



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def     a(      A       :    int       )		->       Any:
					"""simple docstring"""





					a					     =   [
					    'encoder.version',
					    'decoder.version',
					    'model.encoder.version',
					    'model.decoder.version',
					    '_float_tensor',
					    'decoder.output_projection.weight',
					]
					for k in ignore_keys:
										state_dict.pop(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE       )






def     a(      A       :    Optional[Any]       )		->       Tuple:
					"""simple docstring"""





					a					     =   emb.weight.shape
					a					     =   nn.Linear(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     bias=_SCREAMING_SNAKE_CASE       )
					a					     =   emb.weight.data
					return lin_layer






def     a(      A       :    List[Any] ,     A       :    Union[str, Any]="facebook/mbart-large-en-ro" ,     A       :    int=False ,     A       :    Tuple=False       )		->       Tuple:
					"""simple docstring"""





					a					     =   torch.load(_SCREAMING_SNAKE_CASE ,     map_location="cpu"       )['model']
					remove_ignore_keys_(_SCREAMING_SNAKE_CASE       )
					a					     =   state_dict['encoder.embed_tokens.weight'].shape[0]

					a					     =   MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE ,     vocab_size=_SCREAMING_SNAKE_CASE       )
					if mbart_aa and finetuned:
										a					     =   'relu'

					a					     =   state_dict['decoder.embed_tokens.weight']
					a					     =   MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE       )
					model.model.load_state_dict(_SCREAMING_SNAKE_CASE       )

					if finetuned:
										a					     =   make_linear_from_emb(model.model.shared       )

					return model


if __name__ == "__main__":
				_lowercase: Optional[int]      			=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
				)
				parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument(
				    "--hf_config",
				    default="facebook/mbart-large-cc25",
				    type=str,
				    help="Which huggingface architecture to use: mbart-large",
				)
				parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
				parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
				_lowercase: Any      			=		parser.parse_args()
				_lowercase: int      			=		convert_fairseq_mbart_checkpoint_from_disk(
				    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
				)
				model.save_pretrained(args.pytorch_dump_folder_path)




'''simple docstring'''



import requests

__lowercase	:						Tuple       =	''  # <-- Put your OpenWeatherMap appid here!
__lowercase	:						Tuple       =	'https://api.openweathermap.org/data/2.5/'






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str = "Chicago"						,      _SCREAMING_SNAKE_CASE      :   str = APPID   ):
					return requests.get(URL_BASE + 'weather'						,      params=locals()   ).json()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str = "Kolkata, India"						,      _SCREAMING_SNAKE_CASE      :   str = APPID   ):
					return requests.get(URL_BASE + 'forecast'						,      params=locals()   ).json()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   float = 5_5.6_8						,      _SCREAMING_SNAKE_CASE      :   float = 1_2.5_7						,      _SCREAMING_SNAKE_CASE      :   str = APPID   ):
					return requests.get(URL_BASE + 'onecall'						,      params=locals()   ).json()


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

	while True:
		__lowercase	:						Dict       =	input('Enter a location:').strip()
		if location:
			pprint(current_weather(location))
		else:
			break



