





'''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


lowerCAmelCase__      :   Optional[int]      	=			get_logger(__name__)


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		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      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	False  # to tell pytest it's not a test class
					@staticmethod
					def 		lowerCamelCase_   (   UpperCAmelCase_       :      ArgumentParser		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     parser.add_parser("test"		,	help="Test dataset implementation."		)
							test_parser.add_argument("--name"		,	type=UpperCAmelCase_		,	default=UpperCAmelCase_		,	help="Dataset processing name"		)
							test_parser.add_argument(
							    "--cache_dir"		,	type=UpperCAmelCase_		,	default=UpperCAmelCase_		,	help="Cache directory where the datasets are stored."		,	)
							test_parser.add_argument(
							    "--data_dir"		,	type=UpperCAmelCase_		,	default=UpperCAmelCase_		,	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=UpperCAmelCase_		,	help="Name of the dataset to download"		)
							test_parser.set_defaults(func=_test_command_factory		)
					def __init__(   self       :      Union[str, Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool		,	UpperCAmelCase_       :      bool		,	UpperCAmelCase_       :      bool		,	UpperCAmelCase_       :      bool		,	UpperCAmelCase_       :      bool		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     dataset
							__UpperCAmelCase	:							int     =     name
							__UpperCAmelCase	:							int     =     cache_dir
							__UpperCAmelCase	:							int     =     data_dir
							__UpperCAmelCase	:							str     =     all_configs
							__UpperCAmelCase	:							Optional[int]     =     save_infos
							__UpperCAmelCase	:							Union[str, Any]     =     ignore_verifications
							__UpperCAmelCase	:							int     =     force_redownload
							__UpperCAmelCase	:							List[str]     =     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	:							Union[str, Any]     =     True






					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							fl_logger().setLevel(UpperCAmelCase_		)
							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	:							str     =     self._dataset, self._name
							__UpperCAmelCase	:							Optional[int]     =     dataset_module_factory(UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     import_main_class(module.module_path		)
							__UpperCAmelCase	:							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=UpperCAmelCase_		,	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=UpperCAmelCase_		,	)
									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	:							List[Any]     =     os.path.join(builder_cls.get_imported_module_dir()		,	"README.md"		)
											__UpperCAmelCase	:							List[Any]     =     Path(UpperCAmelCase_		).name + ".py"
											__UpperCAmelCase	:							int     =     os.path.join(UpperCAmelCase_		,	UpperCAmelCase_		)
											if os.path.isfile(UpperCAmelCase_		):
													__UpperCAmelCase	:							Optional[int]     =     os.path.dirname(UpperCAmelCase_		)
											elif os.path.isfile(UpperCAmelCase_		):
													__UpperCAmelCase	:							int     =     path
											elif os.path.isdir(UpperCAmelCase_		):  # for local directories containing only data files
													__UpperCAmelCase	:							Optional[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	:							Tuple     =     os.path.join(UpperCAmelCase_		,	"README.md"		)
													copyfile(UpperCAmelCase_		,	UpperCAmelCase_		)
													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(UpperCAmelCase_		):
													logger.warning(f"Clearing cache at {download_dir}"		)
													rmtree(UpperCAmelCase_		)

							print("Test successful."		)











'''simple docstring'''

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

import numpy as np
import torch

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


@dataclass
class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	42
					SCREAMING_SNAKE_CASE							=	42






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0.999,       _UpperCAmelCase="cosine",       ):
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		__UpperCAmelCase	:							Union[str, Any]     =     []
		for i in range(_UpperCAmelCase  ):
				__UpperCAmelCase	:							int     =     i / num_diffusion_timesteps
				__UpperCAmelCase	:							Any     =     (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase  ) / alpha_bar_fn(_UpperCAmelCase  ),       _UpperCAmelCase  )  )
		return torch.tensor(_UpperCAmelCase,       dtype=torch.floataa  )






class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	1
					@register_to_config
					def __init__(   self       :      Dict		,	UpperCAmelCase_       :      int = 1_000		,	UpperCAmelCase_       :      float = 0.0001		,	UpperCAmelCase_       :      float = 0.02		,	UpperCAmelCase_       :      str = "linear"		,	UpperCAmelCase_       :      float = 0.0		,	UpperCAmelCase_       :      Optional[np.ndarray] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							if trained_betas is not None:
									__UpperCAmelCase	:							Tuple     =     torch.from_numpy(UpperCAmelCase_		)
							elif beta_schedule == "linear":
									__UpperCAmelCase	:							Dict     =     torch.linspace(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	dtype=torch.floataa		)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__UpperCAmelCase	:							int     =     (
									    torch.linspace(beta_start**0.5		,	beta_end**0.5		,	UpperCAmelCase_		,	dtype=torch.floataa		) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__UpperCAmelCase	:							str     =     betas_for_alpha_bar(UpperCAmelCase_		)
							elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									__UpperCAmelCase	:							Optional[Any]     =     torch.linspace(-6		,	6		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[int]     =     torch.sigmoid(UpperCAmelCase_		) * (beta_end - beta_start) + beta_start
							else:
									raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

