





"""simple docstring"""





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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[int]       =torch.load(SCREAMING_SNAKE_CASE     ,    map_location='''cpu''' )
							if "model" in sd.keys():
														__lowerCamelCase   :   Any       =torch.load(SCREAMING_SNAKE_CASE     ,    map_location='''cpu''' )['''model''']

							# pop unnecessary weights
							__lowerCamelCase   :   Optional[int]       =[
							    '''decoder.version''',
							    '''decoder.output_projection.weight''',
							]
							for key in keys_to_delete:
														if key in sd:
																					sd.pop(SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   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:
																					__lowerCamelCase   :   int       =sd.pop(SCREAMING_SNAKE_CASE )

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

																					__lowerCamelCase   :   Dict       =key.replace('''.qkv_proj.'''     ,    '''.q_proj.''' )
																					__lowerCamelCase   :   List[str]       =key.replace('''.qkv_proj.'''     ,    '''.k_proj.''' )
																					__lowerCamelCase   :   Dict       =key.replace('''.qkv_proj.'''     ,    '''.v_proj.''' )

																					__lowerCamelCase   :   int       =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   :   str       =torch.split(SCREAMING_SNAKE_CASE     ,    depth // 3     ,    dim=0 )

																					__lowerCamelCase   :   int       =q
																					__lowerCamelCase   :   List[Any]       =k
																					__lowerCamelCase   :   Dict       =v
																					del sd[key]

							return sd



@torch.no_grad()
def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				List[str]=None ):


							'''simple docstring'''







							__lowerCamelCase   :   Union[str, Any]       =load_checkpoint(SCREAMING_SNAKE_CASE )

							if config is not None:
														__lowerCamelCase   :   Optional[int]       =OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   List[str]       =OPTConfig()

							__lowerCamelCase   :   Optional[Any]       =OPTModel(SCREAMING_SNAKE_CASE ).half().eval()
							model.load_state_dict(SCREAMING_SNAKE_CASE )

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


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)











"""simple docstring"""





import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				np.array ):


							'''simple docstring'''







							return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
							import doctest

							doctest.testmod()




