





'''simple docstring'''


import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase :					Any 							=16
lowerCAmelCase :					Optional[Any] 							=32
def 							UpperCAmelCase_  (						__lowerCamelCase : Accelerator     ,__lowerCamelCase : int = 16			):
   lowercase_      :List[Any]         =			AutoTokenizer.from_pretrained("bert-base-cased"			)
   lowercase_      :Any         =			load_dataset("glue"     ,"mrpc"			)

   def tokenize_function(__lowerCamelCase : Dict			):
      # max_length=None => use the model max length (it's actually the default)
      lowercase_      :Any         =			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
   # starting with the main process first:
   with accelerator.main_process_first():
      lowercase_      :List[str]         =			datasets.map(
          __lowerCamelCase     ,batched=__lowerCamelCase     ,remove_columns=["idx", "sentence1", "sentence2"]     ,)

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

   def collate_fn(__lowerCamelCase : Dict			):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowercase_      :int         =			1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         lowercase_      :str         =			16
      elif accelerator.mixed_precision != "no":
         lowercase_      :Dict         =			8
      else:
         lowercase_      :Optional[int]         =			None

      return tokenizer.pad(
          __lowerCamelCase     ,padding="longest"     ,max_length=__lowerCamelCase     ,pad_to_multiple_of=__lowerCamelCase     ,return_tensors="pt"     ,)

   # Instantiate dataloaders.
   lowercase_      :Any         =			DataLoader(
       tokenized_datasets["train"]     ,shuffle=__lowerCamelCase     ,collate_fn=__lowerCamelCase     ,batch_size=__lowerCamelCase     ,drop_last=__lowerCamelCase			)
   lowercase_      :str         =			DataLoader(
       tokenized_datasets["validation"]     ,shuffle=__lowerCamelCase     ,collate_fn=__lowerCamelCase     ,batch_size=__lowerCamelCase     ,drop_last=(accelerator.mixed_precision == "fp8")     ,)

   return train_dataloader, eval_dataloader
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Union[str, Any]			):
   # Initialize accelerator
   lowercase_      :str         =			Accelerator(cpu=args.cpu     ,mixed_precision=args.mixed_precision			)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase_      :Dict         =			config["lr"]
   lowercase_      :int         =			int(config["num_epochs"]			)
   lowercase_      :List[str]         =			int(config["seed"]			)
   lowercase_      :Optional[Any]         =			int(config["batch_size"]			)

   lowercase_      :Optional[Any]         =			evaluate.load("glue"     ,"mrpc"			)

   # If the batch size is too big we use gradient accumulation
   lowercase_      :Any         =			1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      lowercase_      :str         =			batch_size // MAX_GPU_BATCH_SIZE
      lowercase_      :Tuple         =			MAX_GPU_BATCH_SIZE

   set_seed(__lowerCamelCase			)
   lowercase_      ,							lowercase_      :int         =			get_dataloaders(__lowerCamelCase     ,__lowerCamelCase			)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowercase_      :Tuple         =			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"     ,return_dict=__lowerCamelCase			)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   lowercase_      :Tuple         =			model.to(accelerator.device			)
   # Instantiate optimizer
   lowercase_      :Union[str, Any]         =			AdamW(params=model.parameters()     ,lr=__lowerCamelCase			)

   # Instantiate scheduler
   lowercase_      :Optional[Any]         =			get_linear_schedule_with_warmup(
       optimizer=__lowerCamelCase     ,num_warmup_steps=1_00     ,num_training_steps=(len(__lowerCamelCase			) * num_epochs) // gradient_accumulation_steps     ,)

   # 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_      :List[str]         =			accelerator.prepare(
       __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)

