





'''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


__lowerCAmelCase							:	List[str]			=logging.getLogger(__name__)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""sequence-classification"""
 def __init__(				self      :List[Any]						,       lowercase_      :int							)->							Tuple:
     if type(__lowerCamelCase							) == dict:
         A__				=			Namespace(**__lowerCamelCase							)
     A__				=			glue_output_modes[hparams.task]
     A__				=			glue_tasks_num_labels[hparams.task]

     super().__init__(__lowerCamelCase						,       __lowerCamelCase						,       self.mode							)
 def      UpperCAmelCase_							(				self      :List[str]						,       **lowercase_      :Dict							)->							Optional[Any]:
     return self.model(**__lowerCamelCase							)
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Dict						,       lowercase_      :Tuple							)->							Dict:
     A__				=			{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

     A__				=			self(**__lowerCamelCase							)
     A__				=			outputs[0]

     A__				=			self.trainer.lr_schedulers[0]["scheduler"]
     A__				=			{"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
     return {"loss": loss, "log": tensorboard_logs}
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Union[str, Any]:
     A__				=			self.hparams
     A__				=			processors[args.task]()
     A__				=			processor.get_labels()

     for mode in ["train", "dev"]:
         A__				=			self._feature_file(__lowerCamelCase							)
         if os.path.exists(__lowerCamelCase							) and not args.overwrite_cache:
             logger.info("Loading features from cached file %s"						,       __lowerCamelCase							)
         else:
             logger.info("Creating features from dataset file at %s"						,       args.data_dir							)
             A__				=			(
                 processor.get_dev_examples(args.data_dir							)
                 if mode == "dev"
                 else processor.get_train_examples(args.data_dir							)
             )
             A__				=			convert_examples_to_features(
                 __lowerCamelCase						,       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"						,       __lowerCamelCase							)
             torch.save(__lowerCamelCase						,       __lowerCamelCase							)
 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :List[Any]						,       lowercase_      :Optional[Any]						,       lowercase_      :Optional[int] = False							)->							Optional[Any]:
     A__				=			"dev" if mode == "test" else mode

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

     return DataLoader(
         TensorDataset(__lowerCamelCase						,       __lowerCamelCase						,       __lowerCamelCase						,       __lowerCamelCase							)						,       batch_size=__lowerCamelCase						,       shuffle=__lowerCamelCase						,       )
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str						,       lowercase_      :List[Any]							)->							Any:
     A__				=			{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

     A__				=			self(**__lowerCamelCase							)
     A__,	A__				=			outputs[:2]
     A__				=			logits.detach().cpu().numpy()
     A__				=			inputs["labels"].detach().cpu().numpy()

     return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Tuple							)->							Tuple:
     A__				=			torch.stack([x["val_loss"] for x in outputs]							).mean().detach().cpu().item()
     A__				=			np.concatenate([x["pred"] for x in outputs]						,       axis=0							)

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

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

     A__				=			{**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task						,       __lowerCamelCase						,       __lowerCamelCase							)}

     A__				=			dict(results.items()							)
     A__				=			results
     return ret, preds_list, out_label_list
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Any							)->							Tuple:
     A__,	A__,	A__				=			self._eval_end(__lowerCamelCase							)
     A__				=			ret["log"]
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :int							)->							Tuple:
     A__,	A__,	A__				=			self._eval_end(__lowerCamelCase							)
     A__				=			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_							(				lowercase_      :List[str]						,       lowercase_      :Any							)->							List[Any]:
     BaseTransformer.add_model_specific_args(__lowerCamelCase						,       __lowerCamelCase							)
     parser.add_argument(
         "--max_seq_length"						,       default=1_28						,       type=__lowerCamelCase						,       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=__lowerCamelCase						,       required=__lowerCamelCase						,       help="The GLUE task to run"						,       )
     parser.add_argument(
         "--gpus"						,       default=0						,       type=__lowerCamelCase						,       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				(							):
    A__				=			argparse.ArgumentParser()
    add_generic_args(UpperCamelCase__						,       os.getcwd()       )
    A__				=			GLUETransformer.add_model_specific_args(UpperCamelCase__						,       os.getcwd()       )
    A__				=			parser.parse_args()

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

    A__				=			GLUETransformer(UpperCamelCase__       )
    A__				=			generic_train(UpperCamelCase__						,       UpperCamelCase__       )

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


if __name__ == "__main__":
  main()










import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],[0.002, 0.02, 0.2, 2] ):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,)



 def 				UpperCamelCase			(							self ):
        for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, eta in zip([1, 10, 49],[0.0, 0.5, 1.0] ):
               self.check_over_forward(time_step=__lowerCamelCase,eta=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,486 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        A__											=						self.dummy_sample_deter + 0.1
        A__											=						self.dummy_sample_deter - 0.1

        A__											=						samplea.shape[0]
        A__											=						torch.stack([samplea, samplea, samplea],dim=0 )
        A__											=						torch.arange(__lowerCamelCase )[0:3, None].repeat(1,__lowerCamelCase )

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





