






'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()






class        a__     (      unittest.TestCase    ):
     '''simple docstring'''







     @property
     def      _UpperCamelCase (self     :						Dict       )	->							Optional[int]:
           torch.manual_seed(0       )
           __A		 =   UNetaDModel(
               block_out_channels=(32, 64)					,					layers_per_block=2					,					sample_size=32					,					in_channels=3					,					out_channels=3					,					down_block_types=("DownBlock2D", "AttnDownBlock2D")					,					up_block_types=("AttnUpBlock2D", "UpBlock2D")					,					)
           return model






     def      _UpperCamelCase (self     :						Dict       )	->							Dict:
           __A		 =   "cpu"
           __A		 =   self.dummy_uncond_unet
           __A		 =   DDPMScheduler()

           __A		 =   DDPMPipeline(unet=lowercase__					,					scheduler=lowercase__       )
           ddpm.to(lowercase__       )
           ddpm.set_progress_bar_config(disable=lowercase__       )

           __A		 =   torch.Generator(device=lowercase__       ).manual_seed(0       )
           __A		 =   ddpm(generator=lowercase__					,					num_inference_steps=2					,					output_type="numpy"       ).images

           __A		 =   torch.Generator(device=lowercase__       ).manual_seed(0       )
           __A		 =   ddpm(generator=lowercase__					,					num_inference_steps=2					,					output_type="numpy"					,					return_dict=lowercase__       )[0]

           __A		 =   image[0, -3:, -3:, -1]
           __A		 =   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           __A		 =   np.array(
               [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.0_00, 1.199e-03, 2.648e-04, 5.101e-04]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2






     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							Optional[Any]:
           __A		 =   self.dummy_uncond_unet
           __A		 =   DDPMScheduler(prediction_type="sample"       )

           __A		 =   DDPMPipeline(unet=lowercase__					,					scheduler=lowercase__       )
           ddpm.to(lowercase__       )
           ddpm.set_progress_bar_config(disable=lowercase__       )

           __A		 =   torch.manual_seed(0       )
           __A		 =   ddpm(generator=lowercase__					,					num_inference_steps=2					,					output_type="numpy"       ).images

           __A		 =   torch.manual_seed(0       )
           __A		 =   ddpm(generator=lowercase__					,					num_inference_steps=2					,					output_type="numpy"       )[0]

           __A		 =   image[0, -3:, -3:, -1]
           __A		 =   image_eps[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           __A		 =   1e-2 if torch_device != "mps" else 3e-2
           assert np.abs(image_slice.flatten() - image_eps_slice.flatten()       ).max() < tolerance







@slow
@require_torch_gpu
class        a__     (      unittest.TestCase    ):
     '''simple docstring'''







     def      _UpperCamelCase (self     :						int       )	->							Any:
           __A		 =   "google/ddpm-cifar10-32"

           __A		 =   UNetaDModel.from_pretrained(lowercase__       )
           __A		 =   DDPMScheduler.from_pretrained(lowercase__       )

           __A		 =   DDPMPipeline(unet=lowercase__					,					scheduler=lowercase__       )
           ddpm.to(lowercase__       )
           ddpm.set_progress_bar_config(disable=lowercase__       )

           __A		 =   torch.manual_seed(0       )
           __A		 =   ddpm(generator=lowercase__					,					output_type="numpy"       ).images

           __A		 =   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           __A		 =   np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]       )
           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2










'''simple docstring'''







import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode


def     _UpperCamelCase				(  snake_case__				,      **snake_case__				):
      return RunBeamCommand(
          args.dataset				,      args.name				,      args.cache_dir				,      args.beam_pipeline_options				,      args.data_dir				,      args.all_configs				,      args.save_info or args.save_infos				,      args.ignore_verifications				,      args.force_redownload				,      **snake_case__				,      )






