






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


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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  __UpperCAmelCase						(_UpperCAmelCase       ):
			__snake_case							: Union[str, Any]							=				42
			__snake_case							: List[Any]							=				None

def 			__lowerCamelCase     (						snake_case__			,snake_case__=0.999			,snake_case__="cosine"			,)				->    Optional[int]:






						"""simple docstring"""





						if alpha_transform_type == "cosine":

												def alpha_bar_fn(snake_case__						):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(snake_case__						):
																		return math.exp(t * -12.0						)

						else:
												raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'						)

						_SCREAMING_SNAKE_CASE       =    []
						for i in range(snake_case__						):
												_SCREAMING_SNAKE_CASE       =    i / num_diffusion_timesteps
												_SCREAMING_SNAKE_CASE       =    (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(snake_case__						) / alpha_bar_fn(snake_case__						)			,snake_case__						)						)
						return torch.tensor(snake_case__			,dtype=torch.floataa						)




class  __UpperCAmelCase						(_UpperCAmelCase			,_UpperCAmelCase       ):
			__snake_case							: Optional[Any]							=				[e.name for e in KarrasDiffusionSchedulers]
			__snake_case							: Union[str, Any]							=				1







			@register_to_config
			def __init__(      self:    Optional[int] ,      UpperCAmelCase_:    int = 1_000 ,      UpperCAmelCase_:    float = 0.00_01 ,      UpperCAmelCase_:    float = 0.02 ,      UpperCAmelCase_:    str = "linear" ,      UpperCAmelCase_:    Optional[Union[np.ndarray, List[float]]] = None ,      UpperCAmelCase_:    str = "epsilon" ,      UpperCAmelCase_:    str = "linear" ,      UpperCAmelCase_:    Optional[bool] = False ,      UpperCAmelCase_:    str = "linspace" ,      UpperCAmelCase_:    int = 0 ,      ):







									'''simple docstring'''




									if trained_betas is not None:
															_SCREAMING_SNAKE_CASE       =    torch.tensor(lowercase__ ,      dtype=torch.floataa	)
									elif beta_schedule == "linear":
															_SCREAMING_SNAKE_CASE       =    torch.linspace(lowercase__ ,      lowercase__ ,      lowercase__ ,      dtype=torch.floataa	)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															_SCREAMING_SNAKE_CASE       =    (
															    torch.linspace(beta_start**0.5 ,      beta_end**0.5 ,      lowercase__ ,      dtype=torch.floataa	) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_SCREAMING_SNAKE_CASE       =    betas_for_alpha_bar(lowercase__	)
									else:
															raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'	)

									_SCREAMING_SNAKE_CASE       =    1.0 - self.betas
									_SCREAMING_SNAKE_CASE       =    torch.cumprod(self.alphas ,      dim=0	)

									_SCREAMING_SNAKE_CASE       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									_SCREAMING_SNAKE_CASE       =    np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
									_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowercase__	)

									# setable values
									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    np.linspace(0 ,      num_train_timesteps - 1 ,      lowercase__ ,      dtype=lowercase__	)[::-1].copy()
									_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowercase__	)
									_SCREAMING_SNAKE_CASE       =    False
									_SCREAMING_SNAKE_CASE       =    use_karras_sigmas







			@property
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

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







			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    torch.FloatTensor ,      UpperCAmelCase_:    Union[float, torch.FloatTensor]	):







									'''simple docstring'''




									if isinstance(lowercase__ ,      torch.Tensor	):
															_SCREAMING_SNAKE_CASE       =    timestep.to(self.timesteps.device	)
									_SCREAMING_SNAKE_CASE       =    (self.timesteps == timestep).nonzero().item()
									_SCREAMING_SNAKE_CASE       =    self.sigmas[step_index]

									_SCREAMING_SNAKE_CASE       =    sample / ((sigma**2 + 1) ** 0.5)

									_SCREAMING_SNAKE_CASE       =    True
									return sample







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    Union[str, torch.device] = None	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    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":
															_SCREAMING_SNAKE_CASE       =    np.linspace(0 ,      self.config.num_train_timesteps - 1 ,      lowercase__ ,      dtype=lowercase__	)[
															    ::-1
															].copy()
									elif self.config.timestep_spacing == "leading":
															_SCREAMING_SNAKE_CASE       =    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
															_SCREAMING_SNAKE_CASE       =    (np.arange(0 ,      lowercase__	) * step_ratio).round()[::-1].copy().astype(lowercase__	)
															timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
															_SCREAMING_SNAKE_CASE       =    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
															_SCREAMING_SNAKE_CASE       =    (np.arange(self.config.num_train_timesteps ,      0 ,      -step_ratio	)).round().copy().astype(lowercase__	)
															timesteps -= 1
									else:
															raise ValueError(
															    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'	)

									_SCREAMING_SNAKE_CASE       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									_SCREAMING_SNAKE_CASE       =    np.log(lowercase__	)

									if self.config.interpolation_type == "linear":
															_SCREAMING_SNAKE_CASE       =    np.interp(lowercase__ ,      np.arange(0 ,      len(lowercase__	)	) ,      lowercase__	)
									elif self.config.interpolation_type == "log_linear":
															_SCREAMING_SNAKE_CASE       =    torch.linspace(np.log(sigmas[-1]	) ,      np.log(sigmas[0]	) ,      num_inference_steps + 1	).exp()
									else:
															raise ValueError(
															    F'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
															    """ 'linear' or 'log_linear'"""	)

									if self.use_karras_sigmas:
															_SCREAMING_SNAKE_CASE       =    self._convert_to_karras(in_sigmas=lowercase__ ,      num_inference_steps=self.num_inference_steps	)
															_SCREAMING_SNAKE_CASE       =    np.array([self._sigma_to_t(lowercase__ ,      lowercase__	) for sigma in sigmas]	)

