






"""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 	__UpperCamelCase    (    SCREAMING_SNAKE_CASE__	):
     lowercase_						:							torch.FloatTensor
     lowercase_						:							Optional[torch.FloatTensor]			=     None


def 					UpperCAmelCase			(				a__    ,			a__=0.999    ,			a__="cosine"    ,			):





      '''simple docstring'''






      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      lowerCAmelCase      :List[str]				  = []
      for i in range(_UpperCamelCase   ):
            lowerCAmelCase      :str				  = i / num_diffusion_timesteps
            lowerCAmelCase      :List[str]				  = (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 	__UpperCamelCase    (    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__	):
     lowercase_						:							Union[str, Any]			=     [e.name for e in KarrasDiffusionSchedulers]
     lowercase_						:							List[Any]			=     1
     @register_to_config
     def __init__(       self       :  Union[str, Any]    ,   UpperCAmelCase       :  int = 1000    ,   UpperCAmelCase       :  float = 0.0_0_0_1    ,   UpperCAmelCase       :  float = 0.0_2    ,   UpperCAmelCase       :  str = "linear"    ,   UpperCAmelCase       :  Optional[Union[np.ndarray, List[float]]] = None    ,   UpperCAmelCase       :  Optional[bool] = False    ,   UpperCAmelCase       :  str = "epsilon"    ,   UpperCAmelCase       :  str = "linspace"    ,   UpperCAmelCase       :  int = 0    ,   )  ->					Any:



           if trained_betas is not None:
                 lowerCAmelCase      :List[str]				  = torch.tensor(a_    ,   dtype=torch.floataa      )
           elif beta_schedule == "linear":
                 lowerCAmelCase      :Tuple				  = torch.linspace(a_    ,   a_    ,   a_    ,   dtype=torch.floataa      )
           elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 lowerCAmelCase      :List[str]				  = (
                     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      :Tuple				  = betas_for_alpha_bar(a_      )
           else:
                 raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""      )

           lowerCAmelCase      :Dict				  = 1.0 - self.betas
           lowerCAmelCase      :Dict				  = torch.cumprod(self.alphas    ,   dim=0      )

           lowerCAmelCase      :int				  = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
           lowerCAmelCase      :str				  = np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
           lowerCAmelCase      :List[str]				  = torch.from_numpy(a_      )

           # setable values
           lowerCAmelCase      :List[str]				  = None
           lowerCAmelCase      :List[str]				  = use_karras_sigmas
           self.set_timesteps(a_    ,   a_      )
           lowerCAmelCase      :Union[str, Any]				  = []
           lowerCAmelCase      :str				  = False
     @property
     def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					List[str]:



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

           return (self.sigmas.max() ** 2 + 1) ** 0.5
     def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  torch.FloatTensor    ,   UpperCAmelCase       :  Union[float, torch.FloatTensor]      )  ->					Tuple:



           if isinstance(a_    ,   torch.Tensor      ):
                 lowerCAmelCase      :Optional[int]				  = timestep.to(self.timesteps.device      )
           lowerCAmelCase      :str				  = (self.timesteps == timestep).nonzero().item()
           lowerCAmelCase      :List[str]				  = self.sigmas[step_index]
           lowerCAmelCase      :Dict				  = sample / ((sigma**2 + 1) ** 0.5)
           lowerCAmelCase      :Union[str, Any]				  = True
           return sample
     def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  Tuple      )  ->					Optional[int]:



           def lms_derivative(UpperCAmelCase       :  int      ):
                 lowerCAmelCase      :Union[str, 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      :Tuple				  = integrate.quad(a_    ,   self.sigmas[t]    ,   self.sigmas[t + 1]    ,   epsrel=1e-4      )[0]

           return integrated_coeff
     def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  Union[str, torch.device] = None      )  ->					str:
           lowerCAmelCase      :Dict				  = 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      :Tuple				  = np.linspace(0    ,   self.config.num_train_timesteps - 1    ,   a_    ,   dtype=a_      )[
                     ::-1
                 ].copy()
           elif self.config.timestep_spacing == "leading":
                 lowerCAmelCase      :Dict				  = 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[str]				  = (np.arange(0    ,   a_      ) * step_ratio).round()[::-1].copy().astype(a_      )
                 timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                 lowerCAmelCase      :str				  = 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      :Tuple				  = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
           lowerCAmelCase      :str				  = np.log(a_      )
           lowerCAmelCase      :Any				  = np.interp(a_    ,   np.arange(0    ,   len(a_      )      )    ,   a_      )

           if self.use_karras_sigmas:
                 lowerCAmelCase      :List[Any]				  = self._convert_to_karras(in_sigmas=a_      )
                 lowerCAmelCase      :Any				  = np.array([self._sigma_to_t(a_    ,   a_      ) for sigma in sigmas]      )

           lowerCAmelCase      :Union[str, Any]				  = np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )

           lowerCAmelCase      :Optional[Any]				  = torch.from_numpy(a_      ).to(device=a_      )
           if str(a_      ).startswith('mps'      ):
                 # mps does not support float64
                 lowerCAmelCase      :List[Any]				  = torch.from_numpy(a_      ).to(a_    ,   dtype=torch.floataa      )
           else:
                 lowerCAmelCase      :Union[str, Any]				  = torch.from_numpy(a_      ).to(device=a_      )

           lowerCAmelCase      :List[Any]				  = []
     def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Optional[Any]      )  ->					List[str]:
           lowerCAmelCase      :str				  = np.log(a_      )

