




# 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_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   42
    UpperCAmelCase__													=   42


class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   1


    @register_to_config
    def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			int = 2_000      ,				UpperCAmelCase__   :			float = 0.15      ,				UpperCAmelCase__   :			float = 0.01      ,				UpperCAmelCase__   :			float = 1348.0      ,				UpperCAmelCase__   :			float = 1e-5      ,				UpperCAmelCase__   :			int = 1      ,				)		->Tuple:





           '''simple docstring'''

           A__   				=					sigma_max

           # setable values
           A__   				=					None

           self.set_sigmas(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				UpperCAmelCase__   :			Optional[int] = None)		->torch.FloatTensor:





           '''simple docstring'''

           return sample


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			float = None      ,				UpperCAmelCase__   :			Union[str, torch.device] = None)		->Dict:





           '''simple docstring'''

           A__   				=					sampling_eps if sampling_eps is not None else self.config.sampling_eps

           A__   				=					torch.linspace(1      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				device=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			float = None      ,				UpperCAmelCase__   :			float = None      ,				UpperCAmelCase__   :			float = None)		->Optional[Any]:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			Optional[Any])		->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  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				UpperCAmelCase__   :			Optional[torch.Generator] = None      ,				UpperCAmelCase__   :			bool = True      ,				)		->Union[SdeVeOutput, Tuple]:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				UpperCAmelCase__   :			Optional[torch.Generator] = None      ,				UpperCAmelCase__   :			bool = True      ,				)		->Union[SchedulerOutput, Tuple]:





           '''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
           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  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				UpperCAmelCase__   :			torch.FloatTensor      ,				)		->torch.FloatTensor:





           '''simple docstring'''

           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)		->List[Any]:





           '''simple docstring'''

           return self.config.num_train_timesteps








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