									_SCREAMING_SNAKE_CASE       =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
									_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowercase__	).to(device=lowercase__	)
									if str(lowercase__	).startswith("""mps"""	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowercase__	).to(lowercase__ ,      dtype=torch.floataa	)
									else:
															_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowercase__	).to(device=lowercase__	)







			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    np.log(lowercase__	)

									# get distribution
									_SCREAMING_SNAKE_CASE       =    log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									_SCREAMING_SNAKE_CASE       =    np.cumsum((dists >= 0) ,      axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
									_SCREAMING_SNAKE_CASE       =    low_idx + 1

									_SCREAMING_SNAKE_CASE       =    log_sigmas[low_idx]
									_SCREAMING_SNAKE_CASE       =    log_sigmas[high_idx]

									# interpolate sigmas
									_SCREAMING_SNAKE_CASE       =    (low - log_sigma) / (low - high)
									_SCREAMING_SNAKE_CASE       =    np.clip(lowercase__ ,      0 ,      1	)

									# transform interpolation to time range
									_SCREAMING_SNAKE_CASE       =    (1 - w) * low_idx + w * high_idx
									_SCREAMING_SNAKE_CASE       =    t.reshape(sigma.shape	)
									return t







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    torch.FloatTensor ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    in_sigmas[-1].item()
									_SCREAMING_SNAKE_CASE       =    in_sigmas[0].item()

									_SCREAMING_SNAKE_CASE       =    7.0  # 7.0 is the value used in the paper
									_SCREAMING_SNAKE_CASE       =    np.linspace(0 ,      1 ,      lowercase__	)
									_SCREAMING_SNAKE_CASE       =    sigma_min ** (1 / rho)
									_SCREAMING_SNAKE_CASE       =    sigma_max ** (1 / rho)
									_SCREAMING_SNAKE_CASE       =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas







			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    torch.FloatTensor ,      UpperCAmelCase_:    Union[float, torch.FloatTensor] ,      UpperCAmelCase_:    torch.FloatTensor ,      UpperCAmelCase_:    float = 0.0 ,      UpperCAmelCase_:    float = 0.0 ,      UpperCAmelCase_:    float = float("""inf"""	) ,      UpperCAmelCase_:    float = 1.0 ,      UpperCAmelCase_:    Optional[torch.Generator] = None ,      UpperCAmelCase_:    bool = True ,      ):







									'''simple docstring'''




									if (
									    isinstance(lowercase__ ,      lowercase__	)
									    or isinstance(lowercase__ ,      torch.IntTensor	)
									    or isinstance(lowercase__ ,      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(lowercase__ ,      torch.Tensor	):
															_SCREAMING_SNAKE_CASE       =    timestep.to(self.timesteps.device	)

									_SCREAMING_SNAKE_CASE       =    (self.timesteps == timestep).nonzero().item()
									_SCREAMING_SNAKE_CASE       =    self.sigmas[step_index]

									_SCREAMING_SNAKE_CASE       =    min(s_churn / (len(self.sigmas	) - 1) ,      2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

									_SCREAMING_SNAKE_CASE       =    randn_tensor(
									    model_output.shape ,      dtype=model_output.dtype ,      device=model_output.device ,      generator=lowercase__	)

									_SCREAMING_SNAKE_CASE       =    noise * s_noise
									_SCREAMING_SNAKE_CASE       =    sigma * (gamma + 1)

									if gamma > 0:
															_SCREAMING_SNAKE_CASE       =    sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
															_SCREAMING_SNAKE_CASE       =    model_output
									elif self.config.prediction_type == "epsilon":
															_SCREAMING_SNAKE_CASE       =    sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															_SCREAMING_SNAKE_CASE       =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
															raise ValueError(
															    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'	)

									# 2. Convert to an ODE derivative
									_SCREAMING_SNAKE_CASE       =    (sample - pred_original_sample) / sigma_hat

									_SCREAMING_SNAKE_CASE       =    self.sigmas[step_index + 1] - sigma_hat

									_SCREAMING_SNAKE_CASE       =    sample + derivative * dt

									if not return_dict:
															return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=lowercase__ ,      pred_original_sample=lowercase__	)







			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    torch.FloatTensor ,      UpperCAmelCase_:    torch.FloatTensor ,      UpperCAmelCase_:    torch.FloatTensor ,      ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.sigmas.to(device=original_samples.device ,      dtype=original_samples.dtype	)
									if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE       =    self.timesteps.to(original_samples.device ,      dtype=torch.floataa	)
															_SCREAMING_SNAKE_CASE       =    timesteps.to(original_samples.device ,      dtype=torch.floataa	)
									else:
															_SCREAMING_SNAKE_CASE       =    self.timesteps.to(original_samples.device	)
															_SCREAMING_SNAKE_CASE       =    timesteps.to(original_samples.device	)

									_SCREAMING_SNAKE_CASE       =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

									_SCREAMING_SNAKE_CASE       =    sigmas[step_indices].flatten()
									while len(sigma.shape	) < len(original_samples.shape	):
															_SCREAMING_SNAKE_CASE       =    sigma.unsqueeze(-1	)

									_SCREAMING_SNAKE_CASE       =    original_samples + noise * sigma
									return noisy_samples







			def __len__(      self:    Tuple	):







									'''simple docstring'''




									return self.config.num_train_timesteps









'''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}"       )


