
"""simple docstring"""







import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Any		    =   torch.load(_UpperCamelCase ,						map_location='cpu'	)
	if "model" in sd.keys():
		__lowerCAmelCase    :		Union[str, Any]		    =   torch.load(_UpperCamelCase ,						map_location='cpu'	)["model"]

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

	__lowerCAmelCase    :		Dict		    =   {
	    "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    :		Union[str, Any]		    =   sd.pop(_UpperCamelCase	)

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

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

			__lowerCAmelCase    :		Dict		    =   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    :		int		    =   torch.split(_UpperCamelCase ,						depth // 3 ,						dim=0	)

			__lowerCAmelCase    :		Dict		    =   q
			__lowerCAmelCase    :		Dict		    =   k
			__lowerCAmelCase    :		Optional[Any]		    =   v
			del sd[key]

	return sd
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=None	):
	__lowerCAmelCase    :		Optional[Any]		    =   load_checkpoint(_UpperCamelCase	)

	if config is not None:
		__lowerCAmelCase    :		Optional[int]		    =   OPTConfig.from_pretrained(_UpperCamelCase	)
	else:
		__lowerCAmelCase    :		List[Any]		    =   OPTConfig()

	__lowerCAmelCase    :		Optional[Any]		    =   OPTModel(_UpperCamelCase	).half().eval()
	model.load_state_dict(_UpperCamelCase	)

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


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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase	):
	if n_term == "":
		return []
	__lowerCAmelCase    :		list		    =   []
	for temp in range(int(_UpperCamelCase	)	):
		series.append(F"1/{temp + 1}" if series else '1'	)
	return series


if __name__ == "__main__":
							lowerCamelCase__           =	input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))