   # Now we train the model
   for epoch in range(__lowerCamelCase			):
      model.train()
      for step, batch in enumerate(__lowerCamelCase			):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device			)
         lowercase_      :Union[str, Any]         =			model(**__lowerCamelCase			)
         lowercase_      :Dict         =			outputs.loss
         lowercase_      :Optional[Any]         =			loss / gradient_accumulation_steps
         accelerator.backward(__lowerCamelCase			)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      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_      :List[str]         =			model(**__lowerCamelCase			)
         lowercase_      :Dict         =			outputs.logits.argmax(dim=-1			)
         lowercase_      ,							lowercase_      :Dict         =			accelerator.gather_for_metrics((predictions, batch["labels"])			)
         metric.add_batch(
             predictions=__lowerCamelCase     ,references=__lowerCamelCase     ,)

      lowercase_      :List[str]         =			metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'epoch {epoch}:'     ,__lowerCamelCase			)
def 							UpperCAmelCase_  (						):
   lowercase_      :List[str]         =			argparse.ArgumentParser(description="Simple example of training script."			)
   parser.add_argument(
       "--mixed_precision"     ,type=__lowerCamelCase     ,default=__lowerCamelCase     ,choices=["no", "fp16", "bf16", "fp8"]     ,help="Whether to use mixed precision. Choose"
       "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
       "and an Nvidia Ampere GPU."     ,)
   parser.add_argument("--cpu"     ,action="store_true"     ,help="If passed, will train on the CPU."			)
   lowercase_      :Dict         =			parser.parse_args()
   lowercase_      :Any         =			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(__lowerCamelCase     ,__lowerCamelCase			)


if __name__ == "__main__":
 main()









'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 				a_       (    _lowerCAmelCase							):
     __A								=      (UniPCMultistepScheduler,)
     __A								=      (("num_inference_steps", 25),)





     def 							lowercase__				(			self		: Tuple     ,				**lowercase		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Any         =			{
            "num_train_timesteps": 1_000,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "solver_order": 2,
            "solver_type": "bh2",
        }

        config.update(**lowercase						)
        return config





     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: str=0     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			dict(self.forward_default_kwargs						)
        lowercase_      :Tuple         =			kwargs.pop("num_inference_steps"     ,				lowercase						)
        lowercase_      :Union[str, Any]         =			self.dummy_sample
        lowercase_      :Union[str, Any]         =			0.1 * sample
        lowercase_      :Any         =			[residual + 0.2, residual + 0.15, residual + 0.10]

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

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(lowercase						)
              lowercase_      :str         =			scheduler_class.from_pretrained(lowercase						)
              new_scheduler.set_timesteps(lowercase						)
              # copy over dummy past residuals
              lowercase_      :List[Any]         =			dummy_past_residuals[: new_scheduler.config.solver_order]

           lowercase_      ,							lowercase_      :Tuple         =			sample, sample
           for t in range(lowercase     ,				time_step + scheduler.config.solver_order + 1						):
              lowercase_      :Optional[int]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
              lowercase_      :Union[str, Any]         =			new_scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

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





     def 							lowercase__				(			self		: str     ,				lowercase		: Any=0     ,				**lowercase		: str						):





        """simple docstring"""




        lowercase_      :Tuple         =			dict(self.forward_default_kwargs						)
        lowercase_      :Dict         =			kwargs.pop("num_inference_steps"     ,				lowercase						)
        lowercase_      :str         =			self.dummy_sample
        lowercase_      :Optional[int]         =			0.1 * sample
        lowercase_      :Optional[int]         =			[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Any         =			self.get_scheduler_config()
           lowercase_      :Tuple         =			scheduler_class(**lowercase						)
           scheduler.set_timesteps(lowercase						)

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

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

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

           lowercase_      :Dict         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
           lowercase_      :str         =			new_scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

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





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: Optional[int]=None     ,				**lowercase		: int						):





        """simple docstring"""




        if scheduler is None:
           lowercase_      :int         =			self.scheduler_classes[0]
           lowercase_      :Optional[Any]         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Optional[Any]         =			scheduler_class(**lowercase						)

        lowercase_      :str         =			self.scheduler_classes[0]
        lowercase_      :Optional[int]         =			self.get_scheduler_config(**lowercase						)
        lowercase_      :List[Any]         =			scheduler_class(**lowercase						)

        lowercase_      :str         =			10
        lowercase_      :List[str]         =			self.dummy_model()
        lowercase_      :Optional[int]         =			self.dummy_sample_deter
        scheduler.set_timesteps(lowercase						)

        for i, t in enumerate(scheduler.timesteps						):
           lowercase_      :int         =			model(lowercase     ,				lowercase						)
           lowercase_      :Dict         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase						).prev_sample

        return sample





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :str         =			dict(self.forward_default_kwargs						)

        lowercase_      :Union[str, Any]         =			kwargs.pop("num_inference_steps"     ,				lowercase						)

