
'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCAmelCase												=   logging.get_logger(__name__)  # pylint: disable=invalid-name


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= ['safety_checker', 'feature_extractor']






   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = True    ,):
    super().__init__()
    if hasattr(scheduler.config    ,'steps_offset'      ) and scheduler.config.steps_offset != 1:
     SCREAMING_SNAKE_CASE  :  Tuple								=		(
         f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
         f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
         'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
         ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
         ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
         ' file'
     )
     deprecate('steps_offset!=1'    ,'1.0.0'    ,__SCREAMING_SNAKE_CASE    ,standard_warn=__SCREAMING_SNAKE_CASE      )
     SCREAMING_SNAKE_CASE  :  List[Any]								=		dict(scheduler.config      )
     SCREAMING_SNAKE_CASE  :  Dict								=		1
     SCREAMING_SNAKE_CASE  :  Dict								=		FrozenDict(__SCREAMING_SNAKE_CASE      )

    self.register_modules(
        vae=__SCREAMING_SNAKE_CASE    ,text_encoder=__SCREAMING_SNAKE_CASE    ,tokenizer=__SCREAMING_SNAKE_CASE    ,unet=__SCREAMING_SNAKE_CASE    ,scheduler=__SCREAMING_SNAKE_CASE    ,safety_checker=__SCREAMING_SNAKE_CASE    ,feature_extractor=__SCREAMING_SNAKE_CASE    ,)
    self.register_to_config(requires_safety_checker=__SCREAMING_SNAKE_CASE      )






   @property
   def 					__a			(       self      ):
    return {k: getattr(self    ,__SCREAMING_SNAKE_CASE      ) for k in self.config.keys() if not k.startswith('_'      )}






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE = "auto"      ):
    if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     SCREAMING_SNAKE_CASE  :  List[str]								=		self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    self.enable_attention_slicing(__SCREAMING_SNAKE_CASE      )






   @torch.no_grad()
   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 0.8    ,__SCREAMING_SNAKE_CASE = 50    ,__SCREAMING_SNAKE_CASE = 7.5    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,__SCREAMING_SNAKE_CASE = 0.0    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = "pil"    ,__SCREAMING_SNAKE_CASE = True    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,):
    # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
    return StableDiffusionInpaintPipelineLegacy(**self.components      )(
        prompt=__SCREAMING_SNAKE_CASE    ,image=__SCREAMING_SNAKE_CASE    ,mask_image=__SCREAMING_SNAKE_CASE    ,strength=__SCREAMING_SNAKE_CASE    ,num_inference_steps=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,negative_prompt=__SCREAMING_SNAKE_CASE    ,num_images_per_prompt=__SCREAMING_SNAKE_CASE    ,eta=__SCREAMING_SNAKE_CASE    ,generator=__SCREAMING_SNAKE_CASE    ,output_type=__SCREAMING_SNAKE_CASE    ,return_dict=__SCREAMING_SNAKE_CASE    ,callback=__SCREAMING_SNAKE_CASE    ,)






   @torch.no_grad()
   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 0.8    ,__SCREAMING_SNAKE_CASE = 50    ,__SCREAMING_SNAKE_CASE = 7.5    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,__SCREAMING_SNAKE_CASE = 0.0    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = "pil"    ,__SCREAMING_SNAKE_CASE = True    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,**__SCREAMING_SNAKE_CASE    ,):
    # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
    return StableDiffusionImgaImgPipeline(**self.components      )(
        prompt=__SCREAMING_SNAKE_CASE    ,image=__SCREAMING_SNAKE_CASE    ,strength=__SCREAMING_SNAKE_CASE    ,num_inference_steps=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,negative_prompt=__SCREAMING_SNAKE_CASE    ,num_images_per_prompt=__SCREAMING_SNAKE_CASE    ,eta=__SCREAMING_SNAKE_CASE    ,generator=__SCREAMING_SNAKE_CASE    ,output_type=__SCREAMING_SNAKE_CASE    ,return_dict=__SCREAMING_SNAKE_CASE    ,callback=__SCREAMING_SNAKE_CASE    ,callback_steps=__SCREAMING_SNAKE_CASE    ,)






