
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCamelCase__     :		Tuple        = logging.get_logger(__name__)


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset

     from transformers import Trainer



     class    _snake_case (						UpperCAmelCase_				):


      def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = 1_01):


        '''simple docstring'''




        lowercase__	: Dict     		=   length


      def __len__(       self):


        '''simple docstring'''




        return self.length



      def __getitem__(       self       ,	SCREAMING_SNAKE_CASE_):


        '''simple docstring'''




        return i



     class    _snake_case :


      def __call__(       self       ,	SCREAMING_SNAKE_CASE_):


        '''simple docstring'''




        return {"input_ids": torch.tensor(SCREAMING_SNAKE_CASE_), "labels": torch.tensor(SCREAMING_SNAKE_CASE_)}



     class    _snake_case (						nn.Module				):


      def __init__(       self):


        '''simple docstring'''




        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        lowercase__	: Optional[Any]     		=   nn.Linear(1_20       ,	80)



      def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None):


        '''simple docstring'''




        if labels is not None:
          return torch.tensor(0.0       ,	device=input_ids.device), input_ids
        else:
          return input_ids



class    _snake_case (						UpperCAmelCase_				):


 @require_torch_neuroncore
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   f'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
   lowercase__	: Any     		=   self.get_auto_remove_tmp_dir()
   lowercase__	: Optional[int]     		=   f'--output_dir {output_dir}'.split()
   lowercase__	: Union[str, Any]     		=   ["""torchrun"""] + distributed_args + args
   execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=self.get_env())
   # successful return here == success - any errors would have caused an error in the sub-call



class    _snake_case (						UpperCAmelCase_				):


 @require_torch_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   f'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
   lowercase__	: List[str]     		=   self.get_auto_remove_tmp_dir()
   lowercase__	: Dict     		=   f'--output_dir {output_dir}'.split()
   lowercase__	: List[str]     		=   ["""torchrun"""] + distributed_args + args
   execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=self.get_env())
   # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
     # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
     #
     # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

     lowerCamelCase__     :		List[Any]        = HfArgumentParser((TrainingArguments,))
     lowerCamelCase__     :		Optional[Any]        = parser.parse_args_into_dataclasses()[0]

     logger.warning(
         f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
         f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
     )

     # Essentially, what we want to verify in the distributed case is that we get all samples back,
     # in the right order. (this is crucial for prediction for instance)
     for dataset_length in [1_0_1, 4_0, 7]:
          lowerCamelCase__     :		Tuple        = DummyDataset(dataset_length)

          def 		UpperCamelCase						(   lowercase_ )		->						Dict:


            '''simple docstring'''






            lowercase__	: Dict     		=   list(range(len(lowercase_ ) ) )
            lowercase__	: Union[str, Any]     		=   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
            if not success and training_args.local_rank == 0:
              logger.warning(
                  """Predictions and/or labels do not match expected results:\n  - predictions: """
                  F'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}' )
            return {"success": success}

          lowerCamelCase__     :		Optional[int]        = Trainer(
              model=DummyModel(),
              args=training_args,
              data_collator=DummyDataCollator(),
              eval_dataset=dataset,
              compute_metrics=compute_metrics,
          )
          lowerCamelCase__     :		List[Any]        = trainer.evaluate()
          logger.info(metrics)
          if metrics["eval_success"] is not True:
               logger.error(metrics)
               exit(1)

          lowerCamelCase__     :		str        = trainer.predict(dataset)
          logger.info(p.metrics)
          if p.metrics["test_success"] is not True:
               logger.error(p.metrics)
               exit(1)

          lowerCamelCase__     :		str        = 2

          lowerCamelCase__     :		str        = trainer.evaluate()
          logger.info(metrics)
          if metrics["eval_success"] is not True:
               logger.error(metrics)
               exit(1)

          lowerCamelCase__     :		Any        = trainer.predict(dataset)
          logger.info(p.metrics)
          if p.metrics["test_success"] is not True:
               logger.error(p.metrics)
               exit(1)



          lowerCamelCase__     :		List[Any]        = None










"""simple docstring"""



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    (KDPMaDiscreteScheduler,)
   __UpperCAmelCase					:			List[Any]				    =    10







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : List[str]    =       {
           "num_train_timesteps": 1100,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**UpperCamelCase__		)
       return config







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,      [0.0002, 0.002, 0.02]		):
           self.check_over_configs(beta_start=UpperCamelCase__		,      beta_end=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : str    =       self.scheduler_classes[0]
       snake_case       : str    =       self.get_scheduler_config(prediction_type="v_prediction"		)
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : List[str]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : Optional[Any]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : int    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : int    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Optional[Any]    =       output.prev_sample

       snake_case       : Optional[Any]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Any    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.6934e-07		) < 1e-2
           assert abs(result_mean.item() - 6.1112e-10		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 4.693_4286_5017_0972e-07		) < 1e-2
           assert abs(result_mean.item() - 0.0002		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Optional[Any]    =       self.scheduler_classes[0]
       snake_case       : Optional[int]    =       self.get_scheduler_config()
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : Union[str, Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : List[str]    =       sample.to(UpperCamelCase__		)

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

           snake_case       : Optional[int]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : int    =       output.prev_sample

       snake_case       : int    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Union[str, Any]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : Any    =       self.get_scheduler_config()
       snake_case       : Union[str, Any]    =       scheduler_class(**UpperCamelCase__		)

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

       snake_case       : Optional[Any]    =       self.dummy_model()
       snake_case       : Optional[int]    =       self.dummy_sample_deter.to(UpperCamelCase__		) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
           snake_case       : Optional[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Any    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : List[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Dict    =       output.prev_sample

       snake_case       : Dict    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : str    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if str(UpperCamelCase__		).startswith("cpu"		):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3





