
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


snake_case 				=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
snake_case 				=       """    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"""





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Any  )						->					Optional[int]:
         _lowercase							= tempfile.mkdtemp()
         os.makedirs(os.path.join(self.diffusers_dir						,'schedulers/'  )  )
         _lowercase							= self.diffusers_dir
         shutil.copy(
             os.path.join(_A						,'src/diffusers/schedulers/scheduling_ddpm.py'  )						,os.path.join(self.diffusers_dir						,'schedulers/scheduling_ddpm.py'  )						,)







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Union[str, Any]:
         _lowercase							= 'src/diffusers'
         shutil.rmtree(self.diffusers_dir  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Tuple						,__A	:	Optional[Any]						,__A	:	Dict						,__A	:	List[str]=None  )						->					Union[str, Any]:
         _lowercase							= comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
         if overwrite_result is not None:
           _lowercase							= comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
         _lowercase							= black.Mode(target_versions={black.TargetVersion.PYaa}						,line_length=119  )
         _lowercase							= black.format_str(_A						,mode=_A  )
         _lowercase							= os.path.join(self.diffusers_dir						,'new_code.py'  )
         with open(_A						,'w'						,newline='\n'  ) as f:
           f.write(_A  )
         if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(_A  )  ) == 0  )
         else:
           check_copies.is_copy_consistent(f.name						,overwrite=_A  )
           with open(_A						,'r'  ) as f:
             self.assertTrue(f.read()						,_A  )







       def    __UpperCAmelCase (    self	:	int  )						->					List[Any]:
         _lowercase							= check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'  )
         self.assertEqual(_A						,_A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Optional[int]:
         # Base copy consistency
         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'						,'DDPMSchedulerOutput'						,REFERENCE_CODE + '\n'						,)

         # With no empty line at the end
         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'						,'DDPMSchedulerOutput'						,_A						,)

         # Copy consistency with rename
         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'						,'TestSchedulerOutput'						,re.sub('DDPM'						,'Test'						,_A  )						,)

         # Copy consistency with a really long name
         _lowercase							= 'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
         self.check_copy_consistency(
             F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""						,F"""{long_class_name}SchedulerOutput"""						,re.sub('Bert'						,_A						,_A  )						,)

         # Copy consistency with overwrite
         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'						,'TestSchedulerOutput'						,_A						,overwrite_result=re.sub('DDPM'						,'Test'						,_A  )						,)






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


snake_case 				=       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 				A_				(      UpperCAmelCase	):







             """simple docstring"""





             def __init__(    self	:	str						,__A	:	int = 101  )						->					Optional[int]:
               _lowercase							= length







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







             def __getitem__(    self	:	List[Any]						,__A	:	Optional[int]  )						->					int:
               return i





      class 				A_				:







             """simple docstring"""





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





      class 				A_				(      nn.Module	):







             """simple docstring"""





             def __init__(    self	:	Dict  )						->					Any:
               super().__init__()
               # Add some (unused) params otherwise DDP will complain.
               _lowercase							= nn.Linear(120						,80  )







             def    __UpperCAmelCase (    self	:	Dict						,__A	:	Dict						,__A	:	Any=None  )						->					Tuple:
               if labels is not None:
                 return torch.tensor(0.0						,device=input_ids.device  ), input_ids
               else:
                 return input_ids





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_neuroncore
       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,env=self.get_env()  )
         # successful return here == success - any errors would have caused an error in the sub-call





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_multi_gpu
       def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
         _lowercase							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,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

      snake_case 				=       HfArgumentParser((TrainingArguments,))
      snake_case 				=       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]:
            snake_case 				=       DummyDataset(dataset_length)

            def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :EvalPrediction     )      ->						Dict:
              _lowercase							= list(range(len(snake_case__     )     )     )
              _lowercase							= 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}

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

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

            snake_case 				=       2

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

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







            snake_case 				=       None