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

							__UpperCAmelCase	:							str     =     torch.tensor(1.0		)

							# standard deviation of the initial noise distribution
							__UpperCAmelCase	:							Optional[Any]     =     1.0

							# setable values
							__UpperCAmelCase	:							Optional[Any]     =     None
							__UpperCAmelCase	:							Optional[Any]     =     torch.from_numpy(np.arange(0		,	UpperCAmelCase_		)[::-1].copy()		)

							__UpperCAmelCase	:							Tuple     =     eta
					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[int] = None		):







							"""simple docstring"""
							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      Union[str, torch.device] = None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     min(self.config.num_train_timesteps		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							List[Any]     =     num_inference_steps

							__UpperCAmelCase	:							Dict     =     []

							__UpperCAmelCase	:							int     =     {}
							for j in range(0		,	num_inference_steps - jump_length		,	UpperCAmelCase_		):
									__UpperCAmelCase	:							int     =     jump_n_sample - 1

							__UpperCAmelCase	:							Optional[int]     =     num_inference_steps
							while t >= 1:
									__UpperCAmelCase	:							int     =     t - 1
									timesteps.append(UpperCAmelCase_		)

									if jumps.get(UpperCAmelCase_		,	0		) > 0:
											__UpperCAmelCase	:							Any     =     jumps[t] - 1
											for _ in range(UpperCAmelCase_		):
													__UpperCAmelCase	:							Optional[Any]     =     t + 1
													timesteps.append(UpperCAmelCase_		)

							__UpperCAmelCase	:							Optional[Any]     =     np.array(UpperCAmelCase_		) * (self.config.num_train_timesteps // self.num_inference_steps)
							__UpperCAmelCase	:							str     =     torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     t - self.config.num_train_timesteps // self.num_inference_steps

							__UpperCAmelCase	:							str     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							int     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Dict     =     1 - alpha_prod_t
							__UpperCAmelCase	:							Optional[Any]     =     1 - alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from
							# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
							# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
							# variance to pred_sample
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							__UpperCAmelCase	:							Dict     =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance
					def 		lowerCamelCase_   (   self       :      Any		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[torch.Generator] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     timestep
							__UpperCAmelCase	:							Optional[Any]     =     timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							__UpperCAmelCase	:							Any     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							Optional[int]     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Optional[Any]     =     1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							__UpperCAmelCase	:							List[Any]     =     (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									__UpperCAmelCase	:							int     =     torch.clamp(UpperCAmelCase_		,	-1		,	1		)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							__UpperCAmelCase	:							Optional[Any]     =     model_output.device
							__UpperCAmelCase	:							Optional[int]     =     randn_tensor(model_output.shape		,	generator=UpperCAmelCase_		,	device=UpperCAmelCase_		,	dtype=model_output.dtype		)
							__UpperCAmelCase	:							List[Any]     =     self.eta * self._get_variance(UpperCAmelCase_		) ** 0.5

							__UpperCAmelCase	:							str     =     0
							if t > 0 and self.eta > 0:
									__UpperCAmelCase	:							Union[str, Any]     =     std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							int     =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							Any     =     alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							__UpperCAmelCase	:							str     =     (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							__UpperCAmelCase	:							Union[str, Any]     =     mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

							return RePaintSchedulerOutput(prev_sample=UpperCAmelCase_		,	pred_original_sample=UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      List[str]=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.config.num_train_timesteps // self.num_inference_steps

							for i in range(UpperCAmelCase_		):
									__UpperCAmelCase	:							Any     =     self.betas[timestep + i]
									if sample.device.type == "mps":
											# randn does not work reproducibly on mps
											__UpperCAmelCase	:							Tuple     =     randn_tensor(sample.shape		,	dtype=sample.dtype		,	generator=UpperCAmelCase_		)
											__UpperCAmelCase	:							str     =     noise.to(sample.device		)
									else:
											__UpperCAmelCase	:							str     =     randn_tensor(sample.shape		,	generator=UpperCAmelCase_		,	device=sample.device		,	dtype=sample.dtype		)

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									__UpperCAmelCase	:							Any     =     (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.IntTensor		,	):







							"""simple docstring"""
							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)






					def __len__(   self       :      List[str]		):







							"""simple docstring"""
							return self.config.num_train_timesteps




