

"""simple docstring"""

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						(DDPMParallelScheduler,)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					**snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				{
            "num_train_timesteps": 1_000,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "variance_type": "fixed_small",
            "clip_sample": True,
        }

        config.update(**snake_case   )
        return config






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]			,					[0.002, 0.02, 0.2, 2]   ):
         self.check_over_configs(beta_start=snake_case			,					beta_end=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Union[str, Any] 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				Union[str, Any] 			=				scheduler_class(**snake_case   )

        assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.00_979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1e-5






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				str 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				Any 			=				scheduler_class(**snake_case   )

        lowerCAmelCase__   :				Any 			=				len(snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				self.dummy_model()
        lowerCAmelCase__   :				Union[str, Any] 			=				self.dummy_sample_deter
        lowerCAmelCase__   :				Dict 			=				self.dummy_sample_deter + 0.1
        lowerCAmelCase__   :				str 			=				self.dummy_sample_deter - 0.1

        lowerCAmelCase__   :				Tuple 			=				samplea.shape[0]
        lowerCAmelCase__   :				str 			=				torch.stack([samplea, samplea, samplea]			,					dim=0   )
        lowerCAmelCase__   :				str 			=				torch.arange(snake_case   )[0:3, None].repeat(1			,					snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				model(samples.flatten(0			,					1   )			,					timesteps.flatten(0			,					1   )   )
        lowerCAmelCase__   :				str 			=				scheduler.batch_step_no_noise(snake_case			,					timesteps.flatten(0			,					1   )			,					samples.flatten(0			,					1   )   )

        lowerCAmelCase__   :				Optional[Any] 			=				torch.sum(torch.abs(snake_case   )   )
        lowerCAmelCase__   :				List[Any] 			=				torch.mean(torch.abs(snake_case   )   )

        assert abs(result_sum.item() - 1_153.1_833   ) < 1e-2
        assert abs(result_mean.item() - 0.5_005   ) < 1e-3






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Dict 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				str 			=				scheduler_class(**snake_case   )

        lowerCAmelCase__   :				int 			=				len(snake_case   )

        lowerCAmelCase__   :				int 			=				self.dummy_model()
        lowerCAmelCase__   :				Optional[int] 			=				self.dummy_sample_deter
        lowerCAmelCase__   :				str 			=				torch.manual_seed(0   )

        for t in reversed(range(snake_case   )   ):
         # 1. predict noise residual
         lowerCAmelCase__   :				Union[str, Any] 			=				model(snake_case			,					snake_case   )

         # 2. predict previous mean of sample x_t-1
         lowerCAmelCase__   :				Any 			=				scheduler.step(snake_case			,					snake_case			,					snake_case			,					generator=snake_case   ).prev_sample

         lowerCAmelCase__   :				Optional[Any] 			=				pred_prev_sample

        lowerCAmelCase__   :				Optional[int] 			=				torch.sum(torch.abs(snake_case   )   )
        lowerCAmelCase__   :				str 			=				torch.mean(torch.abs(snake_case   )   )

        assert abs(result_sum.item() - 258.9_606   ) < 1e-2
        assert abs(result_mean.item() - 0.3_372   ) < 1e-3






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Union[str, Any] 			=				self.get_scheduler_config(prediction_type="v_prediction"   )
        lowerCAmelCase__   :				Optional[Any] 			=				scheduler_class(**snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				len(snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				self.dummy_model()
        lowerCAmelCase__   :				str 			=				self.dummy_sample_deter
        lowerCAmelCase__   :				List[Any] 			=				torch.manual_seed(0   )

        for t in reversed(range(snake_case   )   ):
         # 1. predict noise residual
         lowerCAmelCase__   :				List[Any] 			=				model(snake_case			,					snake_case   )

         # 2. predict previous mean of sample x_t-1
         lowerCAmelCase__   :				List[str] 			=				scheduler.step(snake_case			,					snake_case			,					snake_case			,					generator=snake_case   ).prev_sample

         lowerCAmelCase__   :				Dict 			=				pred_prev_sample

        lowerCAmelCase__   :				Union[str, Any] 			=				torch.sum(torch.abs(snake_case   )   )
        lowerCAmelCase__   :				List[str] 			=				torch.mean(torch.abs(snake_case   )   )

        assert abs(result_sum.item() - 202.0_296   ) < 1e-2
        assert abs(result_mean.item() - 0.2_631   ) < 1e-3






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				str 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				List[str] 			=				scheduler_class(**snake_case   )

        lowerCAmelCase__   :				Dict 			=				[100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=snake_case   )

        lowerCAmelCase__   :				Dict 			=				scheduler.timesteps

        for i, timestep in enumerate(snake_case   ):
         if i == len(snake_case   ) - 1:
          lowerCAmelCase__   :				Optional[int] 			=				-1
         else:
          lowerCAmelCase__   :				str 			=				timesteps[i + 1]

         lowerCAmelCase__   :				List[str] 			=				scheduler.previous_timestep(snake_case   )
         lowerCAmelCase__   :				Any 			=				prev_t.item()

         self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Optional[Any] 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				Dict 			=				scheduler_class(**snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				[100, 87, 50, 51, 0]

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Dict 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				Optional[int] 			=				scheduler_class(**snake_case   )

        lowerCAmelCase__   :				int 			=				[100, 87, 50, 1, 0]
        lowerCAmelCase__   :				Union[str, Any] 			=				len(snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				List[Any] 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				Tuple 			=				scheduler_class(**snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				[scheduler.config.num_train_timesteps]

