



'''simple docstring'''



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0.9_99  ,   _lowerCAmelCase="cosine"  ,   ):



							"""simple docstring"""



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.cos((t + 0.0_08) / 1.0_08 * 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}"""					)

							__lowercase       	=[]
							for i in range(_lowerCAmelCase					):
														__lowercase       	=i / num_diffusion_timesteps
														__lowercase       	=(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		(     A      ,       A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    [e.name for e in KarrasDiffusionSchedulers]
				lowerCAmelCase__					    =    2

				@register_to_config
				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  int = 1_0_0_0  ,       _lowerCAmelCase  :  float = 0.0_0085  ,       _lowerCAmelCase  :  float = 0.012  ,       _lowerCAmelCase  :  str = "linear"  ,       _lowerCAmelCase  :  Optional[Union[np.ndarray, List[float]]] = None  ,       _lowerCAmelCase  :  str = "epsilon"  ,       _lowerCAmelCase  :  str = "linspace"  ,       _lowerCAmelCase  :  int = 0  ,       ):







											'''simple docstring'''

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

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

											#  set all values
											self.set_timesteps(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											if schedule_timesteps is None:
																		__lowercase       	=self.timesteps

											__lowercase       	=(schedule_timesteps == timestep).nonzero()

											# The sigma index that is taken for the **very** first `step`
											# is always the second index (or the last index if there is only 1)
											# This way we can ensure we don't accidentally skip a sigma in
											# case we start in the middle of the denoising schedule (e.g. for image-to-image)
											if len(self._index_counter) == 0:
																		__lowercase       	=1 if len(_lowerCAmelCase) > 1 else 0
											else:
																		__lowercase       	=timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase) else timestep
																		__lowercase       	=self._index_counter[timestep_int]

											return indices[pos].item()

				@property
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

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

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

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Union[float, torch.FloatTensor]  ,       ):







											'''simple docstring'''

											__lowercase       	=self.index_for_timestep(_lowerCAmelCase)

											if self.state_in_first_order:
																		__lowercase       	=self.sigmas[step_index]
											else:
																		__lowercase       	=self.sigmas_interpol[step_index]

											__lowercase       	=sample / ((sigma**2 + 1) ** 0.5)
											return sample

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Union[str, torch.device] = None  ,       _lowerCAmelCase  :  Optional[int] = None  ,       ):







											'''simple docstring'''

											__lowercase       	=num_inference_steps

											__lowercase       	=num_train_timesteps or self.config.num_train_timesteps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																		__lowercase       	=np.linspace(0  ,       num_train_timesteps - 1  ,       _lowerCAmelCase  ,       dtype=_lowerCAmelCase)[::-1].copy()
											elif self.config.timestep_spacing == "leading":
																		__lowercase       	=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
																		__lowercase       	=(np.arange(0  ,       _lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase)
																		timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																		__lowercase       	=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
																		__lowercase       	=(np.arange(_lowerCAmelCase  ,       0  ,       -step_ratio)).round().copy().astype(_lowerCAmelCase)
																		timesteps -= 1
											else:
																		raise ValueError(
																		    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

											__lowercase       	=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
											__lowercase       	=torch.from_numpy(np.log(_lowerCAmelCase)).to(_lowerCAmelCase)

											__lowercase       	=np.interp(_lowerCAmelCase  ,       np.arange(0  ,       len(_lowerCAmelCase))  ,       _lowerCAmelCase)
											__lowercase       	=np.concatenate([sigmas, [0.0]]).astype(np.floataa)
											__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(device=_lowerCAmelCase)

											# interpolate sigmas
											__lowercase       	=sigmas.log().lerp(sigmas.roll(1).log()  ,       0.5).exp()

											__lowercase       	=torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
											__lowercase       	=torch.cat(
											    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

											if str(_lowerCAmelCase).startswith('mps'):
																		# mps does not support float64
																		__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase  ,       dtype=torch.floataa)
											else:
																		__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)

											# interpolate timesteps
											__lowercase       	=self.sigma_to_t(_lowerCAmelCase).to(_lowerCAmelCase  ,       dtype=timesteps.dtype)
											__lowercase       	=torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,       dim=-1).flatten()

											__lowercase       	=torch.cat([timesteps[:1], interleaved_timesteps])

											__lowercase       	=None

											# for exp beta schedules, such as the one for `pipeline_shap_e.py`
											# we need an index counter
											__lowercase       	=defaultdict(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=sigma.log()

											# get distribution
											__lowercase       	=log_sigma - self.log_sigmas[:, None]

											# get sigmas range
											__lowercase       	=dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
											__lowercase       	=low_idx + 1

											__lowercase       	=self.log_sigmas[low_idx]
											__lowercase       	=self.log_sigmas[high_idx]

											# interpolate sigmas
											__lowercase       	=(low - log_sigma) / (low - high)
											__lowercase       	=w.clamp(0  ,       1)

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

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											return self.sample is None

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  Union[torch.FloatTensor, np.ndarray]  ,       _lowerCAmelCase  :  Union[float, torch.FloatTensor]  ,       _lowerCAmelCase  :  Union[torch.FloatTensor, np.ndarray]  ,       _lowerCAmelCase  :  bool = True  ,       ):







