


'''simple docstring'''





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							:			Dict			,    lowerCAmelCase_							:			Optional[Any]	)      ->		List[str]:
						super().__init__()
						__A=	model
						__A=	2
						__A=	nn.Linear(self.model.config.hidden_size			,    self.num_labels	)

			def 					lowerCAmelCase     (  self							:			str	)      ->		str:
						pass






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			__A=	LongformerModel.from_pretrained(_SCREAMING_SNAKE_CASE )
			__A=	LightningModel(_SCREAMING_SNAKE_CASE )

			__A=	torch.load(_SCREAMING_SNAKE_CASE,map_location=torch.device('cpu' ) )
			lightning_model.load_state_dict(ckpt['state_dict'] )

			# init longformer question answering model
			__A=	LongformerForQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE )

			# 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(_SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
					UpperCAmelCase__  			=				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.'''
					)
					UpperCAmelCase__  			=				parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)







'''simple docstring'''





from __future__ import annotations

import math

UpperCAmelCase__  			=				'''2020.9.26'''
UpperCAmelCase__  			=				'''xcodz-dot, cclaus, dhruvmanila'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float ):






			"""simple docstring"""




			if not all(isinstance(_SCREAMING_SNAKE_CASE,(float, int) ) for val in locals().values() ):
						__A=	f"""Input values must either be float or int: {list(locals().values() )}"""
						raise TypeError(_SCREAMING_SNAKE_CASE )
			__A=	((x * distance) / (z + distance)) * scale
			__A=	((y * distance) / (z + distance)) * scale
			return projected_x, projected_y






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: float ):






			"""simple docstring"""




			if not isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ):
						raise TypeError('Axis must be a str' )
			__A=	locals()
			del input_variables["axis"]
			if not all(isinstance(_SCREAMING_SNAKE_CASE,(float, int) ) for val in input_variables.values() ):
						__A=	(
						    'Input values except axis must either be float or int: '
						    f"""{list(input_variables.values() )}"""
						)
						raise TypeError(_SCREAMING_SNAKE_CASE )
			__A=	(angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						__A=	x * math.cos(_SCREAMING_SNAKE_CASE ) - y * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	y * math.cos(_SCREAMING_SNAKE_CASE ) + x * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z
			elif axis == "x":
						__A=	y * math.cos(_SCREAMING_SNAKE_CASE ) - z * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z * math.cos(_SCREAMING_SNAKE_CASE ) + y * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	x
			elif axis == "y":
						__A=	x * math.cos(_SCREAMING_SNAKE_CASE ) - z * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z * math.cos(_SCREAMING_SNAKE_CASE ) + x * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	y
			else:
						raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\'' )

			return new_x, new_y, new_z


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
					print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")



