



'''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


_UpperCamelCase							    =						get_logger(__name__)

def 	a_					(							_lowerCAmelCase	)				->				List[str]:
  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  lowerCamelCase_   (    SCREAMING_SNAKE_CASE__					):
  """simple docstring"""




  a_							 =False  # to tell pytest it's not a test class




  @staticmethod
  def 						_lowercase					(			_a				:     Any       ) -> Any:
    __lowerCamelCase	:	Union[str, Any]   	=			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				:     Dict      ,     _a				:     List[Any]      ,     _a				:     Union[str, Any]      ,     _a				:     List[Any]      ,     _a				:     List[str]      ,     _a				:     Optional[int]      ,     _a				:     List[str]      ,     _a				:     Optional[Any]      ,     _a				:     Dict      ,     _a				:     Tuple      ,     ) -> int:
    __lowerCamelCase	:	int   	=			dataset
    __lowerCamelCase	:	str   	=			name
    __lowerCamelCase	:	Union[str, Any]   	=			cache_dir
    __lowerCamelCase	:	Optional[int]   	=			data_dir
    __lowerCamelCase	:	Any   	=			all_configs
    __lowerCamelCase	:	Union[str, Any]   	=			save_infos
    __lowerCamelCase	:	Any   	=			ignore_verifications
    __lowerCamelCase	:	Tuple   	=			force_redownload
    __lowerCamelCase	:	Any   	=			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:
      __lowerCamelCase	:	str   	=			True




  def 						_lowercase					(			self				:     Dict       ) -> Any:
    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       )
    __lowerCamelCase	:	List[str]   	=			self._dataset, self._name
    __lowerCamelCase	:	List[str]   	=			dataset_module_factory(_UpperCAmelCase       )
    __lowerCamelCase	:	Optional[Any]   	=			import_main_class(module.module_path       )
    __lowerCamelCase	:	Dict   	=			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})'       )
      __lowerCamelCase	:	Union[str, Any]   	=			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:
        __lowerCamelCase	:	Any   	=			os.path.join(builder_cls.get_imported_module_dir()      ,     'README.md'       )
        __lowerCamelCase	:	Tuple   	=			Path(_UpperCAmelCase       ).name + ".py"
        __lowerCamelCase	:	List[Any]   	=			os.path.join(_UpperCAmelCase      ,     _UpperCAmelCase       )
        if os.path.isfile(_UpperCAmelCase       ):
          __lowerCamelCase	:	int   	=			os.path.dirname(_UpperCAmelCase       )
        elif os.path.isfile(_UpperCAmelCase       ):
          __lowerCamelCase	:	int   	=			path
        elif os.path.isdir(_UpperCAmelCase       ):  # for local directories containing only data files
          __lowerCamelCase	:	Optional[Any]   	=			path
        else:  # in case of a remote dataset
          __lowerCamelCase	:	Optional[Any]   	=			None
          print(f'Dataset card saved at {dataset_readme_path}'       )

        # Move dataset_info back to the user
        if dataset_dir is not None:
          __lowerCamelCase	:	int   	=			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       )
        __lowerCamelCase	:	Dict   	=			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
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =42
  a_							 =None




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=0.999	,_lowerCAmelCase="cosine"	,)				->				Union[str, Any]:
  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  __lowerCamelCase	:	Union[str, Any]   	=			[]
  for i in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			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  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




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




  @register_to_config
  def __init__(			self				:     List[Any]      ,     _a				:     int = 1000      ,     _a				:     float = 0.0001      ,     _a				:     float = 0.02      ,     _a				:     str = "linear"      ,     _a				:     Optional[Union[np.ndarray, List[float]]] = None      ,     _a				:     Optional[bool] = False      ,     _a				:     str = "epsilon"      ,     _a				:     str = "linspace"      ,     _a				:     int = 0      ,     ) -> Tuple:
    if trained_betas is not None:
      __lowerCamelCase	:	Optional[int]   	=			torch.tensor(_a      ,     dtype=torch.floataa       )
    elif beta_schedule == "linear":
      __lowerCamelCase	:	List[str]   	=			torch.linspace(_a      ,     _a      ,     _a      ,     dtype=torch.floataa       )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      __lowerCamelCase	:	Optional[int]   	=			(
          torch.linspace(beta_start**0.5      ,     beta_end**0.5      ,     _a      ,     dtype=torch.floataa       ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      __lowerCamelCase	:	Any   	=			betas_for_alpha_bar(_a       )
    else:
      raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

    __lowerCamelCase	:	Optional[Any]   	=			1.0 - self.betas
    __lowerCamelCase	:	Optional[int]   	=			torch.cumprod(self.alphas      ,     dim=0       )

    __lowerCamelCase	:	str   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       )

    # setable values
    __lowerCamelCase	:	List[str]   	=			None
    __lowerCamelCase	:	int   	=			use_karras_sigmas
    self.set_timesteps(_a      ,     _a       )
    __lowerCamelCase	:	int   	=			[]
    __lowerCamelCase	:	Dict   	=			False




  @property
  def 						_lowercase					(			self				:     Optional[Any]       ) -> Any:
    # 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 						_lowercase					(			self				:     List[Any]      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]       ) -> torch.FloatTensor:
    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	List[Any]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	int   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Any   	=			self.sigmas[step_index]
    __lowerCamelCase	:	List[str]   	=			sample / ((sigma**2 + 1) ** 0.5)
    __lowerCamelCase	:	Any   	=			True
    return sample