											'''simple docstring'''

											__lowercase       	=self.index_for_timestep(_lowerCAmelCase)

											# advance index counter by 1
											__lowercase       	=timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase) else timestep
											self._index_counter[timestep_int] += 1

											if self.state_in_first_order:
																		__lowercase       	=self.sigmas[step_index]
																		__lowercase       	=self.sigmas_interpol[step_index + 1]
																		__lowercase       	=self.sigmas[step_index + 1]
											else:
																		# 2nd order / KDPM2's method
																		__lowercase       	=self.sigmas[step_index - 1]
																		__lowercase       	=self.sigmas_interpol[step_index]
																		__lowercase       	=self.sigmas[step_index]

											# currently only gamma=0 is supported. This usually works best anyways.
											# We can support gamma in the future but then need to scale the timestep before
											# passing it to the model which requires a change in API
											__lowercase       	=0
											__lowercase       	=sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																		__lowercase       	=sigma_hat if self.state_in_first_order else sigma_interpol
																		__lowercase       	=sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
																		__lowercase       	=sigma_hat if self.state_in_first_order else sigma_interpol
																		__lowercase       	=model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																		    sample / (sigma_input**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`""")

											if self.state_in_first_order:
																		# 2. Convert to an ODE derivative for 1st order
																		__lowercase       	=(sample - pred_original_sample) / sigma_hat
																		# 3. delta timestep
																		__lowercase       	=sigma_interpol - sigma_hat

																		# store for 2nd order step
																		__lowercase       	=sample
											else:
																		# DPM-Solver-2
																		# 2. Convert to an ODE derivative for 2nd order
																		__lowercase       	=(sample - pred_original_sample) / sigma_interpol

																		# 3. delta timestep
																		__lowercase       	=sigma_next - sigma_hat

																		__lowercase       	=self.sample
																		__lowercase       	=None

											__lowercase       	=sample + derivative * dt

											if not return_dict:
																		return (prev_sample,)

											return SchedulerOutput(prev_sample=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       ):







											'''simple docstring'''

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

											__lowercase       	=[self.index_for_timestep(_lowerCAmelCase  ,       _lowerCAmelCase) for t in timesteps]

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

											__lowercase       	=original_samples + noise * sigma
											return noisy_samples

				def __len__(							self  :  Tuple):







											'''simple docstring'''

											return self.config.num_train_timesteps








'''simple docstring'''



import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict



@pytest.mark.parametrize(
    'files'  ,   [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=tmp_path_factory.mktemp('dset_infos_dir'					)
							if "full:README.md" in files:
														with open(dataset_infos_dir / 'README.md'  ,   'w'					) as f:
																					f.write('---\ndataset_info:\n  dataset_size: 42\n---'					)
							if "empty:README.md" in files:
														with open(dataset_infos_dir / 'README.md'  ,   'w'					) as f:
																					f.write(''					)
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / 'dataset_infos.json'  ,   'w'					) as f:
																					f.write('{"default": {"dataset_size": 42}}'					)
							__lowercase       	=DatasetInfosDict.from_directory(_lowerCAmelCase					)
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42



@pytest.mark.parametrize(
    'dataset_info'  ,   [
        DatasetInfo(),
        DatasetInfo(
            description='foo'  ,   features=Features({'a': Value('int32'					)}					)  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train'}]  ,   download_size=42  ,   ),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=str(_lowerCAmelCase					)
							dataset_info.write_to_directory(_lowerCAmelCase					)
							__lowercase       	=DatasetInfo.from_directory(_lowerCAmelCase					)
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(_lowerCAmelCase  ,   'dataset_info.json'					)					)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=DatasetInfo(
							    description='foo'  ,   citation='bar'  ,   homepage='https://foo.bar'  ,   license='CC0'  ,   features=Features({'a': Value('int32'					)}					)  ,   post_processed={}  ,   supervised_keys=()  ,   task_templates=[]  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train', 'num_examples': 42}]  ,   download_checksums={}  ,   download_size=1_337  ,   post_processing_size=442  ,   dataset_size=1_234  ,   size_in_bytes=1_337 + 442 + 1_234  ,   )
							__lowercase       	=dataset_info._to_yaml_dict()
							assert sorted(_lowerCAmelCase					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]  ,   (list, dict, int, str)					)
							__lowercase       	=yaml.safe_dump(_lowerCAmelCase					)
							__lowercase       	=yaml.safe_load(_lowerCAmelCase					)
							assert dataset_info_yaml_dict == reloaded



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=DatasetInfo()
							__lowercase       	=dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}



@pytest.mark.parametrize(
    'dataset_infos_dict'  ,   [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}					),
        DatasetInfosDict({'my_config_name': DatasetInfo()}					),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'  ,   features=Features({'a': Value('int32'					)}					)  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train'}]  ,   download_size=42  ,   )
            }					),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42					),
                'v2': DatasetInfo(dataset_size=1_337					),
            }					),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=str(_lowerCAmelCase					)
							dataset_infos_dict.write_to_directory(_lowerCAmelCase					)
							__lowercase       	=DatasetInfosDict.from_directory(_lowerCAmelCase					)

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														__lowercase       	=config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														__lowercase       	=DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(_lowerCAmelCase  ,   'README.md'					)					)



