




import argparse
from pathlib import Path

import torch

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


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





def 							lowercase					(snake_case__   : List[Any] )     ->  Union[str, Any]:


				'''simple docstring'''






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

				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(snake_case__ )

				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(snake_case__      ,       depth // 3      ,       dim=0 )

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

				return sd





@torch.no_grad()
def 							lowercase					(snake_case__   : Optional[Any]      ,       snake_case__   : Optional[int]      ,       snake_case__   : int=None )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				load_checkpoint(snake_case__ )

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

				lowerCAmelCase  =				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      =							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      =							parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)











"""simple docstring"""







# 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


a      =							'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 							lowercase					()     ->  Optional[Any]:


				'''simple docstring'''






				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 							lowercase					(snake_case__   : List[str]=None )     ->  int:


				'''simple docstring'''






				if subparsers is not None:
								lowerCAmelCase  =				subparsers.add_parser("""config"""      ,       description=snake_case__ )
				else:
								lowerCAmelCase  =				argparse.ArgumentParser("""Accelerate config command"""      ,       description=snake_case__ )

				parser.add_argument(
				    """--config_file"""      ,       default=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=snake_case__ )
				return parser





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				get_user_input()
				if args.config_file is not None:
								lowerCAmelCase  =				args.config_file
				else:
								if not os.path.isdir(snake_case__ ):
												os.makedirs(snake_case__ )
								lowerCAmelCase  =				default_yaml_config_file

				if config_file.endswith(""".json""" ):
								config.to_json_file(snake_case__ )
				else:
								config.to_yaml_file(snake_case__ )
				print(f'''accelerate configuration saved at {config_file}''' )





def 							lowercase					()     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				config_command_parser()
				lowerCAmelCase  =				parser.parse_args()
				config_command(snake_case__ )


if __name__ == "__main__":
		main()