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









"""simple docstring"""

import argparse
import gc
import json
import os

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


_UpperCamelCase		     =    1_6
_UpperCamelCase		     =    3_2



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 return int(x / 2**2_0   )






class 			__a					:





       """simple docstring"""


       def __enter__(   self   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowerCAmelCase__   :				str 			=				torch.cuda.memory_allocated()
        return self






       def __exit__(   self			,					*snake_case   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        lowerCAmelCase__   :				List[str] 			=				torch.cuda.memory_allocated()
        lowerCAmelCase__   :				Optional[int] 			=				torch.cuda.max_memory_allocated()
        lowerCAmelCase__   :				List[str] 			=				bamb(self.end - self.begin   )
        lowerCAmelCase__   :				Union[str, Any] 			=				bamb(self.peak - self.begin   )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__ = 1_6					,					lowercase__ = "bert-base-cased"					,					lowercase__ = 3_2_0					,					lowercase__ = 1_6_0					,					)			->						str:
 lowerCAmelCase__   :				Union[str, Any] 			=				AutoTokenizer.from_pretrained(lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				load_dataset(
     "glue"					,					"mrpc"					,					split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

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

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

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

 def collate_fn(lowercase__   ):
  # 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(lowercase__					,					padding="max_length"					,					max_length=1_2_8					,					return_tensors="pt"   )
  return tokenizer.pad(lowercase__					,					padding="longest"					,					return_tensors="pt"   )

 # Instantiate dataloaders.
 lowerCAmelCase__   :				Tuple 			=				DataLoader(
     tokenized_datasets["train"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				DataLoader(
     tokenized_datasets["validation"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )

 return train_dataloader, eval_dataloader



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Dict:
 # Initialize accelerator
 lowerCAmelCase__   :				Optional[Any] 			=				Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCAmelCase__   :				Union[str, Any] 			=				config["lr"]
 lowerCAmelCase__   :				int 			=				int(config["num_epochs"]   )
 lowerCAmelCase__   :				Tuple 			=				int(config["seed"]   )
 lowerCAmelCase__   :				str 			=				int(config["batch_size"]   )
 lowerCAmelCase__   :				Any 			=				args.model_name_or_path

 set_seed(lowercase__   )
 lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[Any] 			=				get_dataloaders(lowercase__					,					lowercase__					,					lowercase__					,					args.n_train					,					args.n_val   )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowerCAmelCase__   :				Any 			=				AutoModelForSequenceClassification.from_pretrained(lowercase__					,					return_dict=lowercase__   )

 # Instantiate optimizer
 lowerCAmelCase__   :				List[str] 			=				(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 lowerCAmelCase__   :				Any 			=				optimizer_cls(params=model.parameters()					,					lr=lowercase__   )

 if accelerator.state.deepspeed_plugin is not None:
  lowerCAmelCase__   :				List[str] 			=				accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  lowerCAmelCase__   :				Dict 			=				1
 lowerCAmelCase__   :				List[str] 			=				(len(lowercase__   ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  lowerCAmelCase__   :				Any 			=				get_linear_schedule_with_warmup(
      optimizer=lowercase__					,					num_warmup_steps=0					,					num_training_steps=lowercase__					,					)
 else:
  lowerCAmelCase__   :				Optional[int] 			=				DummyScheduler(lowercase__					,					total_num_steps=lowercase__					,					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.
 lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				accelerator.prepare(
     lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )

 # We need to keep track of how many total steps we have iterated over
 lowerCAmelCase__   :				str 			=				0
 # We also need to keep track of the stating epoch so files are named properly
 lowerCAmelCase__   :				Tuple 			=				0

 # Now we train the model
 lowerCAmelCase__   :				List[Any] 			=				{}
 for epoch in range(lowercase__					,					lowercase__   ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(lowercase__   ):
    lowerCAmelCase__   :				Optional[Any] 			=				model(**lowercase__   )
    lowerCAmelCase__   :				Optional[int] 			=				outputs.loss
    lowerCAmelCase__   :				Any 			=				loss / gradient_accumulation_steps
    accelerator.backward(lowercase__   )
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

    overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
  accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
  accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
  accelerator.print(
      "Total Peak Memory consumed during the train (max): {}".format(
          tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
  lowerCAmelCase__   :				Optional[int] 			=				tracemalloc.peaked + bamb(tracemalloc.begin   )
  if args.peak_memory_upper_bound is not None:
   assert (
       train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
   ), "Peak memory usage exceeded the upper bound"

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



def       SCREAMING_SNAKE_CASE							( )			->						Optional[Any]:
 lowerCAmelCase__   :				int 			=				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
 parser.add_argument(
     "--model_name_or_path"					,					type=lowercase__					,					default="bert-base-cased"					,					help="Path to pretrained model or model identifier from huggingface.co/models."					,					required=lowercase__					,					)
 parser.add_argument(
     "--output_dir"					,					type=lowercase__					,					default="."					,					help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,					)
 parser.add_argument(
     "--peak_memory_upper_bound"					,					type=lowercase__					,					default=lowercase__					,					help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."					,					)
 parser.add_argument(
     "--n_train"					,					type=lowercase__					,					default=3_2_0					,					help="Number of training examples to use."					,					)
 parser.add_argument(
     "--n_val"					,					type=lowercase__					,					default=1_6_0					,					help="Number of validation examples to use."					,					)
 parser.add_argument(
     "--num_epochs"					,					type=lowercase__					,					default=1					,					help="Number of train epochs."					,					)
 lowerCAmelCase__   :				Optional[int] 			=				parser.parse_args()
 lowerCAmelCase__   :				Dict 			=				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
 training_function(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    main()






