'''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       (			UpperCAmelCase__				:  List[str]		)			->	Optional[int]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Tuple						    = torch.load(UpperCAmelCase__				,						map_location='cpu'		)
							if "model" in sd.keys():
														SCREAMING_SNAKE_CASE__     :int						    = torch.load(UpperCAmelCase__				,						map_location='cpu'		)['model']

							# pop unnecessary weights
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [
							    'decoder.version',
							    'decoder.output_projection.weight',
							]
							for key in keys_to_delete:
														if key in sd:
																					sd.pop(UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = {
							    '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__     :Dict						    = sd.pop(UpperCAmelCase__		)

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

																					SCREAMING_SNAKE_CASE__     :int						    = key.replace('.qkv_proj.'				,						'.q_proj.'		)
																					SCREAMING_SNAKE_CASE__     :str						    = key.replace('.qkv_proj.'				,						'.k_proj.'		)
																					SCREAMING_SNAKE_CASE__     :Tuple						    = key.replace('.qkv_proj.'				,						'.v_proj.'		)

																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = torch.split(UpperCAmelCase__				,						depth // 3				,						dim=0		)

																					SCREAMING_SNAKE_CASE__     :List[Any]						    = q
																					SCREAMING_SNAKE_CASE__     :str						    = k
																					SCREAMING_SNAKE_CASE__     :str						    = v
																					del sd[key]

							return sd


@torch.no_grad()
def    lowerCamelCase       (			UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  Optional[Any]=None		)			->	List[str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[int]						    = load_checkpoint(UpperCAmelCase__		)

							if config is not None:
														SCREAMING_SNAKE_CASE__     :str						    = OPTConfig.from_pretrained(UpperCAmelCase__		)
							else:
														SCREAMING_SNAKE_CASE__     :Tuple						    = OPTConfig()

							SCREAMING_SNAKE_CASE__     :int						    = OPTModel(UpperCAmelCase__		).half().eval()
							model.load_state_dict(UpperCAmelCase__		)

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


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'''







def    lowerCamelCase       (			UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							assert (
							    isinstance(UpperCAmelCase__				,						UpperCAmelCase__		) and number_of_steps > 0
							), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
							if number_of_steps == 1:
														return 1
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = 1, 1
							for _ in range(number_of_steps - 1		):
														SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = current + previous, current
							return current


if __name__ == "__main__":
		import doctest

		doctest.testmod()






