



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 		a__			(		_UpperCamelCase			: str							):
			__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							,__lowerCamelCase							,__lowerCamelCase													=    torch.split(snake_case__ ,depth // 3 ,dim=0							)

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

			return sd





@torch.no_grad()
def 		a__			(		_UpperCamelCase			: List[Any] ,_UpperCamelCase			: Tuple ,_UpperCamelCase			: List[str]=None							):
			__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)











from collections import namedtuple

import requests
from lxml import html  # type: ignore

a_			=					namedtuple("""covid_data""", """cases deaths recovered""")





def 		a__			(		_UpperCamelCase			: str = "https://www.worldometers.info/coronavirus/"							):
   __lowerCamelCase													=    '''//div[@class = "maincounter-number"]/span/text()'''
   return covid_data(*html.fromstring(requests.get(_UpperCamelCase							).content							).xpath(_UpperCamelCase							)							)


a_			=					"""Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""





print(fmt.format(*covid_stats()))






