


"""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 							A_      (       _a ):
  lowerCAmelCase__      			=	42
  lowerCAmelCase__      			=	42




class 							A_      (       _a     ,	_a ):
  lowerCAmelCase__      			=	1


  @register_to_config
  def __init__(			self:						List[Any]							,__lowerCAmelCase:						int = 2_000							,__lowerCAmelCase:						float = 0.15							,__lowerCAmelCase:						float = 0.01							,__lowerCAmelCase:						float = 13_48.0							,__lowerCAmelCase:						float = 1e-5							,__lowerCAmelCase:						int = 1							,):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     sigma_max

     # setable values
     _lowerCamelCase   :				Tuple							=     None

     self.set_sigmas(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    )


  def 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						Optional[int] = None    ):







     '''simple docstring'''







     return sample


  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						int							,__lowerCAmelCase:						float = None							,__lowerCAmelCase:						Union[str, torch.device] = None    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     sampling_eps if sampling_eps is not None else self.config.sampling_eps

     _lowerCamelCase   :				str							=     torch.linspace(1							,__lowerCAmelCase							,__lowerCAmelCase							,device=__lowerCAmelCase    )


  def 							_lowercase					(			self:						int							,__lowerCAmelCase:						int							,__lowerCAmelCase:						float = None							,__lowerCAmelCase:						float = None							,__lowerCAmelCase:						float = None    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     sigma_min if sigma_min is not None else self.config.sigma_min
     _lowerCamelCase   :				List[Any]							=     sigma_max if sigma_max is not None else self.config.sigma_max
     _lowerCamelCase   :				Any							=     sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
        self.set_timesteps(__lowerCAmelCase							,__lowerCAmelCase    )

     _lowerCamelCase   :				Dict							=     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     _lowerCamelCase   :				str							=     torch.exp(torch.linspace(math.log(__lowerCAmelCase    )							,math.log(__lowerCAmelCase    )							,__lowerCAmelCase    )    )
     _lowerCamelCase   :				Tuple							=     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )


  def 							_lowercase					(			self:						Optional[Any]							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Optional[Any]    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						int							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						Optional[torch.Generator] = None							,__lowerCAmelCase:						bool = True							,):







     '''simple docstring'''







     if self.timesteps is None:
        raise ValueError(
            "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

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

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

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


  def 							_lowercase					(			self:						str							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						Optional[torch.Generator] = None							,__lowerCAmelCase:						bool = True							,):







     '''simple docstring'''







     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
     _lowerCamelCase   :				Optional[Any]							=     randn_tensor(sample.shape							,layout=sample.layout							,generator=__lowerCAmelCase    ).to(sample.device    )

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

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=__lowerCAmelCase    )


  def 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						torch.FloatTensor							,):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     timesteps.to(original_samples.device    )
     _lowerCamelCase   :				str							=     self.discrete_sigmas.to(original_samples.device    )[timesteps]
     _lowerCamelCase   :				Optional[Any]							=     (
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(__lowerCAmelCase    ) * sigmas[:, None, None, None]
     )
     _lowerCamelCase   :				Optional[Any]							=     noise + original_samples
     return noisy_samples






  def __len__(			self:						List[str]    ):







     '''simple docstring'''







     return self.config.num_train_timesteps
'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint






@require_torch
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 4], [1, 2, 3, 4]]
					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)
					self.assertTrue(isinstance(dc.token_ids			,       __lowerCAmelCase))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2], [1, 2, 3, 4]]

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(__lowerCAmelCase)  # fails here



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(3)
					lowerCAmelCase     =    stepped is True and completed is True and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 3])







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(4)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2, 4])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 4, 5])

					dc.reset()

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 3)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 2)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.remaining() == 0)
					self.assertTrue(dc.current_seq == [1, 2, 5])




