



import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_									=							logging.get_logger(__name__)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int				)->str:
      _lowerCAmelCase 			=  torch.load(lowercase_     ,				map_location='''cpu'''				)
      if "model" in sd.keys():
            _lowerCAmelCase 			=  torch.load(lowercase_     ,				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(lowercase_				)

      _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(lowercase_				)

      _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 			=  torch.split(lowercase_     ,				depth // 3     ,				dim=0				)

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

      return sd
@torch.no_grad()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	int=None				)->int:
      _lowerCAmelCase 			=  load_checkpoint(lowercase_				)

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

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

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


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




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


UpperCAmelCase_									=							"Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def 							UpperCAmelCase__		(				)->Any:
      _lowerCAmelCase 			=  _ask_options(
          '''In which compute environment are you running?'''     ,				['''This machine''', '''AWS (Amazon SageMaker)''']     ,				_convert_compute_environment     ,				)
      if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
            _lowerCAmelCase 			=  get_sagemaker_input()
      else:
            _lowerCAmelCase 			=  get_cluster_input()
      return config
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int=None				)->str:
      if subparsers is not None:
            _lowerCAmelCase 			=  subparsers.add_parser('''config'''     ,				description=_SCREAMING_SNAKE_CASE				)
      else:
            _lowerCAmelCase 			=  argparse.ArgumentParser('''Accelerate config command'''     ,				description=_SCREAMING_SNAKE_CASE				)

      parser.add_argument(
          '''--config_file'''     ,				default=_SCREAMING_SNAKE_CASE     ,				help=(
              '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
              '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
              '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
              '''with \'huggingface\'.'''
          )     ,				)

      if subparsers is not None:
            parser.set_defaults(func=_SCREAMING_SNAKE_CASE				)
      return parser
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict				)->str:
      _lowerCAmelCase 			=  get_user_input()
      if args.config_file is not None:
            _lowerCAmelCase 			=  args.config_file
      else:
            if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
                  os.makedirs(_SCREAMING_SNAKE_CASE				)
            _lowerCAmelCase 			=  default_yaml_config_file

      if config_file.endswith('''.json'''				):
            config.to_json_file(_SCREAMING_SNAKE_CASE				)
      else:
            config.to_yaml_file(_SCREAMING_SNAKE_CASE				)
      print(f'''accelerate configuration saved at {config_file}'''				)
def 							UpperCAmelCase__		(				)->List[Any]:
      _lowerCAmelCase 			=  config_command_parser()
      _lowerCAmelCase 			=  parser.parse_args()
      config_command(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    main()