

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


_SCREAMING_SNAKE_CASE     :  Union[str, Any]   			=			16
_SCREAMING_SNAKE_CASE     :  Union[str, Any]   			=			32
def 				UpperCAmelCase_   (						_A      ,		_A = 16      ,		_A = "bert-base-cased"				):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__    =    AutoTokenizer.from_pretrained(UpperCamelCase__				)
     SCREAMING_SNAKE_CASE__    =    load_dataset('''glue'''      ,		'''mrpc'''				)

     def tokenize_function(_A				):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE__    =    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
     SCREAMING_SNAKE_CASE__    =    datasets.map(
         UpperCamelCase__      ,		batched=UpperCamelCase__      ,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']      ,		load_from_cache_file=UpperCamelCase__				)

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

     def collate_fn(_A				):
          # 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(UpperCamelCase__      ,		padding='''max_length'''      ,		max_length=1_28      ,		return_tensors='''pt'''				)
          return tokenizer.pad(UpperCamelCase__      ,		padding='''longest'''      ,		return_tensors='''pt'''				)

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

     return train_dataloader, eval_dataloader
def 				UpperCAmelCase_   (						_A      ,		_A				):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__    =    Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE__    =    config['''lr''']
     SCREAMING_SNAKE_CASE__    =    int(config['''num_epochs''']				)
     SCREAMING_SNAKE_CASE__    =    int(config['''seed''']				)
     SCREAMING_SNAKE_CASE__    =    int(config['''batch_size''']				)
     SCREAMING_SNAKE_CASE__    =    args.model_name_or_path

     set_seed(UpperCamelCase__				)
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    get_dataloaders(UpperCamelCase__      ,		UpperCamelCase__      ,		UpperCamelCase__				)

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

     # Instantiate optimizer
     SCREAMING_SNAKE_CASE__    =    (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     SCREAMING_SNAKE_CASE__    =    optimizer_cls(params=model.parameters()      ,		lr=UpperCamelCase__				)

     if accelerator.state.deepspeed_plugin is not None:
          SCREAMING_SNAKE_CASE__    =    accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          SCREAMING_SNAKE_CASE__    =    1
     SCREAMING_SNAKE_CASE__    =    (len(UpperCamelCase__				) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          SCREAMING_SNAKE_CASE__    =    get_linear_schedule_with_warmup(
              optimizer=UpperCamelCase__      ,		num_warmup_steps=0      ,		num_training_steps=UpperCamelCase__      ,		)
     else:
          SCREAMING_SNAKE_CASE__    =    DummyScheduler(UpperCamelCase__      ,		total_num_steps=UpperCamelCase__      ,		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.
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    accelerator.prepare(
         UpperCamelCase__      ,		UpperCamelCase__      ,		UpperCamelCase__      ,		UpperCamelCase__      ,		UpperCamelCase__				)

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

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

               overall_step += 1

          model.eval()
          SCREAMING_SNAKE_CASE__    =    0
          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():
                    SCREAMING_SNAKE_CASE__    =    model(**UpperCamelCase__				)
               SCREAMING_SNAKE_CASE__    =    outputs.logits.argmax(dim=-1				)
               # It is slightly faster to call this once, than multiple times
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    accelerator.gather(
                   (predictions, batch['''labels'''])				)  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(UpperCamelCase__				) - 1:
                         SCREAMING_SNAKE_CASE__    =    predictions[: len(eval_dataloader.dataset				) - samples_seen]
                         SCREAMING_SNAKE_CASE__    =    references[: len(eval_dataloader.dataset				) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=UpperCamelCase__      ,		references=UpperCamelCase__      ,		)

          SCREAMING_SNAKE_CASE__    =    metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''      ,		UpperCamelCase__				)
          SCREAMING_SNAKE_CASE__    =    eval_metric['''accuracy''']

          if best_performance < eval_metric["accuracy"]:
               SCREAMING_SNAKE_CASE__    =    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(UpperCamelCase__      ,		UpperCamelCase__				)
def 				UpperCAmelCase_   (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__    =    argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
     parser.add_argument(
         '''--model_name_or_path'''      ,		type=UpperCamelCase__      ,		default='''bert-base-cased'''      ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''      ,		required=UpperCamelCase__      ,		)
     parser.add_argument(
         '''--output_dir'''      ,		type=UpperCamelCase__      ,		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=UpperCamelCase__      ,		default=UpperCamelCase__      ,		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=UpperCamelCase__      ,		default=3      ,		help='''Number of train epochs.'''      ,		)
     SCREAMING_SNAKE_CASE__    =    parser.parse_args()
     SCREAMING_SNAKE_CASE__    =    {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
     training_function(UpperCamelCase__      ,		UpperCamelCase__				)


if __name__ == "__main__":
       main()









import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DPMSolverSinglestepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''num_inference_steps''', 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''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(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

               A__					,	A__											=						sample, sample
               for t in range(__lowerCamelCase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
                      A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

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



 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )

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

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

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

               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
               A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

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



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )

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

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						50
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:] ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



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



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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

        A__											=						DEISMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverMultistepScheduler.from_config(scheduler.config )
        A__											=						UniPCMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverSinglestepScheduler.from_config(scheduler.config )

        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        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=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,algorithm_type='''dpmsolver++''',solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,)



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



 def 				UpperCamelCase			(							self ):
        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=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    A__											=						self.full_loop(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowerCamelCase )
        self.check_over_configs(lower_order_final=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(variance_type=__lowerCamelCase )
        self.check_over_configs(variance_type='''learned_range''' )



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''',use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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




 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowerCamelCase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        assert sample.dtype == torch.floataa





