

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class    _a						(pl.LightningModule    ):


					'''simple docstring'''






					def __init__(			self   ,	A__      ):
						super().__init__()
						A__							:   str					       =		model
						A__							:   List[str]					       =		2
						A__							:   Dict					       =		nn.Linear(self.model.config.hidden_size   ,	self.num_labels      )






					def   __A      (			self      ):
						pass



def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			str )					->				List[Any]:
	# load longformer model from model identifier
	A__							:   Dict					       =		LongformerModel.from_pretrained(lowercase_ )
	A__							:   str					       =		LightningModel(lowercase_ )

	A__							:   Tuple					       =		torch.load(lowercase_   ,				map_location=torch.device("""cpu""" ) )
	lightning_model.load_state_dict(ckpt["""state_dict"""] )

	# init longformer question answering model
	A__							:   int					       =		LongformerForQuestionAnswering.from_pretrained(lowercase_ )

	# transfer weights
	longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
	longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
	longformer_for_qa.eval()

	# save model
	longformer_for_qa.save_pretrained(lowercase_ )

	print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )


if __name__ == "__main__":
	A_	:			Dict  	=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--longformer_model',
	    default=None,
	    type=str,
	    required=True,
	    help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
	)
	parser.add_argument(
	    '--longformer_question_answering_ckpt_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path the official PyTorch Lightning Checkpoint.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_	:			str  	=      parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)






import requests

A_	:			List[Any]  	=      'YOUR API KEY'

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str = giphy_api_key )					->				list:
	A__							:   Dict					       =		"""+""".join(query.split() )
	A__							:   Optional[int]					       =		f"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
	A__							:   Any					       =		requests.get(lowercase_ ).json()["""data"""]
	return [gif["url"] for gif in gifs]


if __name__ == "__main__":
	print('\n'.join(get_gifs('space ship')))



