


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class 		__a				(    UpperCAmelCase      ):
		_a     :  List[Any]           =     (DPMSolverSDEScheduler,)
		_a     :  Optional[Any]           =     10
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      {
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'noise_sampler_seed': 0,
									}

									config.update(**_SCREAMING_SNAKE_CASE						)
									return config
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]      ,   [0.0002, 0.002, 0.02]						):
																self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      ,   beta_end=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      self.scheduler_classes[0]
									_UpperCAmelCase					=      self.get_scheduler_config()
									_UpperCAmelCase					=      scheduler_class(**_SCREAMING_SNAKE_CASE						)

									scheduler.set_timesteps(self.num_inference_steps						)

									_UpperCAmelCase					=      self.dummy_model()
									_UpperCAmelCase					=      self.dummy_sample_deter * scheduler.init_noise_sigma
									_UpperCAmelCase					=      sample.to(_SCREAMING_SNAKE_CASE						)

									for i, t in enumerate(scheduler.timesteps						):
																_UpperCAmelCase					=      scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      scheduler.step(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      output.prev_sample

									_UpperCAmelCase					=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

									if torch_device in ["mps"]:
																assert abs(result_sum.item() - 167.47821044921875						) < 1e-2
																assert abs(result_mean.item() - 0.2178705964565277						) < 1e-3
									elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 171.59352111816406						) < 1e-2
																assert abs(result_mean.item() - 0.22342906892299652						) < 1e-3
									else:
																assert abs(result_sum.item() - 162.52383422851562						) < 1e-2
																assert abs(result_mean.item() - 0.211619570851326						) < 1e-3
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      self.scheduler_classes[0]
									_UpperCAmelCase					=      self.get_scheduler_config(prediction_type='v_prediction'						)
									_UpperCAmelCase					=      scheduler_class(**_SCREAMING_SNAKE_CASE						)

									scheduler.set_timesteps(self.num_inference_steps						)

									_UpperCAmelCase					=      self.dummy_model()
									_UpperCAmelCase					=      self.dummy_sample_deter * scheduler.init_noise_sigma
									_UpperCAmelCase					=      sample.to(_SCREAMING_SNAKE_CASE						)

									for i, t in enumerate(scheduler.timesteps						):
																_UpperCAmelCase					=      scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      scheduler.step(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      output.prev_sample

									_UpperCAmelCase					=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

									if torch_device in ["mps"]:
																assert abs(result_sum.item() - 124.77149200439453						) < 1e-2
																assert abs(result_mean.item() - 0.16226289014816284						) < 1e-3
									elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 128.1663360595703						) < 1e-2
																assert abs(result_mean.item() - 0.16688326001167297						) < 1e-3
									else:
																assert abs(result_sum.item() - 119.8487548828125						) < 1e-2
																assert abs(result_mean.item() - 0.1560530662536621						) < 1e-3
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.scheduler_classes[0]
									_UpperCAmelCase					=      self.get_scheduler_config()
									_UpperCAmelCase					=      scheduler_class(**_SCREAMING_SNAKE_CASE						)

									scheduler.set_timesteps(self.num_inference_steps      ,   device=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.dummy_model()
									_UpperCAmelCase					=      self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE						) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
																_UpperCAmelCase					=      scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      scheduler.step(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      output.prev_sample

									_UpperCAmelCase					=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

									if torch_device in ["mps"]:
																assert abs(result_sum.item() - 167.46957397460938						) < 1e-2
																assert abs(result_mean.item() - 0.21805934607982635						) < 1e-3
									elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 171.59353637695312						) < 1e-2
																assert abs(result_mean.item() - 0.22342908382415771						) < 1e-3
									else:
																assert abs(result_sum.item() - 162.52383422851562						) < 1e-2
																assert abs(result_mean.item() - 0.211619570851326						) < 1e-3






		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.scheduler_classes[0]
									_UpperCAmelCase					=      self.get_scheduler_config()
									_UpperCAmelCase					=      scheduler_class(**_SCREAMING_SNAKE_CASE      ,   use_karras_sigmas=_SCREAMING_SNAKE_CASE						)

									scheduler.set_timesteps(self.num_inference_steps      ,   device=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.dummy_model()
									_UpperCAmelCase					=      self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE						) * scheduler.init_noise_sigma
									_UpperCAmelCase					=      sample.to(_SCREAMING_SNAKE_CASE						)

									for t in scheduler.timesteps:
																_UpperCAmelCase					=      scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      scheduler.step(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      output.prev_sample

									_UpperCAmelCase					=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

									if torch_device in ["mps"]:
																assert abs(result_sum.item() - 176.66974135742188						) < 1e-2
																assert abs(result_mean.item() - 0.23003872730981811						) < 1e-2
									elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 177.63653564453125						) < 1e-2
																assert abs(result_mean.item() - 0.23003872730981811						) < 1e-2
									else:
																assert abs(result_sum.item() - 170.3135223388672						) < 1e-2
																assert abs(result_mean.item() - 0.23003872730981811						) < 1e-2




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

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

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# 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(a__	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							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.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
