


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a : Any		=				16
a : Dict		=				32


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16       ,			_UpperCamelCase = "bert-base-cased"  ):


   '''simple docstring'''







   __lowercase 					=   AutoTokenizer.from_pretrained(__lowerCAmelCase  )
   __lowercase 					=   load_dataset('''glue'''       ,			'''mrpc'''  )

   def tokenize_function(_UpperCamelCase  ):
      # max_length=None => use the model max length (it's actually the default)
      __lowercase 					=   tokenizer(examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=__lowerCAmelCase       ,			max_length=__lowerCAmelCase  )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __lowercase 					=   datasets.map(
       __lowerCAmelCase       ,			batched=__lowerCAmelCase       ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,			load_from_cache_file=__lowerCAmelCase  )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   __lowercase 					=   tokenized_datasets.rename_column('''label'''       ,			'''labels'''  )

   def collate_fn(_UpperCamelCase  ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(__lowerCAmelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
      return tokenizer.pad(__lowerCAmelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

   # Instantiate dataloaders.
   __lowercase 					=   DataLoader(
       tokenized_datasets['''train''']       ,			shuffle=__lowerCAmelCase       ,			collate_fn=__lowerCAmelCase       ,			batch_size=__lowerCAmelCase  )
   __lowercase 					=   DataLoader(
       tokenized_datasets['''validation''']       ,			shuffle=__lowerCAmelCase       ,			collate_fn=__lowerCAmelCase       ,			batch_size=__lowerCAmelCase  )

   return train_dataloader, eval_dataloader


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


   '''simple docstring'''







   model.eval()
   __lowercase 					=   0
   for step, batch in enumerate(__lowerCAmelCase  ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device  )
      with torch.no_grad():
         __lowercase 					=   model(**__lowerCAmelCase  )
      __lowercase 					=   outputs.logits.argmax(dim=-1  )
      # It is slightly faster to call this once, than multiple times
      __lowercase 					=   accelerator.gather(
          (predictions, batch['''labels'''])  )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
         if step == len(__lowerCAmelCase  ) - 1:
            __lowercase 					=   predictions[: len(eval_dataloader.dataset  ) - samples_seen]
            __lowercase 					=   references[: len(eval_dataloader.dataset  ) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=__lowerCAmelCase       ,			references=__lowerCAmelCase       ,			)

   __lowercase 					=   metric.compute()
   return eval_metric["accuracy"]


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


   '''simple docstring'''







   __lowercase 					=   Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowercase 					=   config["lr"]
   __lowercase 					=   int(config['''num_epochs''']  )
   __lowercase 					=   int(config['''seed''']  )
   __lowercase 					=   int(config['''batch_size''']  )
   __lowercase 					=   args.model_name_or_path

   set_seed(__lowerCAmelCase  )
   __lowercase 					=   get_dataloaders(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase  )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowercase 					=   AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase       ,			return_dict=__lowerCAmelCase  )

   # Instantiate optimizer
   __lowercase 					=   (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __lowercase 					=   optimizer_cls(params=model.parameters()       ,			lr=__lowerCAmelCase  )

   if accelerator.state.deepspeed_plugin is not None:
      __lowercase 					=   accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      __lowercase 					=   1
   __lowercase 					=   (len(__lowerCAmelCase  ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      __lowercase 					=   get_linear_schedule_with_warmup(
          optimizer=__lowerCAmelCase       ,			num_warmup_steps=0       ,			num_training_steps=__lowerCAmelCase       ,			)
   else:
      __lowercase 					=   DummyScheduler(__lowerCAmelCase       ,			total_num_steps=__lowerCAmelCase       ,			warmup_num_steps=0  )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   __lowercase 					=   accelerator.prepare(
       __lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase  )

   # We need to keep track of how many total steps we have iterated over
   __lowercase 					=   0
   # We also need to keep track of the stating epoch so files are named properly
   __lowercase 					=   0
   __lowercase 					=   evaluate.load('''glue'''       ,			'''mrpc'''  )
   __lowercase 					=   num_epochs

   if args.partial_train_epoch is not None:
      __lowercase 					=   args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint  )
      __lowercase 					=   args.resume_from_checkpoint.split('''epoch_'''  )[1]
      __lowercase 					=   ""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      __lowercase 					=   int(__lowerCAmelCase  ) + 1
      __lowercase 					=   evaluation_loop(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase  )
      accelerator.print('''resumed checkpoint performance:'''       ,			__lowerCAmelCase  )
      accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''       ,			lr_scheduler.get_lr()[0]  )
      accelerator.print('''resumed optimizers\'s lr:'''       ,			optimizer.param_groups[0]['''lr''']  )
      with open(os.path.join(args.output_dir       ,			F'state_{starting_epoch-1}.json'  )       ,			'''r'''  ) as f:
         __lowercase 					=   json.load(__lowerCAmelCase  )
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   __lowercase 					=   {}
   for epoch in range(__lowerCAmelCase       ,			__lowerCAmelCase  ):
      model.train()
      for step, batch in enumerate(__lowerCAmelCase  ):
         __lowercase 					=   model(**__lowerCAmelCase  )
         __lowercase 					=   outputs.loss
         __lowercase 					=   loss / gradient_accumulation_steps
         accelerator.backward(__lowerCAmelCase  )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      __lowercase 					=   F'epoch_{epoch}'
      __lowercase 					=   os.path.join(args.output_dir       ,			__lowerCAmelCase  )
      accelerator.save_state(__lowerCAmelCase  )
      __lowercase 					=   evaluation_loop(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase  )
      __lowercase 					=   accuracy
      __lowercase 					=   lr_scheduler.get_lr()[0]
      __lowercase 					=   optimizer.param_groups[0]["lr"]
      __lowercase 					=   epoch
      __lowercase 					=   overall_step
      accelerator.print(F'epoch {epoch}:'       ,			__lowerCAmelCase  )

