





"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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






@dataclass
class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							42
					UpperCamelCase      =							42







class 					_UpperCAmelCase   (	lowercase_     ,							lowercase_ ):
					UpperCamelCase      =							1


					@register_to_config
					def __init__(					self			:Tuple     ,       __UpperCamelCase			:int = 20_00     ,       __UpperCamelCase			:float = 0.15     ,       __UpperCamelCase			:float = 0.01     ,       __UpperCamelCase			:float = 1_348.0     ,       __UpperCamelCase			:float = 1e-5     ,       __UpperCamelCase			:int = 1     ,       ):
												# standard deviation of the initial noise distribution
												A		=  sigma_max

												# setable values
												A		=  None

												self.set_sigmas(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:Optional[int] = None		):
												return sample


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:float = None     ,       __UpperCamelCase			:Union[str, torch.device] = None		):
												A		=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

												A		=  torch.linspace(1     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       device=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:float = None     ,       __UpperCamelCase			:float = None     ,       __UpperCamelCase			:float = None		):
												A		=  sigma_min if sigma_min is not None else self.config.sigma_min
												A		=  sigma_max if sigma_max is not None else self.config.sigma_max
												A		=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																			self.set_timesteps(__UpperCamelCase     ,       __UpperCamelCase		)

												A		=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												A		=  torch.exp(torch.linspace(math.log(__UpperCamelCase		)     ,       math.log(__UpperCamelCase		)     ,       __UpperCamelCase		)		)
												A		=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:Tuple		):
												return torch.where(
												    timesteps == 0     ,       torch.zeros_like(t.to(timesteps.device		)		)     ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device		)     ,       )


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:Optional[torch.Generator] = None     ,       __UpperCamelCase			:bool = True     ,       ):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

												A		=  timestep * torch.ones(
												    sample.shape[0]     ,       device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
												A		=  (timestep * (len(self.timesteps		) - 1)).long()

												# mps requires indices to be in the same device, so we use cpu as is the default with cuda
												A		=  timesteps.to(self.discrete_sigmas.device		)

												A		=  self.discrete_sigmas[timesteps].to(sample.device		)
												A		=  self.get_adjacent_sigma(__UpperCamelCase     ,       __UpperCamelCase		).to(sample.device		)
												A		=  torch.zeros_like(__UpperCamelCase		)
												A		=  (sigma**2 - adjacent_sigma**2) ** 0.5

												# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
												# also equation 47 shows the analog from SDE models to ancestral sampling methods
												A		=  diffusion.flatten()
												while len(diffusion.shape		) < len(sample.shape		):
																			A		=  diffusion.unsqueeze(-1		)
												A		=  drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												A		=  randn_tensor(
												    sample.shape     ,       layout=sample.layout     ,       generator=__UpperCamelCase     ,       device=sample.device     ,       dtype=sample.dtype		)
												A		=  sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												A		=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																			return (prev_sample, prev_sample_mean)

												return SdeVeOutput(prev_sample=__UpperCamelCase     ,       prev_sample_mean=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:Optional[torch.Generator] = None     ,       __UpperCamelCase			:bool = True     ,       ):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

												# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
												# sample noise for correction
												A		=  randn_tensor(sample.shape     ,       layout=sample.layout     ,       generator=__UpperCamelCase		).to(sample.device		)

												# compute step size from the model_output, the noise, and the snr
												A		=  torch.norm(model_output.reshape(model_output.shape[0]     ,       -1		)     ,       dim=-1		).mean()
												A		=  torch.norm(noise.reshape(noise.shape[0]     ,       -1		)     ,       dim=-1		).mean()
												A		=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
												A		=  step_size * torch.ones(sample.shape[0]		).to(sample.device		)
												# self.repeat_scalar(step_size, sample.shape[0])

												# compute corrected sample: model_output term and noise term
												A		=  step_size.flatten()
												while len(step_size.shape		) < len(sample.shape		):
																			A		=  step_size.unsqueeze(-1		)
												A		=  sample + step_size * model_output
												A		=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:torch.FloatTensor     ,       ):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												A		=  timesteps.to(original_samples.device		)
												A		=  self.discrete_sigmas.to(original_samples.device		)[timesteps]
												A		=  (
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(__UpperCamelCase		) * sigmas[:, None, None, None]
												)
												A		=  noise + original_samples
												return noisy_samples


					def __len__(					self			:Tuple		):
												return self.config.num_train_timesteps









"""simple docstring"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format






class 					_UpperCAmelCase   :
					UpperCamelCase      =							None


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)
												A		=  json.loads(feat_extract.to_json_string()		)
												for key, value in self.feat_extract_dict.items():
																			self.assertEqual(obj[key]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			A		=  os.path.join(__UpperCamelCase     ,       "feat_extract.json"		)
																			feat_extract_first.to_json_file(__UpperCamelCase		)
																			A		=  self.feature_extraction_class.from_json_file(__UpperCamelCase		)

												self.assertEqual(feat_extract_second.to_dict()     ,       feat_extract_first.to_dict()		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			A		=  feat_extract_first.save_pretrained(__UpperCamelCase		)[0]
																			check_json_file_has_correct_format(__UpperCamelCase		)
																			A		=  self.feature_extraction_class.from_pretrained(__UpperCamelCase		)

												self.assertEqual(feat_extract_second.to_dict()     ,       feat_extract_first.to_dict()		)


					def 	lowerCamelCase       (					self			:Tuple		):
												A		=  self.feature_extraction_class()
												self.assertIsNotNone(__UpperCamelCase		)


