




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						:   Union[str, Any]			=   logging.getLogger(__name__)
class 			_UpperCamelCase      (				a__		):
      UpperCAmelCase_        =		"sequence-classification"
      def __init__(			self   :Optional[Any] ,      lowerCamelCase   :Dict      )     ->			List[Any]:
       if type(lowerCamelCase_      ) == dict:
        UpperCAmelCase__							 =						Namespace(**lowerCamelCase_      )
       UpperCAmelCase__							 =						glue_output_modes[hparams.task]
       UpperCAmelCase__							 =						glue_tasks_num_labels[hparams.task]

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

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

       UpperCAmelCase__							 =						self(**lowerCamelCase_      )
       UpperCAmelCase__							 =						outputs[0]

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

       for mode in ["train", "dev"]:
        UpperCAmelCase__							 =						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      )
         UpperCAmelCase__							 =						(
             processor.get_dev_examples(args.data_dir      )
             if mode == "dev"
             else processor.get_train_examples(args.data_dir      )
         )
         UpperCAmelCase__							 =						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   :Tuple ,      lowerCamelCase   :str ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :Any = False      )     ->			DataLoader:
       UpperCAmelCase__							 =						"dev" if mode == "test" else mode

       UpperCAmelCase__							 =						self._feature_file(lowerCamelCase_      )
       logger.info("Loading features from cached file %s" ,      lowerCamelCase_      )
       UpperCAmelCase__							 =						torch.load(lowerCamelCase_      )
       UpperCAmelCase__							 =						torch.tensor([f.input_ids for f in features] ,      dtype=torch.long      )
       UpperCAmelCase__							 =						torch.tensor([f.attention_mask for f in features] ,      dtype=torch.long      )
       UpperCAmelCase__							 =						torch.tensor([f.token_type_ids for f in features] ,      dtype=torch.long      )
       if self.hparams.glue_output_mode == "classification":
        UpperCAmelCase__							 =						torch.tensor([f.label for f in features] ,      dtype=torch.long      )
       elif self.hparams.glue_output_mode == "regression":
        UpperCAmelCase__							 =						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   :int ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[int]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

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

       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Union[str, Any]      )     ->			tuple:
       UpperCAmelCase__							 =						torch.stack([x["val_loss"] for x in outputs]      ).mean().detach().cpu().item()
       UpperCAmelCase__							 =						np.concatenate([x["pred"] for x in outputs] ,      axis=0      )

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

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

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

       UpperCAmelCase__							 =						dict(results.items()      )
       UpperCAmelCase__							 =						results
       return ret, preds_list, out_label_list
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Any      )     ->			dict:
       UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						self._eval_end(lowerCamelCase_      )
       UpperCAmelCase__							 =						ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :int      )     ->			dict:
       UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						self._eval_end(lowerCamelCase_      )
       UpperCAmelCase__							 =						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_						(			lowerCamelCase   :Tuple ,      lowerCamelCase   :Optional[Any]      )     ->			Union[str, Any]:
       BaseTransformer.add_model_specific_args(lowerCamelCase_ ,      lowerCamelCase_      )
       parser.add_argument(
           "--max_seq_length" ,      default=128 ,      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 						lowerCAmelCase		(		):




 """simple docstring"""







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

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

 UpperCAmelCase__							 =						GLUETransformer(_lowerCAmelCase	)
 UpperCAmelCase__							 =						generic_train(_lowerCAmelCase						,							_lowerCAmelCase	)

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


if __name__ == "__main__":
  main()











import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		(IPNDMScheduler,)
      UpperCAmelCase_        =		(("""num_inference_steps""", 50),)
      def 	UpperCAmelCase_						(			self   :List[str] ,      **lowerCamelCase   :List[Any]      )     ->			List[str]:
       UpperCAmelCase__							 =						{"num_train_timesteps": 1000}
       config.update(**lowerCamelCase      )
       return config
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Union[str, Any]=0 ,      **lowerCamelCase   :str      )     ->			str:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )
        # copy over dummy past residuals
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         new_scheduler.set_timesteps(lowerCamelCase      )
         # copy over dummy past residuals
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Tuple:
       pass
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[str]=0 ,      **lowerCamelCase   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )

        # copy over dummy past residuals (must be after setting timesteps)
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(lowerCamelCase      )

         # copy over dummy past residual (must be after setting timesteps)
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :int ,      **lowerCamelCase   :Any      )     ->			int:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						10
       UpperCAmelCase__							 =						self.dummy_model()
       UpperCAmelCase__							 =						self.dummy_sample_deter
       scheduler.set_timesteps(lowerCamelCase      )

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       return sample
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )

       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

        UpperCAmelCase__							 =						self.dummy_sample
        UpperCAmelCase__							 =						0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase ,      "set_timesteps"      ):
         scheduler.set_timesteps(lowerCamelCase      )
        elif num_inference_steps is not None and not hasattr(lowerCamelCase ,      "set_timesteps"      ):
         UpperCAmelCase__							 =						num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.timesteps[5]
        UpperCAmelCase__							 =						scheduler.timesteps[6]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Tuple:
       for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase ,      time_step=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       for t, num_inference_steps in zip([1, 5, 10] ,      [10, 50, 100]      ):
        self.check_over_forward(num_inference_steps=lowerCamelCase ,      time_step=lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :Any      )     ->			Dict:
       UpperCAmelCase__							 =						self.full_loop()
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_mean.item() - 254_0529      ) < 10





