


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											=       16
lowerCamelCase											=       32




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Accelerator		,			SCREAMING_SNAKE_CASE__					:      int = 16     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=AutoTokenizer.from_pretrained('bert-base-cased'     )
       _lowerCamelCase       :		Any		=load_dataset('glue'		,			'mrpc'     )

       def tokenize_function(SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCamelCase       :		int		=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
       # starting with the main process first:
       with accelerator.main_process_first():
              _lowerCamelCase       :		Tuple		=datasets.map(
                  SCREAMING_SNAKE_CASE__		,			batched=SCREAMING_SNAKE_CASE__		,			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
       _lowerCamelCase       :		List[Any]		=tokenized_datasets.rename_column('label'		,			'labels'     )

       def collate_fn(SCREAMING_SNAKE_CASE__					:      List[str]     ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _lowerCamelCase       :		List[str]		=128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     _lowerCamelCase       :		Optional[int]		=16
              elif accelerator.mixed_precision != "no":
                     _lowerCamelCase       :		Optional[int]		=8
              else:
                     _lowerCamelCase       :		Tuple		=None

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

       # Instantiate dataloaders.
       _lowerCamelCase       :		str		=DataLoader(
           tokenized_datasets['train']		,			shuffle=SCREAMING_SNAKE_CASE__		,			collate_fn=SCREAMING_SNAKE_CASE__		,			batch_size=SCREAMING_SNAKE_CASE__		,			drop_last=SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=DataLoader(
           tokenized_datasets['validation']		,			shuffle=SCREAMING_SNAKE_CASE__		,			collate_fn=SCREAMING_SNAKE_CASE__		,			batch_size=SCREAMING_SNAKE_CASE__		,			drop_last=(accelerator.mixed_precision == 'fp8')		,			)

       return train_dataloader, eval_dataloader




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=Accelerator(cpu=args.cpu		,			mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _lowerCamelCase       :		Tuple		=config['lr']
       _lowerCamelCase       :		List[str]		=int(config['num_epochs']     )
       _lowerCamelCase       :		Any		=int(config['seed']     )
       _lowerCamelCase       :		Optional[int]		=int(config['batch_size']     )

       _lowerCamelCase       :		Any		=evaluate.load('glue'		,			'mrpc'     )

       # If the batch size is too big we use gradient accumulation
       _lowerCamelCase       :		str		=1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              _lowerCamelCase       :		Optional[int]		=batch_size // MAX_GPU_BATCH_SIZE
              _lowerCamelCase       :		Union[str, Any]		=MAX_GPU_BATCH_SIZE

       set_seed(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase	,     _lowerCamelCase       :		List[Any]		=get_dataloaders(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _lowerCamelCase       :		List[str]		=AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,			return_dict=SCREAMING_SNAKE_CASE__     )

       # 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).
       _lowerCamelCase       :		Tuple		=model.to(accelerator.device     )
       # Instantiate optimizer
       _lowerCamelCase       :		Any		=AdamW(params=model.parameters()		,			lr=SCREAMING_SNAKE_CASE__     )

       # Instantiate scheduler
       _lowerCamelCase       :		int		=get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE__		,			num_warmup_steps=100		,			num_training_steps=(len(SCREAMING_SNAKE_CASE__     ) * 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.

       _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		Optional[Any]		=accelerator.prepare(
           SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE__     ):
              model.train()
              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     )
                     _lowerCamelCase       :		Dict		=model(**SCREAMING_SNAKE_CASE__     )
                     _lowerCamelCase       :		int		=outputs.loss
                     _lowerCamelCase       :		List[str]		=loss / gradient_accumulation_steps
                     accelerator.backward(SCREAMING_SNAKE_CASE__     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              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():
                            _lowerCamelCase       :		Union[str, Any]		=model(**SCREAMING_SNAKE_CASE__     )
                     _lowerCamelCase       :		List[str]		=outputs.logits.argmax(dim=-1     )
                     _lowerCamelCase	,     _lowerCamelCase       :		Tuple		=accelerator.gather_for_metrics((predictions, batch['labels'])     )
                     metric.add_batch(
                         predictions=SCREAMING_SNAKE_CASE__		,			references=SCREAMING_SNAKE_CASE__		,			)

