


# 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 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			42
 SCREAMING_SNAKE_CASE__ =			42







class 						_snake_case    ( _snake_case		,       _snake_case				):
 SCREAMING_SNAKE_CASE__ =			1




 @register_to_config
 def __init__(						self   ,    _lowerCamelCase = 2000   ,    _lowerCamelCase = 0.15   ,    _lowerCamelCase = 0.01   ,    _lowerCamelCase = 1348.0   ,    _lowerCamelCase = 1e-5   ,    _lowerCamelCase = 1   ,    ):
       # standard deviation of the initial noise distribution
       a		:int									=			sigma_max

       # setable values
       a		:Tuple									=			None

       self.set_sigmas(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       return sample




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None       ):
       a		:Any									=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

       a		:Dict									=			torch.linspace(1   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    device=_lowerCamelCase       )




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

       a		:Any									=			sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       a		:List[str]									=			torch.exp(torch.linspace(math.log(_lowerCamelCase       )   ,    math.log(_lowerCamelCase       )   ,    _lowerCamelCase       )       )
       a		:Optional[int]									=			torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]       )




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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = True   ,    ):
       if self.timesteps is None:
             raise ValueError(
                 '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

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

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

       #  equation 6: sample noise for the diffusion term of
       a		:int									=			randn_tensor(
           sample.shape   ,    layout=sample.layout   ,    generator=_lowerCamelCase   ,    device=sample.device   ,    dtype=sample.dtype       )
       a		:str									=			sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       a		:Dict									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = 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		:Optional[int]									=			randn_tensor(sample.shape   ,    layout=sample.layout   ,    generator=_lowerCamelCase       ).to(sample.device       )

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

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       a		:Optional[Any]									=			timesteps.to(original_samples.device       )
       a		:Tuple									=			self.discrete_sigmas.to(original_samples.device       )[timesteps]
       a		:Optional[int]									=			(
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(_lowerCamelCase       ) * sigmas[:, None, None, None]
       )
       a		:Optional[Any]									=			noise + original_samples
       return noisy_samples




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









import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





      a		:List[Any]									=			[]
      for step in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        a		:Union[str, Any]									=			os.path.join(UpperCAmelCase_					,    '''schedule.bin'''		)
                        torch.save(scheduler.state_dict()					,    UpperCAmelCase_		)

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			Adafactor(
           params=[w]   ,    lr=1e-2   ,    eps=(1e-30, 1e-3)   ,    clip_threshold=1.0   ,    decay_rate=-0.8   ,    betaa=_lowerCamelCase   ,    weight_decay=0.0   ,    relative_step=_lowerCamelCase   ,    scale_parameter=_lowerCamelCase   ,    warmup_init=_lowerCamelCase   ,    )
       for _ in range(1000       ):
             a		:str									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )







@require_torch
class 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase   ,    msg=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		:Any									=			{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'''num_warmup_steps''': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, '''num_cycles''': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'''num_warmup_steps''': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

             a		:List[Any]									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase       )  # wrap to test picklability of the schedule
             a		:Dict									=			unwrap_and_save_reload_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    msg=F'''failed for {scheduler_func} in save and reload'''       )







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





