



import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__lowerCamelCase								=   get_logger(__name__)




def       UpperCAmelCase__	(							__snake_case							)				->	List[str]:
  return TestCommand(
      args.dataset  ,			args.name  ,			args.cache_dir  ,			args.data_dir  ,			args.all_configs  ,			args.save_info or args.save_infos  ,			args.ignore_verifications  ,			args.force_redownload  ,			args.clear_cache  ,			)



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     False  # to tell pytest it's not a test class
 @staticmethod
 def lowercase_						(  a   )		->							int:




   """simple docstring"""







   _A          =      parser.add_parser('''test'''		,      help='''Test dataset implementation.'''   )
   test_parser.add_argument('''--name'''		,      type=a		,      default=a		,      help='''Dataset processing name'''   )
   test_parser.add_argument(
       '''--cache_dir'''		,      type=a		,      default=a		,      help='''Cache directory where the datasets are stored.'''		,      )
   test_parser.add_argument(
       '''--data_dir'''		,      type=a		,      default=a		,      help='''Can be used to specify a manual directory to get the files from.'''		,      )
   test_parser.add_argument('''--all_configs'''		,      action='''store_true'''		,      help='''Test all dataset configurations'''   )
   test_parser.add_argument(
       '''--save_info'''		,      action='''store_true'''		,      help='''Save the dataset infos in the dataset card (README.md)'''   )
   test_parser.add_argument(
       '''--ignore_verifications'''		,      action='''store_true'''		,      help='''Run the test without checksums and splits checks.'''		,      )
   test_parser.add_argument('''--force_redownload'''		,      action='''store_true'''		,      help='''Force dataset redownload'''   )
   test_parser.add_argument(
       '''--clear_cache'''		,      action='''store_true'''		,      help='''Remove downloaded files and cached datasets after each config test'''		,      )
   # aliases
   test_parser.add_argument('''--save_infos'''		,      action='''store_true'''		,      help='''alias to save_info'''   )
   test_parser.add_argument('''dataset'''		,      type=a		,      help='''Name of the dataset to download'''   )
   test_parser.set_defaults(func=_test_command_factory   )
 def __init__(  self		,      a		,      a		,      a		,      a		,      a		,      a		,      a		,      a		,      a		,      )		->							List[Any]:




   """simple docstring"""







   _A          =      dataset
   _A          =      name
   _A          =      cache_dir
   _A          =      data_dir
   _A          =      all_configs
   _A          =      save_infos
   _A          =      ignore_verifications
   _A          =      force_redownload
   _A          =      clear_cache
   if clear_cache and not cache_dir:
     print(
         '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
         '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
         '''Please provide a --cache_dir that will be used to test the dataset script.'''   )
     exit(1   )
   if save_infos:
     _A          =      True



 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   fl_logger().setLevel(a   )
   if self._name is not None and self._all_configs:
     print('''Both parameters `config` and `all_configs` can\'t be used at once.'''   )
     exit(1   )
   _A      ,				_A          =      self._dataset, self._name
   _A          =      dataset_module_factory(a   )
   _A          =      import_main_class(module.module_path   )
   _A          =      len(builder_cls.BUILDER_CONFIGS   ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

   def get_builders() -> Generator[DatasetBuilder, None, None]:
     if self._all_configs and builder_cls.BUILDER_CONFIGS:
       for i, config in enumerate(builder_cls.BUILDER_CONFIGS   ):
         if "config_name" in module.builder_kwargs:
           yield builder_cls(
               cache_dir=self._cache_dir		,      data_dir=self._data_dir		,      **module.builder_kwargs		,      )
         else:
           yield builder_cls(
               config_name=config.name		,      cache_dir=self._cache_dir		,      data_dir=self._data_dir		,      **module.builder_kwargs		,      )
     else:
       if "config_name" in module.builder_kwargs:
         yield builder_cls(cache_dir=self._cache_dir		,      data_dir=self._data_dir		,      **module.builder_kwargs   )
       else:
         yield builder_cls(
             config_name=a		,      cache_dir=self._cache_dir		,      data_dir=self._data_dir		,      **module.builder_kwargs		,      )

   for j, builder in enumerate(get_builders()   ):
     print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''   )
     _A          =      os.path.exists(
         os.path.join(builder.get_imported_module_dir()		,      datasets.config.DATASETDICT_INFOS_FILENAME   )   )  # record checksums only if we need to update a (deprecated) dataset_infos.json
     builder.download_and_prepare(
         download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
         if not self._force_redownload
         else DownloadMode.FORCE_REDOWNLOAD		,      verification_mode=VerificationMode.NO_CHECKS
         if self._ignore_verifications
         else VerificationMode.ALL_CHECKS		,      try_from_hf_gcs=a		,      )
     builder.as_dataset()
     if self._save_infos:
       builder._save_infos()

     # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
     # The dataset_infos are saved in the YAML part of the README.md

     # Let's move it to the original directory of the dataset script, to allow the user to
     # upload them on S3 at the same time afterwards.
     if self._save_infos:
       _A          =      os.path.join(builder_cls.get_imported_module_dir()		,      '''README.md'''   )
       _A          =      Path(a   ).name + '''.py'''
       _A          =      os.path.join(a		,      a   )
       if os.path.isfile(a   ):
         _A          =      os.path.dirname(a   )
       elif os.path.isfile(a   ):
         _A          =      path
       elif os.path.isdir(a   ):  # for local directories containing only data files
         _A          =      path
       else:  # in case of a remote dataset
         _A          =      None
         print(f'''Dataset card saved at {dataset_readme_path}'''   )

       # Move dataset_info back to the user
       if dataset_dir is not None:
         _A          =      os.path.join(a		,      '''README.md'''   )
         copyfile(a		,      a   )
         print(f'''Dataset card saved at {user_dataset_readme_path}'''   )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
     if self._clear_cache:
       if os.path.isdir(builder._cache_dir   ):
         logger.warning(f'''Clearing cache at {builder._cache_dir}'''   )
         rmtree(builder._cache_dir   )
       _A          =      os.path.join(self._cache_dir		,      datasets.config.DOWNLOADED_DATASETS_DIR   )
       if os.path.isdir(a   ):
         logger.warning(f'''Clearing cache at {download_dir}'''   )
         rmtree(a   )

   print('''Test successful.'''   )




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     (EulerAncestralDiscreteScheduler,)
 lowerCamelCase_       =     1_0
 def lowercase_						(  self		,      **a   )		->							Tuple:




   """simple docstring"""







   _A          =      {
       '''num_train_timesteps''': 1_1_0_0,
       '''beta_start''': 0.0_001,
       '''beta_end''': 0.02,
       '''beta_schedule''': '''linear''',
   }

   config.update(**a   )
   return config
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=a   )
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   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 lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=a   )
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=a   )
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config(prediction_type='''v_prediction'''   )
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 108.4_439   ) < 1e-2
   assert abs(result_mean.item() - 0.1_412   ) < 1e-3



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




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps		,      device=a   )
   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for t in scheduler.timesteps:
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3