


'''simple docstring'''







# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_SCREAMING_SNAKE_CASE       :   Dict        = 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    (				snake_case_		):






        '''simple docstring'''







        def __init__(  self:	Optional[Any]      ,							__UpperCamelCase:	int = 101     )							->	Tuple:
            __magic_name__				:					int						       =       length







        def __len__(  self:	List[Any]     )							->	Dict:
            return self.length







        def __getitem__(  self:	Tuple      ,							__UpperCamelCase:	Tuple     )							->	int:
            return i






  class 		_snake_case    :






        '''simple docstring'''







        def __call__(  self:	List[str]      ,							__UpperCamelCase:	Any     )							->	List[str]:
            return {"input_ids": torch.tensor(__UpperCamelCase     ), "labels": torch.tensor(__UpperCamelCase     )}






  class 		_snake_case    (				nn.Module		):






        '''simple docstring'''







        def __init__(  self:	str     )							->	Optional[Any]:
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            __magic_name__				:					str						       =       nn.Linear(120      ,							80     )







        def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	Any      ,							__UpperCamelCase:	Any=None     )							->	Tuple:







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



def 						_UpperCamelCase							( ):






    """simple docstring"""



    __magic_name__				:					Any						       =       HfArgumentParser((TrainingArguments,)		)
    sys.argv += ["--output_dir", "./examples"]
    __magic_name__				:					List[Any]						       =       parser.parse_args_into_dataclasses()[0]

    logger.warning(
        F"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
        F"""tpu_num_cores: {training_args.tpu_num_cores}"""     ,   )

    # 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 [1001, 256, 15]:
        __magic_name__				:					int						       =       DummyDataset(__snake_case		)

        def compute_metrics(UpperCamelCase__		) -> Dict:
            __magic_name__				:					Any						       =       list(range(len(__snake_case		)		)		)
            __magic_name__				:					List[Any]						       =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
            return {"success": success}

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

        __magic_name__				:					int						       =       trainer.predict(__snake_case		)
        logger.info(p.metrics		)
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics		)
            exit(1		)

        __magic_name__				:					int						       =       2

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

        __magic_name__				:					Tuple						       =       trainer.predict(__snake_case		)
        logger.info(p.metrics		)
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics		)
            exit(1		)

        __magic_name__				:					int						       =       None

    logger.info("🔥 All distributed tests successful"		)



def 						_UpperCamelCase							( UpperCamelCase__		):






    """simple docstring"""



    main()


if __name__ == "__main__":
  main()



'''simple docstring'''







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      (LMSDiscreteScheduler,)
						__snake_case   =      1_0







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							**__UpperCamelCase:	Any     )							->	List[str]:
										__magic_name__				:					List[str]						       =       {
										    "num_train_timesteps": 1100,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__UpperCamelCase     )
										return config







						def lowerCAmelCase__ (  self:	Any     )							->	str:
										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	int:
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]      ,							[0.0_0_0_2, 0.0_0_2, 0.0_2]     ):
														self.check_over_configs(beta_start=__UpperCamelCase      ,							beta_end=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	Any:
										for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Dict     )							->	int:
										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[Any]:
										for t in [0, 500, 800]:
														self.check_over_forward(time_step=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	List[str]     )							->	Optional[int]:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					List[str]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					str						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Dict						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Optional[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Tuple						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Union[str, Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	int     )							->	Tuple:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					Tuple						       =       self.get_scheduler_config(prediction_type="v_prediction"     )
										__magic_name__				:					List[Any]						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					Tuple						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					int						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Dict						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Optional[Any]						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					List[Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 0.0_0_1_7     ) < 1E-2
										assert abs(result_mean.item() - 2.2_676E-06     ) < 1E-3







						def lowerCAmelCase__ (  self:	Union[str, Any]     )							->	str:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					List[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Any						       =       scheduler_class(**__UpperCamelCase     )

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

										__magic_name__				:					Optional[Any]						       =       self.dummy_model()
										__magic_name__				:					Union[str, Any]						       =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										__magic_name__				:					int						       =       sample.to(__UpperCamelCase     )

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Optional[int]						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					List[Any]						       =       output.prev_sample

										__magic_name__				:					Any						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					int						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					Optional[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase      ,							use_karras_sigmas=__UpperCamelCase     )

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

										__magic_name__				:					int						       =       self.dummy_model()
										__magic_name__				:					Optional[int]						       =       self.dummy_sample_deter.to(__UpperCamelCase     ) * scheduler.init_noise_sigma
										__magic_name__				:					List[str]						       =       sample.to(__UpperCamelCase     )

										for t in scheduler.timesteps:
														__magic_name__				:					str						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					List[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					str						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					int						       =       output.prev_sample

										__magic_name__				:					str						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Dict						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 3_8_1_2.9_9_2_7     ) < 2E-2
										assert abs(result_mean.item() - 4.9_6_4_8     ) < 1E-3