'''simple docstring'''

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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							(DPMSolverSDEScheduler,)
		lowercase   :      Dict       =							10






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={
								    'num_train_timesteps': 1100,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'noise_sampler_seed': 0,
								}

								config.update(**__A				)
								return config






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,						[0.0_002, 0.002, 0.02]				):
														self.check_over_configs(beta_start=__A		,						beta_end=__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A				)

								scheduler.set_timesteps(self.num_inference_steps				)

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter * scheduler.init_noise_sigma
								_lowerCAmelCase										=sample.to(__A				)

								for i, t in enumerate(scheduler.timesteps				):
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 167.47_821_044_921_875				) < 1E-2
														assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 171.59_352_111_816_406				) < 1E-2
														assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1E-3
								else:
														assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1E-2
														assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1E-3






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config(prediction_type='v_prediction'				)
								_lowerCAmelCase										=scheduler_class(**__A				)

								scheduler.set_timesteps(self.num_inference_steps				)

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter * scheduler.init_noise_sigma
								_lowerCAmelCase										=sample.to(__A				)

								for i, t in enumerate(scheduler.timesteps				):
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 124.77_149_200_439_453				) < 1E-2
														assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 128.1_663_360_595_703				) < 1E-2
														assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1E-3
								else:
														assert abs(result_sum.item() - 119.8_487_548_828_125				) < 1E-2
														assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1E-3






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A				)

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

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 167.46_957_397_460_938				) < 1E-2
														assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 171.59_353_637_695_312				) < 1E-2
														assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1E-3
								else:
														assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1E-2
														assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1E-3






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A		,						use_karras_sigmas=__A				)

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

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
								_lowerCAmelCase										=sample.to(__A				)

								for t in scheduler.timesteps:
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 176.66_974_135_742_188				) < 1E-2
														assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1E-2
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 177.63_653_564_453_125				) < 1E-2
														assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1E-2
								else:
														assert abs(result_sum.item() - 170.3_135_223_388_672				) < 1E-2
														assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1E-2
'''simple docstring'''

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowercase_     		= logging.getLogger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'sequence-classification'






		def __init__(   self		,						__A				) ->     List[Any]:
								if type(__A				) == dict:
														_lowerCAmelCase										=Namespace(**__A				)
								_lowerCAmelCase										=glue_output_modes[hparams.task]
								_lowerCAmelCase										=glue_tasks_num_labels[hparams.task]

								super().__init__(__A		,						__A		,						self.mode				)






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return self.model(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
														_lowerCAmelCase										=batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase										=outputs[0]

								_lowerCAmelCase										=self.trainer.lr_schedulers[0]['scheduler']
								_lowerCAmelCase										={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss, "log": tensorboard_logs}






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.hparams
								_lowerCAmelCase										=processors[args.task]()
								_lowerCAmelCase										=processor.get_labels()

								for mode in ["train", "dev"]:
														_lowerCAmelCase										=self._feature_file(__A				)
														if os.path.exists(__A				) and not args.overwrite_cache:
																				logger.info('Loading features from cached file %s'		,						__A				)
														else:
																				logger.info('Creating features from dataset file at %s'		,						args.data_dir				)
																				_lowerCAmelCase										=(
																				    processor.get_dev_examples(args.data_dir				)
																				    if mode == 'dev'
																				    else processor.get_train_examples(args.data_dir				)
																				)
																				_lowerCAmelCase										=convert_examples_to_features(
																				    __A		,						self.tokenizer		,						max_length=args.max_seq_length		,						label_list=self.labels		,						output_mode=args.glue_output_mode		,						)
																				logger.info('Saving features into cached file %s'		,						__A				)
																				torch.save(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = False				) ->     DataLoader:
								_lowerCAmelCase										='dev' if mode == 'test' else mode

								_lowerCAmelCase										=self._feature_file(__A				)
								logger.info('Loading features from cached file %s'		,						__A				)
								_lowerCAmelCase										=torch.load(__A				)
								_lowerCAmelCase										=torch.tensor([f.input_ids for f in features]		,						dtype=torch.long				)
								_lowerCAmelCase										=torch.tensor([f.attention_mask for f in features]		,						dtype=torch.long				)
								_lowerCAmelCase										=torch.tensor([f.token_type_ids for f in features]		,						dtype=torch.long				)
								if self.hparams.glue_output_mode == "classification":
														_lowerCAmelCase										=torch.tensor([f.label for f in features]		,						dtype=torch.long				)
								elif self.hparams.glue_output_mode == "regression":
														_lowerCAmelCase										=torch.tensor([f.label for f in features]		,						dtype=torch.float				)

								return DataLoader(
								    TensorDataset(__A		,						__A		,						__A		,						__A				)		,						batch_size=__A		,						shuffle=__A		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[str]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
														_lowerCAmelCase										=batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=outputs[:2]
								_lowerCAmelCase										=logits.detach().cpu().numpy()
								_lowerCAmelCase										=inputs['labels'].detach().cpu().numpy()

								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






		def 		UpperCamelCase__       (   self		,						__A				) ->     tuple:
								_lowerCAmelCase										=torch.stack([x['val_loss'] for x in outputs]				).mean().detach().cpu().item()
								_lowerCAmelCase										=np.concatenate([x['pred'] for x in outputs]		,						axis=0				)

								if self.hparams.glue_output_mode == "classification":
														_lowerCAmelCase										=np.argmax(__A		,						axis=1				)
								elif self.hparams.glue_output_mode == "regression":
														_lowerCAmelCase										=np.squeeze(__A				)

								_lowerCAmelCase										=np.concatenate([x['target'] for x in outputs]		,						axis=0				)
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]

								_lowerCAmelCase										={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,						__A		,						__A				)}

								_lowerCAmelCase										=dict(results.items()				)
								_lowerCAmelCase										=results
								return ret, preds_list, out_label_list






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		@staticmethod
		def 		UpperCamelCase__       (   __A		,						__A				) ->     Any:
								BaseTransformer.add_model_specific_args(__A		,						__A				)
								parser.add_argument(
								    '--max_seq_length'		,						default=128		,						type=__A		,						help=(
								        'The maximum total input sequence length after tokenization. Sequences longer '
								        'than this will be truncated, sequences shorter will be padded.'
								    )		,						)

								parser.add_argument(
								    '--task'		,						default=''		,						type=__A		,						required=__A		,						help='The GLUE task to run'		,						)
								parser.add_argument(
								    '--gpus'		,						default=0		,						type=__A		,						help='The number of GPUs allocated for this, it is by default 0 meaning none'		,						)

								parser.add_argument(
								    '--overwrite_cache'		,						action='store_true'		,						help='Overwrite the cached training and evaluation sets'				)

								return parser



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser()
						add_generic_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=GLUETransformer.add_model_specific_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=parser.parse_args()

						# If output_dir not provided, a folder will be generated in pwd
						if args.output_dir is None:
												_lowerCAmelCase										=os.path.join(
												    './results'			,      F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'      )}'''			,      )
												os.makedirs(args.output_dir      )

						_lowerCAmelCase										=GLUETransformer(a__      )
						_lowerCAmelCase										=generic_train(a__			,      a__      )

						# Optionally, predict on dev set and write to output_dir
						if args.do_predict:
												_lowerCAmelCase										=sorted(glob.glob(os.path.join(args.output_dir			,      'checkpoint-epoch=*.ckpt'      )			,      recursive=a__      )      )
												_lowerCAmelCase										=model.load_from_checkpoint(checkpoints[-1]      )
												return trainer.test(a__      )


if __name__ == "__main__":
							main()





