
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        __lowercase(							__snake_case				:		Tuple    ,**__snake_case				:		Any					)       ->   Any:
       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    ,**lowerCAmelCase__    ,)







class  _lowerCamelCase			(lowercase_					):

     @staticmethod
     def 						__lowerCamelCase				(  SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   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_							,       ):
            __snake_case						       =   dataset
            __snake_case						       =   name
            __snake_case						       =   cache_dir
            __snake_case						       =   beam_pipeline_options
            __snake_case						       =   data_dir
            __snake_case						       =   all_configs
            __snake_case						       =   save_infos
            __snake_case						       =   ignore_verifications
            __snake_case						       =   force_redownload
            __snake_case						       =   config_kwargs

     def 						__lowerCamelCase				(  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    )
            __snake_case						       =   self._dataset, self._name
            __snake_case						       =   dataset_module_factory(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   import_main_class(dataset_module.module_path    )
            __snake_case						       =   []
            if self._beam_pipeline_options:
                   __snake_case						       =   beam.options.pipeline_options.PipelineOptions(
                       flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','    ) if opt]    )
            else:
                   __snake_case						       =   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:
                   __snake_case						       =   os.path.join(builder_cls.get_imported_module_dir()							,       config.DATASETDICT_INFOS_FILENAME    )

                   __snake_case						       =   Path(SCREAMING_SNAKE_CASE_    ).name + '.py'

                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   if os.path.isfile(SCREAMING_SNAKE_CASE_    ):
                          __snake_case						       =   os.path.dirname(SCREAMING_SNAKE_CASE_    )
                   elif os.path.isfile(SCREAMING_SNAKE_CASE_    ):
                          __snake_case						       =   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
                   __snake_case						       =   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}'''    )





"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	None




def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							Optional[Any]=0.999       ,  lowerCAmelCase__ :							Optional[int]="cosine"       ,  )		->  Optional[Any]:

  """simple docstring"""




  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowerCAmelCase__ :							Optional[Any]		):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowerCAmelCase__ :							Union[str, Any]		):
      return math.exp(t * -12.0		)

  else:
    raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

  lowerCAmelCase_  :							List[str]								=					[]
  for i in range(lowerCAmelCase__		):
    lowerCAmelCase_  :							str								=					i / num_diffusion_timesteps
    lowerCAmelCase_  :							Optional[int]								=					(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__		) / alpha_bar_fn(lowerCAmelCase__		)       ,  lowerCAmelCase__		)		)
  return torch.tensor(lowerCAmelCase__       ,  dtype=torch.floataa		)







class  UpperCamelCase__  (      lowercase_,   lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	[e.name for e in KarrasDiffusionSchedulers]
      _SCREAMING_SNAKE_CASE    							=	1



      @register_to_config
      def __init__( self							: str       ,     SCREAMING_SNAKE_CASE_							: int = 1_0_0_0       ,     SCREAMING_SNAKE_CASE_							: float = 0.00_01       ,     SCREAMING_SNAKE_CASE_							: float = 0.02       ,     SCREAMING_SNAKE_CASE_							: str = "linear"       ,     SCREAMING_SNAKE_CASE_							: Optional[Union[np.ndarray, List[float]]] = None       ,     SCREAMING_SNAKE_CASE_							: str = "epsilon"       ,     SCREAMING_SNAKE_CASE_							: str = "linspace"       ,     SCREAMING_SNAKE_CASE_							: int = 0       ,     ):
        if trained_betas is not None:
          lowerCAmelCase_  :							int								=					torch.tensor(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        elif beta_schedule == "linear":
          lowerCAmelCase_  :							Union[str, Any]								=					torch.linspace(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowerCAmelCase_  :							Dict								=					(
              torch.linspace(beta_start**0.5       ,     beta_end**0.5       ,     SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowerCAmelCase_  :							Optional[int]								=					betas_for_alpha_bar(SCREAMING_SNAKE_CASE_			)
        else:
          raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

        lowerCAmelCase_  :							str								=					1.0 - self.betas
        lowerCAmelCase_  :							List[Any]								=					torch.cumprod(self.alphas       ,     dim=0			)

        lowerCAmelCase_  :							Dict								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							Optional[int]								=					np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							Optional[int]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)

        # setable values
        lowerCAmelCase_  :							Union[str, Any]								=					None
        lowerCAmelCase_  :							List[Any]								=					np.linspace(0       ,     num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[::-1].copy()
        lowerCAmelCase_  :							int								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							List[str]								=					False