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Optional[Any]         =			self.get_scheduler_config()
           lowercase_      :Optional[int]         =			scheduler_class(**lowercase						)

           lowercase_      :Union[str, Any]         =			self.dummy_sample
           lowercase_      :str         =			0.1 * sample

           if num_inference_steps is not None and hasattr(lowercase     ,				"set_timesteps"						):
              scheduler.set_timesteps(lowercase						)
           elif num_inference_steps is not None and not hasattr(lowercase     ,				"set_timesteps"						):
              lowercase_      :str         =			num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowercase_      :Dict         =			[residual + 0.2, residual + 0.15, residual + 0.10]
           lowercase_      :Any         =			dummy_past_residuals[: scheduler.config.solver_order]

           lowercase_      :Optional[int]         =			scheduler.timesteps[5]
           lowercase_      :List[Any]         =			scheduler.timesteps[6]

           lowercase_      :Optional[Any]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
           lowercase_      :List[str]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

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





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			UniPCMultistepScheduler(**self.get_scheduler_config()						)
        lowercase_      :Dict         =			self.full_loop(scheduler=lowercase						)
        lowercase_      :List[Any]         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3

        lowercase_      :List[str]         =			DPMSolverSinglestepScheduler.from_config(scheduler.config						)
        lowercase_      :Tuple         =			DEISMultistepScheduler.from_config(scheduler.config						)
        lowercase_      :Optional[int]         =			DPMSolverMultistepScheduler.from_config(scheduler.config						)
        lowercase_      :Tuple         =			UniPCMultistepScheduler.from_config(scheduler.config						)

        lowercase_      :Tuple         =			self.full_loop(scheduler=lowercase						)
        lowercase_      :Tuple         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=lowercase						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        self.check_over_configs(thresholding=lowercase						)
        for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        thresholding=lowercase     ,				prediction_type=lowercase     ,				sample_max_value=lowercase     ,				solver_order=lowercase     ,				solver_type=lowercase     ,				)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=lowercase     ,				solver_type=lowercase     ,				prediction_type=lowercase     ,				)
                 lowercase_      :Tuple         =			self.full_loop(
                     solver_order=lowercase     ,				solver_type=lowercase     ,				prediction_type=lowercase     ,				)
                 assert not torch.isnan(lowercase						).any(), "Samples have nan numbers"





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        self.check_over_configs(lower_order_final=lowercase						)
        self.check_over_configs(lower_order_final=lowercase						)





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=lowercase     ,				time_step=0						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :List[str]         =			self.full_loop()
        lowercase_      :List[str]         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :Dict         =			self.full_loop(prediction_type="v_prediction"						)
        lowercase_      :Any         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.10_14						) < 1e-3





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.scheduler_classes[0]
        lowercase_      :str         =			self.get_scheduler_config(thresholding=lowercase     ,				dynamic_thresholding_ratio=0						)
        lowercase_      :Tuple         =			scheduler_class(**lowercase						)

        lowercase_      :Optional[int]         =			10
        lowercase_      :Optional[int]         =			self.dummy_model()
        lowercase_      :Any         =			self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase						)

        for i, t in enumerate(scheduler.timesteps						):
           lowercase_      :Tuple         =			model(lowercase     ,				lowercase						)
           lowercase_      :Any         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase						).prev_sample

        assert sample.dtype == torch.floataa







     def 							lowercase__				(			self		: List[Any]     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        for scheduler_class in self.scheduler_classes:
           lowercase_      :Optional[int]         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Dict         =			scheduler_class(**lowercase						)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
           assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps


