




'''simple docstring'''



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCamelCase_    	=				"true"




def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Any=82      ,__UpperCamelCase:					Union[str, Any]=16    ):

      """simple docstring"""






      set_seed(42    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						RegressionModel()
      SCREAMING_SNAKE_CASE   :      List[str]    					=						deepcopy(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						RegressionDataset(length=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						DataLoader(__UpperCamelCase      ,batch_size=__UpperCamelCase    )
      model.to(accelerator.device    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						accelerator.prepare(__UpperCamelCase      ,__UpperCamelCase    )
      return model, ddp_model, dataloader




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					Tuple=False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						load_dataset('glue'      ,'mrpc'      ,split='validation'    )

      def tokenize_function(__UpperCamelCase:					str    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer(examples['sentence1']      ,examples['sentence2']      ,truncation=__UpperCamelCase      ,max_length=__UpperCamelCase    )
            return outputs

      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						dataset.map(
                __UpperCamelCase      ,batched=__UpperCamelCase      ,remove_columns=['idx', 'sentence1', 'sentence2']      ,)

      SCREAMING_SNAKE_CASE   :      Any    					=						tokenized_datasets.rename_column('label'      ,'labels'    )

      def collate_fn(__UpperCamelCase:					List[Any]    ):
            if use_longest:
                  return tokenizer.pad(__UpperCamelCase      ,padding='longest'      ,return_tensors='pt'    )
            return tokenizer.pad(__UpperCamelCase      ,padding='max_length'      ,max_length=1_28      ,return_tensors='pt'    )

      return DataLoader(__UpperCamelCase      ,shuffle=__UpperCamelCase      ,collate_fn=__UpperCamelCase      ,batch_size=16    )




def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						Accelerator(dispatch_batches=__UpperCamelCase      ,split_batches=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						get_dataloader(__UpperCamelCase      ,not dispatch_batches    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoModelForSequenceClassification.from_pretrained(
          'hf-internal-testing/mrpc-bert-base-cased'      ,return_dict=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						accelerator.prepare(__UpperCamelCase      ,__UpperCamelCase    )
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




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

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
      for batch in dataloader:
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						batch.values()
            with torch.no_grad():
                  SCREAMING_SNAKE_CASE   :      int    					=						model(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						accelerator.gather_for_metrics((logit, target)    )
                  logits_and_targets.append((logit, target)    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[], []
      for logit, targ in logits_and_targets:
            logits.append(__UpperCamelCase    )
            targs.append(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Any    					=						torch.cat(__UpperCamelCase    ), torch.cat(__UpperCamelCase    )
      return logits, targs




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					int=82      ,__UpperCamelCase:					List[Any]=False      ,__UpperCamelCase:					Optional[Any]=False      ,__UpperCamelCase:					str=16    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						get_basic_setup(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						generate_predictions(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      assert (
          len(__UpperCamelCase    ) == num_samples
      ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCamelCase    )}"




def   lowercase__(			__UpperCamelCase:					bool = False      ,__UpperCamelCase:					bool = False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						evaluate.load('glue'      ,'mrpc'    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						get_mrpc_setup(__UpperCamelCase      ,__UpperCamelCase    )
      # First do baseline
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						setup['no']
      model.to(__UpperCamelCase    )
      model.eval()
      for batch in dataloader:
            batch.to(__UpperCamelCase    )
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						outputs.logits.argmax(dim=-1    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=batch['labels']    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						metric.compute()

      # Then do distributed
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						setup['ddp']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      str    					=						outputs.logits.argmax(dim=-1    )
            SCREAMING_SNAKE_CASE   :      Tuple    					=						batch['labels']
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						accelerator.gather_for_metrics((preds, references)    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key]      ,distributed[key]    ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print('**Testing gather_for_metrics**'    )
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"    )
                        test_mrpc(__UpperCamelCase      ,__UpperCamelCase    )
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test torch metrics**'    )
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
                  if accelerator.is_local_main_process:
                        print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"    )
                  test_torch_metrics(__UpperCamelCase      ,99    )
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test last batch is not dropped when perfectly divisible**'    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Accelerator()
      test_torch_metrics(__UpperCamelCase      ,5_12    )
      accelerator.state._reset_state()




def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      main()


if __name__ == "__main__":
      main()




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DPMSolverSDEScheduler,)
       __lowerCamelCase				       =						1_0


       def 	_snake_case				(    self						,       **__A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''noise_sampler_seed''': 0,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self				)						->    Any:
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				int		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Tuple		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				str		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.47_821_044_921_875				) < 1e-2
                     assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_352_111_816_406				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_scheduler_config(prediction_type='''v_prediction'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				Tuple		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 124.77_149_200_439_453				) < 1e-2
                     assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 128.1_663_360_595_703				) < 1e-2
                     assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1e-3
              else:
                     assert abs(result_sum.item() - 119.8_487_548_828_125				) < 1e-2
                     assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1e-3


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.46_957_397_460_938				) < 1e-2
                     assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_353_637_695_312				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A						,       use_karras_sigmas=__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				str		=sample.to(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				int		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 176.66_974_135_742_188				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 177.63_653_564_453_125				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              else:
                     assert abs(result_sum.item() - 170.3_135_223_388_672				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