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




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=argparse.ArgumentParser(description='Simple example of training script.'     )
       parser.add_argument(
           '--mixed_precision'		,			type=SCREAMING_SNAKE_CASE__		,			default=SCREAMING_SNAKE_CASE__		,			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.'     )
       _lowerCamelCase       :		List[str]		=parser.parse_args()
       _lowerCamelCase       :		Optional[Any]		={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Any      =(DPMSolverSinglestepScheduler,)
      UpperCamelCase__							:  Tuple      =(('num_inference_steps', 25),)






      def        lowerCamelCase       (					self						:     Optional[Any]			,   **lowercase_						:     List[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'solver_order': 2,
                 'prediction_type': 'epsilon',
                 'thresholding': False,
                 'sample_max_value': 1.0,
                 'algorithm_type': 'dpmsolver++',
                 'solver_type': 'midpoint',
                 'lambda_min_clipped': -float('inf' ),
                 'variance_type': None,
             }

             config.update(**lowercase_ )
             return config






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Optional[Any]=0			,   **lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Optional[Any]		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Dict		=0.1 * sample
             _lowerCamelCase       :		Optional[int]		=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                    _lowerCamelCase	,     _lowerCamelCase       :		Any		=sample, sample
                    for t in range(lowercase_			,   time_step + scheduler.config.solver_order + 1 ):
                           _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                           _lowerCamelCase       :		Optional[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        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             pass






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=0			,   **lowercase_						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		int		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Dict		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Any		=0.1 * sample
             _lowerCamelCase       :		int		=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_ )
                           _lowerCamelCase       :		Optional[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)
                           _lowerCamelCase       :		str		=dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase       :		Dict		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                    _lowerCamelCase       :		Dict		=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        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]=None			,   **lowercase_						:     List[str] )					->						str:
             """simple docstring"""




             if scheduler is None:
                    _lowerCamelCase       :		Tuple		=self.scheduler_classes[0]
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config(**lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=scheduler_class(**lowercase_ )

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

             _lowerCamelCase       :		str		=10
             _lowerCamelCase       :		Union[str, Any]		=self.dummy_model()
             _lowerCamelCase       :		Dict		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		List[str]		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Optional[int]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             return sample






      def        lowerCamelCase       (					self						:     Dict )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		int		=50
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		Tuple		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             # make sure that the first t is uneven
             for i, t in enumerate(scheduler.timesteps[3:] ):
                    _lowerCamelCase       :		Any		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             _lowerCamelCase       :		int		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2574 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		Optional[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		Tuple		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3

             _lowerCamelCase       :		Dict		=DEISMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Optional[int]		=DPMSolverMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Union[str, Any]		=UniPCMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		List[str]		=DPMSolverSinglestepScheduler.from_config(scheduler.config )

             _lowerCamelCase       :		List[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             self.check_over_configs(thresholding=lowercase_ )
             for order in [1, 2, 3]:
                    for solver_type in ["midpoint", "heun"]:
                           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_			,   algorithm_type='dpmsolver++'			,   solver_order=lowercase_			,   solver_type=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




             for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                    for solver_type in ["midpoint", "heun"]:
                           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_			,   algorithm_type=lowercase_			,   )
                                         _lowerCamelCase       :		Any		=self.full_loop(
                                             solver_order=lowercase_			,   solver_type=lowercase_			,   prediction_type=lowercase_			,   algorithm_type=lowercase_			,   )
                                         assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             self.check_over_configs(lower_order_final=lowercase_ )
             self.check_over_configs(lower_order_final=lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             self.check_over_configs(lambda_min_clipped=-float('inf' ) )
             self.check_over_configs(lambda_min_clipped=-5.1 )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             self.check_over_configs(variance_type=lowercase_ )
             self.check_over_configs(variance_type='learned_range' )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=lowercase_			,   time_step=0 )






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.full_loop()
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2248 ) < 1E-3






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.full_loop(prediction_type='v_prediction' )
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.1453 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(prediction_type='v_prediction'			,   use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.0649 ) < 1E-3






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.scheduler_classes[0]
             _lowerCamelCase       :		List[str]		=self.get_scheduler_config(thresholding=lowercase_			,   dynamic_thresholding_ratio=0 )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

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

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

             assert sample.dtype == torch.floataa


