'''simple docstring'''


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


__snake_case						  =		16
__snake_case						  =		32

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = 16 ,	SCREAMING_SNAKE_CASE_ = "bert-base-cased"						)			->Optional[int]:
  lowercase_     =       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       load_dataset("""glue""" ,	"""mrpc"""						)

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

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

  # 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(SCREAMING_SNAKE_CASE_						):
    # 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(SCREAMING_SNAKE_CASE_ ,	padding="""max_length""" ,	max_length=1_28 ,	return_tensors="""pt"""						)
    return tokenizer.pad(SCREAMING_SNAKE_CASE_ ,	padding="""longest""" ,	return_tensors="""pt"""						)

  # Instantiate dataloaders.
  lowercase_     =       DataLoader(
      tokenized_datasets["""train"""] ,	shuffle=SCREAMING_SNAKE_CASE_ ,	collate_fn=SCREAMING_SNAKE_CASE_ ,	batch_size=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       DataLoader(
      tokenized_datasets["""validation"""] ,	shuffle=SCREAMING_SNAKE_CASE_ ,	collate_fn=SCREAMING_SNAKE_CASE_ ,	batch_size=SCREAMING_SNAKE_CASE_						)

  return train_dataloader, eval_dataloader

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  # Initialize accelerator
  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(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       get_dataloaders(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

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

  # 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=SCREAMING_SNAKE_CASE_						)

  if accelerator.state.deepspeed_plugin is not None:
    lowercase_     =       accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    lowercase_     =       1
  lowercase_     =       (len(SCREAMING_SNAKE_CASE_						) * 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=SCREAMING_SNAKE_CASE_ ,	num_warmup_steps=0 ,	num_training_steps=SCREAMING_SNAKE_CASE_ ,	)
  else:
    lowercase_     =       DummyScheduler(SCREAMING_SNAKE_CASE_ ,	total_num_steps=SCREAMING_SNAKE_CASE_ ,	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_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       accelerator.prepare(
      SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # 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

  # Now we train the model
  lowercase_     =       evaluate.load("""glue""" ,	"""mrpc"""						)
  lowercase_     =       0
  lowercase_     =       {}
  for epoch in range(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    model.train()
    for step, batch in enumerate(SCREAMING_SNAKE_CASE_						):
      lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)
      lowercase_     =       outputs.loss
      lowercase_     =       loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE_						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

    model.eval()
    lowercase_     =       0
    for step, batch in enumerate(SCREAMING_SNAKE_CASE_						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      with torch.no_grad():
        lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)
      lowercase_     =       outputs.logits.argmax(dim=-1						)
      # It is slightly faster to call this once, than multiple times
      lowercase_     ,	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(SCREAMING_SNAKE_CASE_						) - 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=SCREAMING_SNAKE_CASE_ ,	references=SCREAMING_SNAKE_CASE_ ,	)

    lowercase_     =       metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:""" ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       eval_metric["""accuracy"""]

    if best_performance < eval_metric["accuracy"]:
      lowercase_     =       eval_metric["""accuracy"""]

  if args.performance_lower_bound is not None:
    assert (
        args.performance_lower_bound <= best_performance
    ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir ,	"""all_results.json"""						) ,	"""w"""						) as f:
      json.dump(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       )			->List[Any]:
  lowercase_     =       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""						)
  parser.add_argument(
      """--model_name_or_path""" ,	type=SCREAMING_SNAKE_CASE_ ,	default="""bert-base-cased""" ,	help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,	required=SCREAMING_SNAKE_CASE_ ,	)
  parser.add_argument(
      """--output_dir""" ,	type=SCREAMING_SNAKE_CASE_ ,	default=""".""" ,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,	)
  parser.add_argument(
      """--performance_lower_bound""" ,	type=SCREAMING_SNAKE_CASE_ ,	default=SCREAMING_SNAKE_CASE_ ,	help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" ,	)
  parser.add_argument(
      """--num_epochs""" ,	type=SCREAMING_SNAKE_CASE_ ,	default=3 ,	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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       main()


'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						(DDPMParallelScheduler,)




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       {
        """num_train_timesteps""": 1_000,
        """beta_start""": 0.0_0_0_1,
        """beta_end""": 0.0_2,
        """beta_schedule""": """linear""",
        """variance_type""": """fixed_small""",
        """clip_sample""": True,
    }

    config.update(**lowercase_      )
    return config




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    for timesteps in [1, 5, 100, 1_000]:
      self.check_over_configs(num_train_timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,				[0.0_0_2, 0.0_2, 0.2, 2]      ):
      self.check_over_configs(beta_start=lowercase_   ,				beta_end=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for variance in ["fixed_small", "fixed_large", "other"]:
      self.check_over_configs(variance_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    for clip_sample in [True, False]:
      self.check_over_configs(clip_sample=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for t in [0, 500, 999]:
      self.check_over_forward(time_step=lowercase_      )




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


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

    assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1e-5




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

    lowercase_     =       len(lowercase_      )

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

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

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

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 1_1_5_3.1_8_3_3      ) < 1e-2
    assert abs(result_mean.item() - 0.5_0_0_5      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


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

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       torch.manual_seed(0      )

    for t in reversed(range(lowercase_      )      ):
      # 1. predict noise residual
      lowercase_     =       model(lowercase_   ,				lowercase_      )

      # 2. predict previous mean of sample x_t-1
      lowercase_     =       scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				generator=lowercase_      ).prev_sample

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_5_8.9_6_0_6      ) < 1e-2
    assert abs(result_mean.item() - 0.3_3_7_2      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config(prediction_type="""v_prediction"""      )
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       torch.manual_seed(0      )

    for t in reversed(range(lowercase_      )      ):
      # 1. predict noise residual
      lowercase_     =       model(lowercase_   ,				lowercase_      )

      # 2. predict previous mean of sample x_t-1
      lowercase_     =       scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				generator=lowercase_      ).prev_sample

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_0_2.0_2_9_6      ) < 1e-2
    assert abs(result_mean.item() - 0.2_6_3_1      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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

    lowercase_     =       [100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=lowercase_      )

    lowercase_     =       scheduler.timesteps

    for i, timestep in enumerate(lowercase_      ):
      if i == len(lowercase_      ) - 1:
        lowercase_     =       -1
      else:
        lowercase_     =       timesteps[i + 1]

      lowercase_     =       scheduler.previous_timestep(lowercase_      )
      lowercase_     =       prev_t.item()

      self.assertEqual(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

    lowercase_     =       [100, 87, 50, 51, 0]

    with self.assertRaises(lowercase_   ,				msg="""`custom_timesteps` must be in descending order."""      ):
      scheduler.set_timesteps(timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

    lowercase_     =       [100, 87, 50, 1, 0]
    lowercase_     =       len(lowercase_      )

    with self.assertRaises(lowercase_   ,				msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""      ):
      scheduler.set_timesteps(num_inference_steps=lowercase_   ,				timesteps=lowercase_      )





  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


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

    lowercase_     =       [scheduler.config.num_train_timesteps]

    with self.assertRaises(
        lowercase_   ,				msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""   ,				):
      scheduler.set_timesteps(timesteps=lowercase_      )

