





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


UpperCAmelCase     :     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     __lowerCAmelCase	(   UpperCamelCase__):







           def __init__(   self		,				lowerCAmelCase__ = 1_0_1							)       ->						Tuple:



             '''simple docstring'''




             a__     : Optional[Any]  =length







           def __len__(   self							)       ->						Union[str, Any]:



             '''simple docstring'''




             return self.length







           def __getitem__(   self		,				lowerCAmelCase__							)       ->						int:



             '''simple docstring'''




             return i







     class     __lowerCAmelCase	:







           def __call__(   self		,				lowerCAmelCase__							)       ->						Union[str, Any]:



             '''simple docstring'''




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







     class     __lowerCAmelCase	(   nn.Module):







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



             '''simple docstring'''




             super().__init__()
             # Add some (unused) params otherwise DDP will complain.
             a__     : int  =nn.Linear(1_2_0		,				8_0							)







           def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=None							)       ->						Optional[int]:



             '''simple docstring'''




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







class     __lowerCAmelCase	(   UpperCamelCase__):







      @require_torch_neuroncore
      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




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







class     __lowerCAmelCase	(   UpperCamelCase__):







      @require_torch_multi_gpu
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : int  =F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
        a__     : Union[str, Any]  =self.get_auto_remove_tmp_dir()
        a__     : List[Any]  =F'''--output_dir {output_dir}'''.split()
        a__     : Optional[int]  =["torchrun"] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__		,				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

     UpperCAmelCase     :     Optional[int]							=	HfArgumentParser((TrainingArguments,))
     UpperCAmelCase     :     int							=	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 [101, 40, 7]:
          UpperCAmelCase     :     int							=	DummyDataset(dataset_length)
          def 					_A	(		SCREAMING_SNAKE_CASE					:       EvalPrediction						):





            """simple docstring"""



            a__     : Dict  =list(range(len(SCREAMING_SNAKE_CASE						)						)						)
            a__     : Tuple  =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}

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

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

          UpperCAmelCase     :     Tuple							=	2

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

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





          UpperCAmelCase     :     Any							=	None













import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Any            =      (PNDMScheduler,)
      _lowercase    : str            =      (("""num_inference_steps""", 50),)







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Dict  ={
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**lowerCAmelCase__							)
        return config







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : List[str]  =0.1 * sample
        a__     : str  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__     : int  =self.get_scheduler_config(**lowerCAmelCase__							)
          a__     : Union[str, Any]  =scheduler_class(**lowerCAmelCase__							)
          scheduler.set_timesteps(lowerCAmelCase__							)
          # copy over dummy past residuals
          a__     : Any  =dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__							)
            a__     : List[Any]  =scheduler_class.from_pretrained(lowerCAmelCase__							)
            new_scheduler.set_timesteps(lowerCAmelCase__							)
            # copy over dummy past residuals
            a__     : str  =dummy_past_residuals[:]

          a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : Optional[int]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Union[str, Any]  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        pass







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : List[str]  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : int  =0.1 * sample
        a__     : Tuple  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__     : Dict  =self.get_scheduler_config()
          a__     : List[str]  =scheduler_class(**lowerCAmelCase__							)
          scheduler.set_timesteps(lowerCAmelCase__							)

          # copy over dummy past residuals (must be after setting timesteps)
          a__     : Dict  =dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__							)
            a__     : Dict  =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__     : Optional[int]  =dummy_past_residuals[:]

          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : List[Any]  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Any  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.scheduler_classes[0]
        a__     : Optional[Any]  =self.get_scheduler_config(**lowerCAmelCase__							)
        a__     : Any  =scheduler_class(**lowerCAmelCase__							)

        a__     : int  =1_0
        a__     : Union[str, Any]  =self.dummy_model()
        a__     : Optional[int]  =self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase__							)

        for i, t in enumerate(scheduler.prk_timesteps							):
          a__     : List[Any]  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps							):
          a__     : int  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : int  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        return sample







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : str  =dict(self.forward_default_kwargs							)

        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)

        for scheduler_class in self.scheduler_classes:
          a__     : Union[str, Any]  =self.get_scheduler_config()
          a__     : List[str]  =scheduler_class(**lowerCAmelCase__							)

          a__     : List[Any]  =self.dummy_sample
          a__     : Dict  =0.1 * sample

          if num_inference_steps is not None and hasattr(lowerCAmelCase__		,				"set_timesteps"							):
            scheduler.set_timesteps(lowerCAmelCase__							)
          elif num_inference_steps is not None and not hasattr(lowerCAmelCase__		,				"set_timesteps"							):
            a__     : int  =num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          a__     : Tuple  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          a__     : str  =dummy_past_residuals[:]

          a__     : List[Any]  =scheduler.step_prk(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : int  =scheduler.step_prk(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

          self.assertEqual(output_a.shape		,				sample.shape							)
          self.assertEqual(output_a.shape		,				output_a.shape							)

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =scheduler.step_plms(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

          self.assertEqual(output_a.shape		,				sample.shape							)
          self.assertEqual(output_a.shape		,				output_a.shape							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=lowerCAmelCase__							)







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



        '''simple docstring'''




        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCAmelCase__							)

        a__     : Optional[Any]  =self.scheduler_classes[0]
        a__     : Tuple  =self.get_scheduler_config(steps_offset=1							)
        a__     : Optional[Any]  =scheduler_class(**lowerCAmelCase__							)
        scheduler.set_timesteps(1_0							)
        assert torch.equal(
            scheduler.timesteps		,				torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]							)		,				)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for beta_start, beta_end in zip([0.00_01, 0.0_01]		,				[0.0_02, 0.02]							):
          self.check_over_configs(beta_start=lowerCAmelCase__		,				beta_end=lowerCAmelCase__							)







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



        '''simple docstring'''




        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCAmelCase__							)







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



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        for t, num_inference_steps in zip([1, 5, 1_0]		,				[1_0, 5_0, 1_0_0]							):
          self.check_over_forward(num_inference_steps=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Dict  =2_7

        for scheduler_class in self.scheduler_classes:
          a__     : Tuple  =self.dummy_sample
          a__     : Dict  =0.1 * sample

          a__     : Dict  =self.get_scheduler_config()
          a__     : int  =scheduler_class(**lowerCAmelCase__							)

          scheduler.set_timesteps(lowerCAmelCase__							)

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]							):
            a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample







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



        '''simple docstring'''




        with self.assertRaises(lowerCAmelCase__							):
          a__     : List[Any]  =self.scheduler_classes[0]
          a__     : Dict  =self.get_scheduler_config()
          a__     : Tuple  =scheduler_class(**lowerCAmelCase__							)

          scheduler.step_plms(self.dummy_sample		,				1		,				self.dummy_sample							).prev_sample







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



        '''simple docstring'''




        a__     : List[Any]  =self.full_loop()
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_98.13_18							) < 1E-2
        assert abs(result_mean.item() - 0.25_80							) < 1E-3







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =self.full_loop(prediction_type="v_prediction"							)
        a__     : int  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[int]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 67.39_86							) < 1E-2
        assert abs(result_mean.item() - 0.08_78							) < 1E-3







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



        '''simple docstring'''




        a__     : Tuple  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Dict  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 2_30.03_99							) < 1E-2
        assert abs(result_mean.item() - 0.29_95							) < 1E-3







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Dict  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : Union[str, Any]  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Union[str, Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_86.94_82							) < 1E-2
        assert abs(result_mean.item() - 0.24_34							) < 1E-3