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

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

           lowerCAmelCase      :str				  = log_sigmas[low_idx]
           lowerCAmelCase      :List[Any]				  = log_sigmas[high_idx]

           # interpolate sigmas
           lowerCAmelCase      :Dict				  = (low - log_sigma) / (low - high)
           lowerCAmelCase      :Any				  = np.clip(a_    ,   0    ,   1      )

           # transform interpolation to time range
           lowerCAmelCase      :Tuple				  = (1 - w) * low_idx + w * high_idx
           lowerCAmelCase      :Tuple				  = t.reshape(sigma.shape      )
           return t
     def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  torch.FloatTensor      )  ->					List[str]:
           lowerCAmelCase      :Optional[Any]				  = in_sigmas[-1].item()
           lowerCAmelCase      :Optional[Any]				  = in_sigmas[0].item()

           lowerCAmelCase      :Dict				  = 7.0  # 7.0 is the value used in the paper
           lowerCAmelCase      :Union[str, Any]				  = np.linspace(0    ,   1    ,   self.num_inference_steps      )
           lowerCAmelCase      :List[Any]				  = sigma_min ** (1 / rho)
           lowerCAmelCase      :List[str]				  = sigma_max ** (1 / rho)
           lowerCAmelCase      :str				  = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
     def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  torch.FloatTensor    ,   UpperCAmelCase       :  Union[float, torch.FloatTensor]    ,   UpperCAmelCase       :  torch.FloatTensor    ,   UpperCAmelCase       :  int = 4    ,   UpperCAmelCase       :  bool = True    ,   )  ->					List[Any]:



           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      :List[str]				  = timestep.to(self.timesteps.device      )
           lowerCAmelCase      :str				  = (self.timesteps == timestep).nonzero().item()
           lowerCAmelCase      :int				  = self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                 lowerCAmelCase      :Union[str, Any]				  = sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                 # * c_out + input * c_skip
                 lowerCAmelCase      :Dict				  = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
                 lowerCAmelCase      :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      :List[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      :str				  = min(step_index + 1    ,   a_      )
           lowerCAmelCase      :Union[str, Any]				  = [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 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  torch.FloatTensor    ,   UpperCAmelCase       :  torch.FloatTensor    ,   UpperCAmelCase       :  torch.FloatTensor    ,   )  ->					List[str]:
           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      :List[str]				  = self.timesteps.to(original_samples.device    ,   dtype=torch.floataa      )
                 lowerCAmelCase      :Any				  = timesteps.to(original_samples.device    ,   dtype=torch.floataa      )
           else:
                 lowerCAmelCase      :Optional[Any]				  = self.timesteps.to(original_samples.device      )
                 lowerCAmelCase      :Dict				  = timesteps.to(original_samples.device      )

           lowerCAmelCase      :Dict				  = [(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      :Optional[Any]				  = sigma.unsqueeze(-1      )

           lowerCAmelCase      :Any				  = original_samples + noise * sigma
           return noisy_samples
     def __len__(       self       :  Optional[int]      )  ->					List[str]:
           return self.config.num_train_timesteps







"""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


__SCREAMING_SNAKE_CASE      =      get_logger(__name__)


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						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 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							str			=     False  # to tell pytest it's not a test class
					@staticmethod
					def 					UpperCAmelCase__   (       UpperCAmelCase       :  ArgumentParser      )  ->					Optional[int]:
											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       :  List[Any]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   )  ->					Any:
											lowerCAmelCase      :int				  = dataset
											lowerCAmelCase      :Tuple				  = name
											lowerCAmelCase      :Tuple				  = cache_dir
											lowerCAmelCase      :List[Any]				  = data_dir
											lowerCAmelCase      :int				  = all_configs
											lowerCAmelCase      :Dict				  = save_infos
											lowerCAmelCase      :Union[str, Any]				  = ignore_verifications
											lowerCAmelCase      :int				  = force_redownload
											lowerCAmelCase      :Union[str, 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      :List[str]				  = True
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					int:
											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  ,   lowerCAmelCase      :Dict				  = self._dataset, self._name
											lowerCAmelCase      :int				  = dataset_module_factory(UpperCAmelCase      )
											lowerCAmelCase      :int				  = 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      :int				  = 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      :Optional[Any]				  = os.path.join(builder_cls.get_imported_module_dir()    ,   'README.md'      )
																							lowerCAmelCase      :List[str]				  = Path(UpperCAmelCase      ).name + '.py'
																							lowerCAmelCase      :Dict				  = os.path.join(UpperCAmelCase    ,   UpperCAmelCase      )
																							if os.path.isfile(UpperCAmelCase      ):
																													lowerCAmelCase      :Any				  = os.path.dirname(UpperCAmelCase      )
																							elif os.path.isfile(UpperCAmelCase      ):
																													lowerCAmelCase      :Any				  = path
																							elif os.path.isdir(UpperCAmelCase      ):  # for local directories containing only data files
																													lowerCAmelCase      :Dict				  = path
																							else:  # in case of a remote dataset
																													lowerCAmelCase      :int				  = None
																													print(f"""Dataset card saved at {dataset_readme_path}"""      )

																							# Move dataset_info back to the user
																							if dataset_dir is not None:
																													lowerCAmelCase      :Any				  = 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      :str				  = 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.'      )