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     str      ,     _a				:     List[str]      ,     _a				:     Union[str, Any]       ) -> Optional[Any]:

    def lms_derivative(_a				:     List[str]       ):
      __lowerCamelCase	:	Any   	=			1.0
      for k in range(_a       ):
        if current_order == k:
          continue
        prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
      return prod

    __lowerCamelCase	:	Any   	=			integrate.quad(_a      ,     self.sigmas[t]      ,     self.sigmas[t + 1]      ,     epsrel=1e-4       )[0]

    return integrated_coeff




  def 						_lowercase					(			self				:     int      ,     _a				:     int      ,     _a				:     Union[str, torch.device] = None       ) -> str:
    __lowerCamelCase	:	Optional[Any]   	=			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      ,     _a      ,     dtype=_a       )[
          ::-1
      ].copy()
    elif self.config.timestep_spacing == "leading":
      __lowerCamelCase	:	Union[str, 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	:	Union[str, Any]   	=			(np.arange(0      ,     _a       ) * step_ratio).round()[::-1].copy().astype(_a       )
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      __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	:	str   	=			(np.arange(self.config.num_train_timesteps      ,     0      ,     -step_ratio       )).round().copy().astype(_a       )
      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[Any]   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	List[Any]   	=			np.log(_a       )
    __lowerCamelCase	:	Optional[int]   	=			np.interp(_a      ,     np.arange(0      ,     len(_a       )       )      ,     _a       )

    if self.use_karras_sigmas:
      __lowerCamelCase	:	int   	=			self._convert_to_karras(in_sigmas=_a       )
      __lowerCamelCase	:	str   	=			np.array([self._sigma_to_t(_a      ,     _a       ) for sigma in sigmas]       )

    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       ).to(device=_a       )
    if str(_a       ).startswith('mps'       ):
      # mps does not support float64
      __lowerCamelCase	:	List[str]   	=			torch.from_numpy(_a       ).to(_a      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	str   	=			torch.from_numpy(_a       ).to(device=_a       )

    __lowerCamelCase	:	List[Any]   	=			[]




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     List[Any]      ,     _a				:     int       ) -> Tuple:
    # get log sigma
    __lowerCamelCase	:	Optional[int]   	=			np.log(_a       )

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

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

    __lowerCamelCase	:	Optional[Any]   	=			log_sigmas[low_idx]
    __lowerCamelCase	:	str   	=			log_sigmas[high_idx]

    # interpolate sigmas
    __lowerCamelCase	:	List[str]   	=			(low - log_sigma) / (low - high)
    __lowerCamelCase	:	List[str]   	=			np.clip(_a      ,     0      ,     1       )

    # transform interpolation to time range
    __lowerCamelCase	:	Dict   	=			(1 - w) * low_idx + w * high_idx
    __lowerCamelCase	:	List[str]   	=			t.reshape(sigma.shape       )
    return t




  def 						_lowercase					(			self				:     int      ,     _a				:     torch.FloatTensor       ) -> torch.FloatTensor:
    __lowerCamelCase	:	float   	=			in_sigmas[-1].item()
    __lowerCamelCase	:	float   	=			in_sigmas[0].item()

    __lowerCamelCase	:	Optional[Any]   	=			7.0  # 7.0 is the value used in the paper
    __lowerCamelCase	:	Any   	=			np.linspace(0      ,     1      ,     self.num_inference_steps       )
    __lowerCamelCase	:	Optional[Any]   	=			sigma_min ** (1 / rho)
    __lowerCamelCase	:	Tuple   	=			sigma_max ** (1 / rho)
    __lowerCamelCase	:	str   	=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas




  def 						_lowercase					(			self				:     Dict      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]      ,     _a				:     torch.FloatTensor      ,     _a				:     int = 4      ,     _a				:     bool = True      ,     ) -> Union[LMSDiscreteSchedulerOutput, Tuple]:
    if not self.is_scale_input_called:
      warnings.warn(
          'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
          'See `StableDiffusionPipeline` for a usage example.'       )

    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	Optional[int]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	List[str]   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Optional[int]   	=			self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
      __lowerCamelCase	:	Optional[Any]   	=			sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
      # * c_out + input * c_skip
      __lowerCamelCase	:	List[str]   	=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
      __lowerCamelCase	:	Union[str, Any]   	=			model_output
    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
    __lowerCamelCase	:	str   	=			(sample - pred_original_sample) / sigma
    self.derivatives.append(_a       )
    if len(self.derivatives       ) > order:
      self.derivatives.pop(0       )

    # 3. Compute linear multistep coefficients
    __lowerCamelCase	:	List[str]   	=			min(step_index + 1      ,     _a       )
    __lowerCamelCase	:	int   	=			[self.get_lms_coefficient(_a      ,     _a      ,     _a       ) for curr_order in range(_a       )]

    # 4. Compute previous sample based on the derivatives path
    __lowerCamelCase	:	Optional[Any]   	=			sample + sum(
        coeff * derivative for coeff, derivative in zip(_a      ,     reversed(self.derivatives       )       )       )

    if not return_dict:
      return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=_a      ,     pred_original_sample=_a       )




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

    __lowerCamelCase	:	List[str]   	=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    __lowerCamelCase	:	Union[str, Any]   	=			sigmas[step_indices].flatten()
    while len(sigma.shape       ) < len(original_samples.shape       ):
      __lowerCamelCase	:	List[Any]   	=			sigma.unsqueeze(-1       )

    __lowerCamelCase	:	Optional[Any]   	=			original_samples + noise * sigma
    return noisy_samples




  def __len__(			self				:     Union[str, Any]       ) -> int:
    return self.config.num_train_timesteps