   @torch.no_grad()
   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 512    ,__SCREAMING_SNAKE_CASE = 512    ,__SCREAMING_SNAKE_CASE = 50    ,__SCREAMING_SNAKE_CASE = 7.5    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,__SCREAMING_SNAKE_CASE = 0.0    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = "pil"    ,__SCREAMING_SNAKE_CASE = True    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,):
    # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
    return StableDiffusionPipeline(**self.components      )(
        prompt=__SCREAMING_SNAKE_CASE    ,height=__SCREAMING_SNAKE_CASE    ,width=__SCREAMING_SNAKE_CASE    ,num_inference_steps=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,negative_prompt=__SCREAMING_SNAKE_CASE    ,num_images_per_prompt=__SCREAMING_SNAKE_CASE    ,eta=__SCREAMING_SNAKE_CASE    ,generator=__SCREAMING_SNAKE_CASE    ,latents=__SCREAMING_SNAKE_CASE    ,output_type=__SCREAMING_SNAKE_CASE    ,return_dict=__SCREAMING_SNAKE_CASE    ,callback=__SCREAMING_SNAKE_CASE    ,callback_steps=__SCREAMING_SNAKE_CASE    ,)




'''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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[str]   ,     **snake_case_				:	str							)       ->	Optional[int]:
 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     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   @staticmethod
   def 					__a			(       __SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		parser.add_parser('run_beam'    ,help='Run a Beam dataset processing pipeline'      )
    run_beam_parser.add_argument('dataset'    ,type=__SCREAMING_SNAKE_CASE    ,help='Name of the dataset to download'      )
    run_beam_parser.add_argument('--name'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Dataset config name'      )
    run_beam_parser.add_argument(
        '--cache_dir'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Cache directory where the datasets are stored'    ,)
    run_beam_parser.add_argument(
        '--beam_pipeline_options'    ,type=__SCREAMING_SNAKE_CASE    ,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=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,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=__SCREAMING_SNAKE_CASE      )






   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		dataset
    SCREAMING_SNAKE_CASE  :  List[Any]								=		name
    SCREAMING_SNAKE_CASE  :  int								=		cache_dir
    SCREAMING_SNAKE_CASE  :  str								=		beam_pipeline_options
    SCREAMING_SNAKE_CASE  :  List[str]								=		data_dir
    SCREAMING_SNAKE_CASE  :  int								=		all_configs
    SCREAMING_SNAKE_CASE  :  Any								=		save_infos
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ignore_verifications
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		force_redownload
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		config_kwargs






   def 					__a			(       self      ):
    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      )
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		self._dataset, self._name
    SCREAMING_SNAKE_CASE  :  str								=		dataset_module_factory(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		import_main_class(dataset_module.module_path      )
    SCREAMING_SNAKE_CASE  :  List[DatasetBuilder]								=		[]
    if self._beam_pipeline_options:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		beam.options.pipeline_options.PipelineOptions(
         flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','      ) if opt]      )
    else:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		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=__SCREAMING_SNAKE_CASE    ,cache_dir=self._cache_dir    ,base_path=dataset_module.builder_kwargs.get('base_path'      )    ,)      )
    else:
     builders.append(
         builder_cls(
             config_name=__SCREAMING_SNAKE_CASE    ,data_dir=self._data_dir    ,beam_options=__SCREAMING_SNAKE_CASE    ,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=__SCREAMING_SNAKE_CASE    ,)
     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:
     SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(builder_cls.get_imported_module_dir()    ,config.DATASETDICT_INFOS_FILENAME      )

     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		Path(__SCREAMING_SNAKE_CASE      ).name + '.py'

     SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
     if os.path.isfile(__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  Tuple								=		os.path.dirname(__SCREAMING_SNAKE_CASE      )
     elif os.path.isfile(__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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
     SCREAMING_SNAKE_CASE  :  List[Any]								=		os.path.join(__SCREAMING_SNAKE_CASE    ,config.DATASETDICT_INFOS_FILENAME      )
     copyfile(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
     print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""      )


