


'''simple docstring'''






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


lowercase							=get_logger(__name__)





def lowerCamelCase__			(						__lowerCamelCase       :							Tuple							):







   '''simple docstring'''




   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 					__magic_name__							(		_lowerCAmelCase				):
       UpperCAmelCase						      =False  # to tell pytest it's not a test class






       @staticmethod
       def 			lowerCAmelCase		(			snake_case)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase   :   Optional[Any]							    =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  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							)						->  str:




          '''simple docstring'''
          _UpperCAmelCase   :   int							    =dataset
          _UpperCAmelCase   :   Dict							    =name
          _UpperCAmelCase   :   List[Any]							    =cache_dir
          _UpperCAmelCase   :   int							    =data_dir
          _UpperCAmelCase   :   Tuple							    =all_configs
          _UpperCAmelCase   :   Dict							    =save_infos
          _UpperCAmelCase   :   Dict							    =ignore_verifications
          _UpperCAmelCase   :   List[str]							    =force_redownload
          _UpperCAmelCase   :   Tuple							    =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:
             _UpperCAmelCase   :   List[str]							    =True






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




          '''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)
          _UpperCAmelCase    ,       _UpperCAmelCase   :   Optional[int]							    =self._dataset, self._name
          _UpperCAmelCase   :   Optional[int]							    =dataset_module_factory(A__)
          _UpperCAmelCase   :   Dict							    =import_main_class(module.module_path)
          _UpperCAmelCase   :   Union[str, Any]							    =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})")
             _UpperCAmelCase   :   Dict							    =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:
                _UpperCAmelCase   :   Optional[int]							    =os.path.join(builder_cls.get_imported_module_dir()  ,							'README.md')
                _UpperCAmelCase   :   Optional[int]							    =Path(A__).name + '.py'
                _UpperCAmelCase   :   List[Any]							    =os.path.join(A__  ,							A__)
                if os.path.isfile(A__):
                   _UpperCAmelCase   :   Optional[Any]							    =os.path.dirname(A__)
                elif os.path.isfile(A__):
                   _UpperCAmelCase   :   Tuple							    =path
                elif os.path.isdir(A__):  # for local directories containing only data files
                   _UpperCAmelCase   :   List[Any]							    =path
                else:  # in case of a remote dataset
                   _UpperCAmelCase   :   List[str]							    =None
                   print(f"Dataset card saved at {dataset_readme_path}")

                # Move dataset_info back to the user
                if dataset_dir is not None:
                   _UpperCAmelCase   :   int							    =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)
                _UpperCAmelCase   :   Tuple							    =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.')







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

           # set step values
           self.scheduler.set_timesteps(A__	)

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 lowerCamelCase__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = image.cpu().permute(0		,     2		,     3		,     1	).numpy()
           if output_type == "pil":
                 lowerCamelCase__					     = self.numpy_to_pil(A__	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)