




"""simple docstring"""
import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a_				:    Optional[Any]										=     logging.get_logger(__name__)





def 						UpperCAmelCase				(			A__:    Optional[int]     )  ->     Dict:
			__lowerCamelCase					:	Optional[int]	       =				torch.load(_lowerCamelCase							,				map_location='cpu'     )
			if "model" in sd.keys():
						__lowerCamelCase					:	Union[str, Any]	       =				torch.load(_lowerCamelCase							,				map_location='cpu'     )['model']

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

			__lowerCamelCase					:	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:
									__lowerCamelCase					:	Tuple	       =				sd.pop(_lowerCamelCase     )

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

									__lowerCamelCase					:	List[Any]	       =				key.replace('.qkv_proj.'							,				'.q_proj.'     )
									__lowerCamelCase					:	Optional[int]	       =				key.replace('.qkv_proj.'							,				'.k_proj.'     )
									__lowerCamelCase					:	Any	       =				key.replace('.qkv_proj.'							,				'.v_proj.'     )

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

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

			return sd





@torch.no_grad()
def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    List[str]							,				A__:    Any=None     )  ->     List[str]:
			__lowerCamelCase					:	Optional[Any]	       =				load_checkpoint(_lowerCamelCase     )

			if config is not None:
						__lowerCamelCase					:	str	       =				OPTConfig.from_pretrained(_lowerCamelCase     )
			else:
						__lowerCamelCase					:	Tuple	       =				OPTConfig()

			__lowerCamelCase					:	Dict	       =				OPTModel(_lowerCamelCase     ).half().eval()
			model.load_state_dict(_lowerCamelCase     )

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


if __name__ == "__main__":
	a_				:    List[str]										=     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_				:    Union[str, Any]										=     parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









"""simple docstring"""
import requests

a_				:    Optional[Any]										=     ''''''  # <-- Put your OpenWeatherMap appid here!
a_				:    str										=     '''https://api.openweathermap.org/data/2.5/'''





def 						UpperCAmelCase				(			A__:    str = "Chicago"							,				A__:    str = APPID     )  ->     dict:
			return requests.get(URL_BASE + 'weather'							,				params=locals()     ).json()





def 						UpperCAmelCase				(			A__:    str = "Kolkata, India"							,				A__:    str = APPID     )  ->     dict:
			return requests.get(URL_BASE + 'forecast'							,				params=locals()     ).json()





def 						UpperCAmelCase				(			A__:    float = 55.68							,				A__:    float = 12.57							,				A__:    str = APPID     )  ->     dict:
			return requests.get(URL_BASE + 'onecall'							,				params=locals()     ).json()


if __name__ == "__main__":
					from pprint import pprint

					while True:
										a_				:    List[Any]										=     input('''Enter a location:''').strip()
										if location:
															pprint(current_weather(location))
										else:
															break