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     @staticmethod
     def      _UpperCamelCase (lowercase__     :						ArgumentParser       )	->							List[Any]:
           __A		 =   parser.add_parser("run_beam"					,					help="Run a Beam dataset processing pipeline"       )
           run_beam_parser.add_argument("dataset"					,					type=lowercase__					,					help="Name of the dataset to download"       )
           run_beam_parser.add_argument("--name"					,					type=lowercase__					,					default=lowercase__					,					help="Dataset config name"       )
           run_beam_parser.add_argument(
               "--cache_dir"					,					type=lowercase__					,					default=lowercase__					,					help="Cache directory where the datasets are stored"					,					)
           run_beam_parser.add_argument(
               "--beam_pipeline_options"					,					type=lowercase__					,					default=""					,					help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"					,					)
           run_beam_parser.add_argument(
               "--data_dir"					,					type=lowercase__					,					default=lowercase__					,					help="Can be used to specify a manual directory to get the files from"					,					)
           run_beam_parser.add_argument("--all_configs"					,					action="store_true"					,					help="Test all dataset configurations"       )
           run_beam_parser.add_argument("--save_info"					,					action="store_true"					,					help="Save the dataset infos file"       )
           run_beam_parser.add_argument(
               "--ignore_verifications"					,					action="store_true"					,					help="Run the test without checksums and splits checks"       )
           run_beam_parser.add_argument("--force_redownload"					,					action="store_true"					,					help="Force dataset redownload"       )
           # aliases
           run_beam_parser.add_argument("--save_infos"					,					action="store_true"					,					help="alias for save_info"       )
           run_beam_parser.set_defaults(func=lowercase__       )






     def __init__(self     :						Any					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						bool					,					lowercase__     :						bool					,					lowercase__     :						bool					,					lowercase__     :						bool					,					**lowercase__     :						Tuple					,					)	->							List[Any]:
           __A		 =   dataset
           __A		 =   name
           __A		 =   cache_dir
           __A		 =   beam_pipeline_options
           __A		 =   data_dir
           __A		 =   all_configs
           __A		 =   save_infos
           __A		 =   ignore_verifications
           __A		 =   force_redownload
           __A		 =   config_kwargs






     def      _UpperCamelCase (self     :						int       )	->							Dict:
           import apache_beam as beam

           if self._name is not None and self._all_configs:
                 print("Both parameters `name` and `all_configs` can't be used at once."       )
                 exit(1       )
           __A ,					__A		 =   self._dataset, self._name
           __A		 =   dataset_module_factory(lowercase__       )
           __A		 =   import_main_class(dataset_module.module_path       )
           __A		 =   []
           if self._beam_pipeline_options:
                 __A		 =   beam.options.pipeline_options.PipelineOptions(
                     flags=[f"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","       ) if opt]       )
           else:
                 __A		 =   None
           if self._all_configs and len(builder_cls.BUILDER_CONFIGS       ) > 0:
                 for builder_config in builder_cls.BUILDER_CONFIGS:
                       builders.append(
                           builder_cls(
                               config_name=builder_config.name					,					data_dir=self._data_dir					,					hash=dataset_module.hash					,					beam_options=lowercase__					,					cache_dir=self._cache_dir					,					base_path=dataset_module.builder_kwargs.get("base_path"       )					,					)       )
           else:
                 builders.append(
                     builder_cls(
                         config_name=lowercase__					,					data_dir=self._data_dir					,					beam_options=lowercase__					,					cache_dir=self._cache_dir					,					base_path=dataset_module.builder_kwargs.get("base_path"       )					,					**self._config_kwargs					,					)       )

           for builder in builders:
                 builder.download_and_prepare(
                     download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                     if not self._force_redownload
                     else DownloadMode.FORCE_REDOWNLOAD					,					download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH       )					,					verification_mode=VerificationMode.NO_CHECKS
                     if self._ignore_verifications
                     else VerificationMode.ALL_CHECKS					,					try_from_hf_gcs=lowercase__					,					)
                 if self._save_infos:
                       builder._save_infos()

           print("Apache beam run successful."       )

           # If save_infos=True, the dataset infos file is created next to the loaded module file.
           # 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()					,					config.DATASETDICT_INFOS_FILENAME       )

                 __A		 =   Path(lowercase__       ).name + ".py"

                 __A		 =   os.path.join(lowercase__					,					lowercase__       )
                 if os.path.isfile(lowercase__       ):
                       __A		 =   os.path.dirname(lowercase__       )
                 elif os.path.isfile(lowercase__       ):
                       __A		 =   path
                 else:  # in case of a remote dataset
                       print(f"Dataset Infos file saved at {dataset_infos_path}"       )
                       exit(1       )

                 # Move datasetinfo back to the user
                 __A		 =   os.path.join(lowercase__					,					config.DATASETDICT_INFOS_FILENAME       )
                 copyfile(lowercase__					,					lowercase__       )
                 print(f"Dataset Infos file saved at {user_dataset_infos_path}"       )


