



"""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_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       torch.FloatTensor
      __SCREAMING_SNAKE_CASE						:       torch.FloatTensor







class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			1



      @register_to_config
      def __init__(					self					:     List[Any]					,     A					:     int = 2_0_0_0					,     A					:     float = 0.15					,     A					:     float = 0.01					,     A					:     float = 1_348.0					,     A					:     float = 1e-5					,     A					:     int = 1					,     ):
           # standard deviation of the initial noise distribution
           _UpperCAmelCase      :     Union[str, Any]        =   sigma_max

           # setable values
           _UpperCAmelCase      :     List[Any]        =   None

           self.set_sigmas(A					,     A					,     A					,     A						)



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



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

           _UpperCAmelCase      :     Optional[Any]        =   torch.linspace(1					,     A					,     A					,     device=A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     int					,     A					:     float = None					,     A					:     float = None					,     A					:     float = None						):
           _UpperCAmelCase      :     Optional[int]        =   sigma_min if sigma_min is not None else self.config.sigma_min
           _UpperCAmelCase      :     Any        =   sigma_max if sigma_max is not None else self.config.sigma_max
           _UpperCAmelCase      :     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						)

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



      def 						snake_case_ (					self					:     Dict					,     A					:     Any					,     A					:     Dict						):
           return torch.where(
               timesteps == 0					,     torch.zeros_like(t.to(timesteps.device						)						)					,     self.discrete_sigmas[timesteps - 1].to(timesteps.device						)					,     )



      def 						snake_case_ (					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"						)

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

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

           #  equation 6: sample noise for the diffusion term of
           _UpperCAmelCase      :     Union[str, Any]        =   randn_tensor(
               sample.shape					,     layout=sample.layout					,     generator=A					,     device=sample.device					,     dtype=sample.dtype						)
           _UpperCAmelCase      :     Tuple        =   sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           _UpperCAmelCase      :     str        =   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 						snake_case_ (					self					:     Optional[Any]					,     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
           _UpperCAmelCase      :     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
           _UpperCAmelCase      :     Optional[int]        =   torch.norm(model_output.reshape(model_output.shape[0]					,     -1						)					,     dim=-1						).mean()
           _UpperCAmelCase      :     Dict        =   torch.norm(noise.reshape(noise.shape[0]					,     -1						)					,     dim=-1						).mean()
           _UpperCAmelCase      :     Tuple        =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
           _UpperCAmelCase      :     str        =   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
           _UpperCAmelCase      :     Optional[Any]        =   step_size.flatten()
           while len(step_size.shape						) < len(sample.shape						):
                _UpperCAmelCase      :     Optional[int]        =   step_size.unsqueeze(-1						)
           _UpperCAmelCase      :     Any        =   sample + step_size * model_output
           _UpperCAmelCase      :     List[str]        =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

           if not return_dict:
                return (prev_sample,)

           return SchedulerOutput(prev_sample=A						)



      def 						snake_case_ (					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
           _UpperCAmelCase      :     List[str]        =   timesteps.to(original_samples.device						)
           _UpperCAmelCase      :     Any        =   self.discrete_sigmas.to(original_samples.device						)[timesteps]
           _UpperCAmelCase      :     Dict        =   (
               noise * sigmas[:, None, None, None]
               if noise is not None
               else torch.randn_like(A						) * sigmas[:, None, None, None]
           )
           _UpperCAmelCase      :     int        =   noise + original_samples
           return noisy_samples



      def __len__(					self					:     Optional[Any]						):
           return self.config.num_train_timesteps





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowerCAmelCase    : Union[str, Any]							=							get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 _lowerCAmelCase    : Tuple							=							json.load(f)






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           return FSMTTokenizer.from_pretrained(A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   FSMTForConditionalGeneration.from_pretrained(A						).to(A						)
           if torch_device == "cuda":
                model.half()
           return model



      @parameterized.expand(
          [
              ["en-ru", 26.0],
              ["ru-en", 22.0],
              ["en-de", 22.0],
              ["de-en", 29.0],
          ]						)
      @slow
      def 						snake_case_ (					self					:     Any					,     A					:     Dict					,     A					:     List[str]						):
           # note: this test is not testing the best performance since it only evals a small batch
           # but it should be enough to detect a regression in the output quality
           _UpperCAmelCase      :     Any        =   f'facebook/wmt19-{pair}'
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer(A						)
           _UpperCAmelCase      :     Optional[int]        =   self.get_model(A						)

           _UpperCAmelCase      :     int        =   bleu_data[pair]["src"]
           _UpperCAmelCase      :     Optional[int]        =   bleu_data[pair]["tgt"]

           _UpperCAmelCase      :     List[str]        =   tokenizer(A					,     return_tensors="pt"					,     truncation=A					,     padding="longest"						).to(A						)
           _UpperCAmelCase      :     List[str]        =   model.generate(
               input_ids=batch.input_ids					,     num_beams=8					,     )
           _UpperCAmelCase      :     Any        =   tokenizer.batch_decode(
               A					,     skip_special_tokens=A					,     clean_up_tokenization_spaces=A						)
           _UpperCAmelCase      :     Any        =   calculate_bleu(A					,     A						)
           print(A						)
           self.assertGreaterEqual(scores["bleu"]					,     A						)
