

'''simple docstring'''

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

UpperCAmelCase							=					32

def 			__UpperCamelCase							(		lowercase__  :					Optional[int],   lowercase__  :					Any = 16	):






     '''simple docstring'''





     __lowercase     						=AutoTokenizer.from_pretrained('bert-base-cased'	)
     __lowercase     						=load_dataset('glue',   'mrpc'	)

     def tokenize_function(lowercase__  :					str	):
          # max_length=None => use the model max length (it's actually the default)
          __lowercase     						=tokenizer(examples['sentence1'],   examples['sentence2'],   truncation=__UpperCamelCase,   max_length=__UpperCamelCase	)
          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     						=datasets.map(
              __UpperCamelCase,   batched=__UpperCamelCase,   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     						=tokenized_datasets.rename_column('label',   'labels'	)

     def collate_fn(lowercase__  :					List[str]	):
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __lowercase     						=16
          elif accelerator.mixed_precision != "no":
               __lowercase     						=8
          else:
               __lowercase     						=None

          return tokenizer.pad(
              __UpperCamelCase,   padding='longest',   pad_to_multiple_of=__UpperCamelCase,   return_tensors='pt',   )

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

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       UpperCAmelCase							=					mocked_dataloaders  # noqa: F811

def 			__UpperCamelCase							(		lowercase__  :					Tuple,   lowercase__  :					List[str]	):






     '''simple docstring'''





     if os.environ.get('TESTING_MOCKED_DATALOADERS',   __UpperCamelCase	) == "1":
          __lowercase     						=2
     # Initialize accelerator
     __lowercase     						=Accelerator(cpu=args.cpu,   mixed_precision=args.mixed_precision	)
     # 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     						=evaluate.load('glue',   'mrpc'	)

     # New Code #
     # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
     # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
     # half each time. From this, we can calculate the number of gradient accumulation steps needed
     # and modify the Accelerator object as a result
     @find_executable_batch_size(starting_batch_size=int(__UpperCamelCase	)	)
     def inner_training_loop(lowercase__  :					Optional[int]	):
          # Since we need to modify the outside accelerator object, we need to bring it
          # to the local scope
          nonlocal accelerator

          # We can calculate the number of gradient accumulation steps based on the current
          # batch size vs the starting batch size
          __lowercase     						=observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          __lowercase     						=num_gradient_accumulation_steps

          # Next we need to free all of the stored model references in the Accelerator each time
          accelerator.free_memory()

          # And set the seed so our results are reproducable each reset
          set_seed(__UpperCamelCase	)

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          __lowercase     						=AutoModelForSequenceClassification.from_pretrained('bert-base-cased',   return_dict=__UpperCamelCase	)

          # 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     						=model.to(accelerator.device	)

          # Instantiate optimizer
          __lowercase     						=AdamW(params=model.parameters(),   lr=__UpperCamelCase	)
          __lowercase						,       __lowercase     						=get_dataloaders(__UpperCamelCase,   __UpperCamelCase	)

          # Instantiate scheduler
          __lowercase     						=get_linear_schedule_with_warmup(
              optimizer=__UpperCamelCase,   num_warmup_steps=1_00,   num_training_steps=(len(__UpperCamelCase	) * num_epochs),   )

          # 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(
              __UpperCamelCase,   __UpperCamelCase,   __UpperCamelCase,   __UpperCamelCase,   __UpperCamelCase	)

          # Now we train the model
          for epoch in range(__UpperCamelCase	):
               model.train()
               for step, batch in enumerate(__UpperCamelCase	):
                    # And perform gradient accumulation
                    with accelerator.accumulate(__UpperCamelCase	):
                         # We could avoid this line since we set the accelerator with `device_placement=True`.
                         batch.to(accelerator.device	)
                         __lowercase     						=model(**__UpperCamelCase	)
                         __lowercase     						=outputs.loss
                         accelerator.backward(__UpperCamelCase	)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

               model.eval()
               for step, batch in enumerate(__UpperCamelCase	):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device	)
                    with torch.no_grad():
                         __lowercase     						=model(**__UpperCamelCase	)
                    __lowercase     						=outputs.logits.argmax(dim=-1	)
                    __lowercase						,       __lowercase     						=accelerator.gather_for_metrics((predictions, batch['labels'])	)
                    metric.add_batch(
                        predictions=__UpperCamelCase,   references=__UpperCamelCase,   )

               __lowercase     						=metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'''epoch {epoch}:''',   __UpperCamelCase	)

          # New Code #
          # And call it at the end with no arguments
          # Note: You could also refactor this outside of your training loop function
     inner_training_loop()

def 			__UpperCamelCase							(		):






     '''simple docstring'''





     __lowercase     						=argparse.ArgumentParser(description='Simple example of training script.'	)
     parser.add_argument(
         '--mixed_precision',   type=__UpperCamelCase,   default=__UpperCamelCase,   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     						=parser.parse_args()
     # New Code #
     # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
     __lowercase     						={'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
     training_function(__UpperCamelCase,   __UpperCamelCase	)


if __name__ == "__main__":
       main()





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







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

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

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

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """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      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[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 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

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

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

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

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


