                  # vertices.
                  if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        self.adj_list[destination_vertex].append(__snake_case  )
                  # if only source vertex is present in adjacency list, add destination vertex
                  # to source vertex list of adjacent vertices, then create a new vertex with
                  # destination vertex as key and assign a list containing the source vertex
                  # as it's first adjacent vertex.
                  elif source_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[source_vertex]
                  # if only destination vertex is present in adjacency list, add source vertex
                  # to destination vertex list of adjacent vertices, then create a new vertex
                  # with source vertex as key and assign a list containing the source vertex
                  # as it's first adjacent vertex.
                  elif destination_vertex in self.adj_list:
                        self.adj_list[destination_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[destination_vertex]
                  # if both source vertex and destination vertex are not present in adjacency
                  # list, create a new vertex with source vertex as key and assign a list
                  # containing the destination vertex as it's first adjacent vertex also
                  # create a new vertex with destination vertex as key and assign a list
                  # containing the source vertex as it's first adjacent vertex.
                  else:
                        _lowerCAmelCase												=							[destination_vertex]
                        _lowerCAmelCase												=							[source_vertex]
            else:  # For directed graphs
                  # if both source vertex and destination vertex are present in adjacency
                  # list, add destination vertex to source vertex list of adjacent vertices.
                  if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                  # if only source vertex is present in adjacency list, add destination
                  # vertex to source vertex list of adjacent vertices and create a new vertex
                  # with destination vertex as key, which has no adjacent vertex
                  elif source_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[]
                  # if only destination vertex is present in adjacency list, create a new
                  # vertex with source vertex as key and assign a list containing destination
                  # vertex as first adjacent vertex
                  elif destination_vertex in self.adj_list:
                        _lowerCAmelCase												=							[destination_vertex]
                  # if both source vertex and destination vertex are not present in adjacency
                  # list, create a new vertex with source vertex as key and a list containing
                  # destination vertex as it's first adjacent vertex. Then create a new vertex
                  # with destination vertex as key, which has no adjacent vertex
                  else:
                        _lowerCAmelCase												=							[destination_vertex]
                        _lowerCAmelCase												=							[]

            return self




      def __repr__(      self      :			int  )    ->				str:
            return pformat(self.adj_list  )