      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Union[float, torch.FloatTensor]			):
        if isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							List[Any]								=					timestep.to(self.timesteps.device			)
        lowerCAmelCase_  :							Any								=					(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase_  :							List[str]								=					self.sigmas[step_index]
        lowerCAmelCase_  :							Optional[int]								=					sample / ((sigma**2 + 1) ** 0.5)
        lowerCAmelCase_  :							Dict								=					True
        return sample



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: Union[str, torch.device] = None			):
        lowerCAmelCase_  :							Optional[int]								=					num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
          lowerCAmelCase_  :							Any								=					np.linspace(0       ,     self.config.num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          lowerCAmelCase_  :							int								=					self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowerCAmelCase_  :							List[Any]								=					(np.arange(0       ,     SCREAMING_SNAKE_CASE_			) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_			)
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          lowerCAmelCase_  :							List[Any]								=					self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowerCAmelCase_  :							int								=					(np.arange(self.config.num_train_timesteps       ,     0       ,     -step_ratio			)).round().copy().astype(SCREAMING_SNAKE_CASE_			)
          timesteps -= 1
        else:
          raise ValueError(
              F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."			)

        lowerCAmelCase_  :							Optional[int]								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							List[str]								=					np.interp(SCREAMING_SNAKE_CASE_       ,     np.arange(0       ,     len(SCREAMING_SNAKE_CASE_			)			)       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Union[str, Any]								=					np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
        if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
          # mps does not support float64
          lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							List[Any]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Union[float, torch.FloatTensor]       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Optional[torch.Generator] = None       ,     SCREAMING_SNAKE_CASE_							: bool = True       ,     ):

        if (
            isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     torch.IntTensor			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     torch.LongTensor			)
        ):
          raise ValueError(
              (
                  'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                  ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                  ' one of the `scheduler.timesteps` as a timestep.'
              )       ,     )

        if not self.is_scale_input_called:
          logger.warning(
              'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
              'See `StableDiffusionPipeline` for a usage example.'			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							Union[str, Any]								=					timestep.to(self.timesteps.device			)

        lowerCAmelCase_  :							Optional[int]								=					(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase_  :							Dict								=					self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          lowerCAmelCase_  :							List[str]								=					sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowerCAmelCase_  :							Tuple								=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
          raise NotImplementedError('prediction_type not implemented yet: sample'			)
        else:
          raise ValueError(
              F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"			)

        lowerCAmelCase_  :							Any								=					self.sigmas[step_index]
        lowerCAmelCase_  :							Union[str, Any]								=					self.sigmas[step_index + 1]
        lowerCAmelCase_  :							List[str]								=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowerCAmelCase_  :							Dict								=					(sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        lowerCAmelCase_  :							Dict								=					(sample - pred_original_sample) / sigma

        lowerCAmelCase_  :							Tuple								=					sigma_down - sigma

        lowerCAmelCase_  :							List[str]								=					sample + derivative * dt

        lowerCAmelCase_  :							Any								=					model_output.device
        lowerCAmelCase_  :							Optional[int]								=					randn_tensor(model_output.shape       ,     dtype=model_output.dtype       ,     device=SCREAMING_SNAKE_CASE_       ,     generator=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					prev_sample + noise * sigma_up

        if not return_dict:
          return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=SCREAMING_SNAKE_CASE_       ,     pred_original_sample=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     ):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCAmelCase_  :							int								=					self.sigmas.to(device=original_samples.device       ,     dtype=original_samples.dtype			)
        if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_			):
          # mps does not support float64
          lowerCAmelCase_  :							Any								=					self.timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
          lowerCAmelCase_  :							str								=					timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							Dict								=					self.timesteps.to(original_samples.device			)
          lowerCAmelCase_  :							List[str]								=					timesteps.to(original_samples.device			)

        lowerCAmelCase_  :							Any								=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowerCAmelCase_  :							Dict								=					sigmas[step_indices].flatten()
        while len(sigma.shape			) < len(original_samples.shape			):
          lowerCAmelCase_  :							Optional[int]								=					sigma.unsqueeze(-1			)

        lowerCAmelCase_  :							List[str]								=					original_samples + noise * sigma
        return noisy_samples





      def __len__( self							: List[Any]			):
        return self.config.num_train_timesteps





