






# 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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	42
					a_			=	42



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_		):





					"""simple docstring"""






					a_			=	1



					@register_to_config
					def __init__(      self  :						Any							,	__A  :						int = 2_0_0_0							,	__A  :						float = 0.1_5							,	__A  :						float = 0.0_1							,	__A  :						float = 1_3_4_8.0							,	__A  :						float = 1e-5							,	__A  :						int = 1							,	):
						# standard deviation of the initial noise distribution
						snake_case__				:   Tuple     =						sigma_max

						# setable values
						snake_case__				:   Union[str, Any]     =						None

						self.set_sigmas(__A							,	__A							,	__A							,	__A )



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



					def  _lowercase  (      self  :						str							,	__A  :						int							,	__A  :						float = None							,	__A  :						Union[str, torch.device] = None ):
						snake_case__				:   List[Any]     =						sampling_eps if sampling_eps is not None else self.config.sampling_eps

						snake_case__				:   Union[str, Any]     =						torch.linspace(1							,	__A							,	__A							,	device=__A )



					def  _lowercase  (      self  :						str							,	__A  :						int							,	__A  :						float = None							,	__A  :						float = None							,	__A  :						float = None ):
						snake_case__				:   List[Any]     =						sigma_min if sigma_min is not None else self.config.sigma_min
						snake_case__				:   Tuple     =						sigma_max if sigma_max is not None else self.config.sigma_max
						snake_case__				:   Union[str, Any]     =						sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if self.timesteps is None:
							self.set_timesteps(__A							,	__A )

						snake_case__				:   str     =						sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
						snake_case__				:   List[str]     =						torch.exp(torch.linspace(math.log(__A )							,	math.log(__A )							,	__A ) )
						snake_case__				:   Union[str, Any]     =						torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )



					def  _lowercase  (      self  :						Tuple							,	__A  :						int							,	__A  :						str ):
						return torch.where(
						    timesteps == 0							,	torch.zeros_like(t.to(timesteps.device ) )							,	self.discrete_sigmas[timesteps - 1].to(timesteps.device )							,	)



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

						snake_case__				:   Dict     =						timestep * torch.ones(
						    sample.shape[0]							,	device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
						snake_case__				:   str     =						(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
						snake_case__				:   Optional[int]     =						timesteps.to(self.discrete_sigmas.device )

						snake_case__				:   Dict     =						self.discrete_sigmas[timesteps].to(sample.device )
						snake_case__				:   List[Any]     =						self.get_adjacent_sigma(__A							,	__A ).to(sample.device )
						snake_case__				:   Any     =						torch.zeros_like(__A )
						snake_case__				:   int     =						(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
						snake_case__				:   Any     =						diffusion.flatten()
						while len(diffusion.shape ) < len(sample.shape ):
							snake_case__				:   Optional[int]     =						diffusion.unsqueeze(-1 )
						snake_case__				:   List[Any]     =						drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						snake_case__				:   List[str]     =						randn_tensor(
						    sample.shape							,	layout=sample.layout							,	generator=__A							,	device=sample.device							,	dtype=sample.dtype )
						snake_case__				:   Tuple     =						sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						snake_case__				:   Tuple     =						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=__A							,	prev_sample_mean=__A )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						torch.FloatTensor							,	__A  :						torch.FloatTensor							,	__A  :						Optional[torch.Generator] = None							,	__A  :						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
						snake_case__				:   Optional[int]     =						randn_tensor(sample.shape							,	layout=sample.layout							,	generator=__A ).to(sample.device )

						# compute step size from the model_output, the noise, and the snr
						snake_case__				:   Dict     =						torch.norm(model_output.reshape(model_output.shape[0]							,	-1 )							,	dim=-1 ).mean()
						snake_case__				:   Any     =						torch.norm(noise.reshape(noise.shape[0]							,	-1 )							,	dim=-1 ).mean()
						snake_case__				:   Dict     =						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
						snake_case__				:   Optional[int]     =						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
						snake_case__				:   Optional[int]     =						step_size.flatten()
						while len(step_size.shape ) < len(sample.shape ):
							snake_case__				:   List[Any]     =						step_size.unsqueeze(-1 )
						snake_case__				:   List[str]     =						sample + step_size * model_output
						snake_case__				:   Optional[int]     =						prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
							return (prev_sample,)

						return SchedulerOutput(prev_sample=__A )



					def  _lowercase  (      self  :						Any							,	__A  :						torch.FloatTensor							,	__A  :						torch.FloatTensor							,	__A  :						torch.FloatTensor							,	):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						snake_case__				:   Tuple     =						timesteps.to(original_samples.device )
						snake_case__				:   Union[str, Any]     =						self.discrete_sigmas.to(original_samples.device )[timesteps]
						snake_case__				:   List[Any]     =						(
						    noise * sigmas[:, None, None, None]
						    if noise is not None
						    else torch.randn_like(__A ) * sigmas[:, None, None, None]
						)
						snake_case__				:   str     =						noise + original_samples
						return noisy_samples






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











import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Tuple     =						get_activation("gelu" )
						self.assertTrue(torch.allclose(gelu_python(__A )							,	torch_builtin(__A ) ) )
						self.assertFalse(torch.allclose(gelu_python(__A )							,	gelu_new(__A ) ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						snake_case__				:   int     =						get_activation("gelu_10" )

						snake_case__				:   Optional[int]     =						torch_builtin(__A )
						snake_case__				:   Dict     =						geluaa(__A )

						snake_case__				:   Optional[Any]     =						torch.where(y_gelu_aa < 1_0.0							,	1							,	0 )

						self.assertTrue(torch.max(__A ).item() == 1_0.0 )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask							,	y_gelu_aa * clipped_mask ) )



					def  _lowercase  (      self  :						str ):
						get_activation("gelu" )
						get_activation("gelu_10" )
						get_activation("gelu_fast" )
						get_activation("gelu_new" )
						get_activation("gelu_python" )
						get_activation("gelu_pytorch_tanh" )
						get_activation("linear" )
						get_activation("mish" )
						get_activation("quick_gelu" )
						get_activation("relu" )
						get_activation("sigmoid" )
						get_activation("silu" )
						get_activation("swish" )
						get_activation("tanh" )
						with self.assertRaises(__A ):
							get_activation("bogus" )
						with self.assertRaises(__A ):
							get_activation(__A )






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[str]     =						get_activation("gelu" )
						snake_case__				:   Any     =						1
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						self.assertEqual(acta.a							,	1 )
						with self.assertRaises(__A ):
							snake_case__				:   int     =						acta.a