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir       ,			F'state_{epoch}.json'  )       ,			'''w'''  ) as f:
            json.dump(__lowerCAmelCase       ,			__lowerCAmelCase  )


def 		lowercase_  (     ):


   '''simple docstring'''







   __lowercase 					=   argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''  )
   parser.add_argument(
       '''--model_name_or_path'''       ,			type=__lowerCAmelCase       ,			default='''bert-base-cased'''       ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,			required=__lowerCAmelCase       ,			)
   parser.add_argument(
       '''--output_dir'''       ,			type=__lowerCAmelCase       ,			default='''.'''       ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,			)
   parser.add_argument(
       '''--resume_from_checkpoint'''       ,			type=__lowerCAmelCase       ,			default=__lowerCAmelCase       ,			help='''If the training should continue from a checkpoint folder.'''       ,			)
   parser.add_argument(
       '''--partial_train_epoch'''       ,			type=__lowerCAmelCase       ,			default=__lowerCAmelCase       ,			help='''If passed, the training will stop after this number of epochs.'''       ,			)
   parser.add_argument(
       '''--num_epochs'''       ,			type=__lowerCAmelCase       ,			default=2       ,			help='''Number of train epochs.'''       ,			)
   __lowercase 					=   parser.parse_args()
   __lowercase 					=   {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

   training_function(__lowerCAmelCase       ,			__lowerCAmelCase  )


if __name__ == "__main__":
 main()






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DEISMultistepScheduler,)
					__UpperCAmelCase            =				(("num_inference_steps", 25),)






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config(**snake_case_						)
											__lowercase 					=   scheduler_class(**snake_case_						)
											scheduler.set_timesteps(snake_case_						)
											# copy over dummy past residuals
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

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

											__lowercase					, __lowercase 					=   sample, sample
											for t in range(snake_case_ ,       time_step + scheduler.config.solver_order + 1						):
														__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
														__lowercase 					=   new_scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

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






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								pass






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**snake_case_						)
											scheduler.set_timesteps(snake_case_						)

											# copy over dummy past residuals (must be after setting timesteps)
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

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

														# copy over dummy past residual (must be after setting timesteps)
														__lowercase 					=   dummy_past_residuals[: new_scheduler.config.solver_order]

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   new_scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

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






					def A      (				self ,       snake_case_=None ,       **snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if scheduler is None:
											__lowercase 					=   self.scheduler_classes[0]
											__lowercase 					=   self.get_scheduler_config(**snake_case_						)
											__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(**snake_case_						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								scheduler.set_timesteps(snake_case_						)

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

								return sample






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)

								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**snake_case_						)

											__lowercase 					=   self.dummy_sample
											__lowercase 					=   0.1 * sample

											if num_inference_steps is not None and hasattr(snake_case_ ,       '''set_timesteps'''						):
														scheduler.set_timesteps(snake_case_						)
											elif num_inference_steps is not None and not hasattr(snake_case_ ,       '''set_timesteps'''						):
														__lowercase 					=   num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

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

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

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






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   DEISMultistepScheduler(**self.get_scheduler_config()						)
								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3

								__lowercase 					=   DPMSolverSinglestepScheduler.from_config(scheduler.config						)
								__lowercase 					=   DPMSolverMultistepScheduler.from_config(scheduler.config						)
								__lowercase 					=   UniPCMultistepScheduler.from_config(scheduler.config						)
								__lowercase 					=   DEISMultistepScheduler.from_config(scheduler.config						)

								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






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







								'''simple docstring'''

								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								self.check_over_configs(thresholding=snake_case_						)
								for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
														for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    thresholding=snake_case_ ,       prediction_type=snake_case_ ,       sample_max_value=snake_case_ ,       algorithm_type='''deis''' ,       solver_order=snake_case_ ,       solver_type=snake_case_ ,       )






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

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






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







								'''simple docstring'''

								for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
														for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				__lowercase 					=   self.full_loop(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				assert not torch.isnan(snake_case_						).any(), "Samples have nan numbers"






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								self.check_over_configs(lower_order_final=snake_case_						)
								self.check_over_configs(lower_order_final=snake_case_						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_forward(num_inference_steps=snake_case_ ,       time_step=0						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   self.full_loop()
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(prediction_type='''v_prediction'''						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.0_9_1						) < 1e-3






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(thresholding=snake_case_ ,       dynamic_thresholding_ratio=0						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case_						)

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

								assert sample.dtype == torch.floataa


