






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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							@slow
							def       __A (  self:   str   )    ->				Any:
												_A								= AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''		,     return_dict=__A   ).to(__A   )
												_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(__A   )		,     labels=labels.to(__A   )   ).loss
												_A								= -(labels.shape[-1] * loss.item())

												_A								= -84.9_127
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )











import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case					):





							"""simple docstring"""

							A_						  =					1

							@register_to_config
							def __init__(  self:   Any		,     __A:   int = 10_00		,     __A:   Optional[Union[np.ndarray, List[float]]] = None   )    ->				List[str]:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__A   )

												# standard deviation of the initial noise distribution
												_A								= 1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												_A								= 4

												# running values
												_A								= []

							def       __A (  self:   str		,     __A:   int		,     __A:   Union[str, torch.device] = None   )    ->				int:
												_A								= num_inference_steps
												_A								= torch.linspace(1		,     0		,     num_inference_steps + 1   )[:-1]
												_A								= torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																	_A								= torch.tensor(self.config.trained_betas		,     dtype=torch.floataa   )
												else:
																	_A								= torch.sin(steps * math.pi / 2   ) ** 2

												_A								= (1.0 - self.betas**2) ** 0.5

												_A								= (torch.atana(self.betas		,     self.alphas   ) / math.pi * 2)[:-1]
												_A								= timesteps.to(__A   )

												_A								= []

							def       __A (  self:   Tuple		,     __A:   torch.FloatTensor		,     __A:   int		,     __A:   torch.FloatTensor		,     __A:   bool = True		,     )    ->				Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																	raise ValueError(
																	    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''   )

												_A								= (self.timesteps == timestep).nonzero().item()
												_A								= timestep_index + 1

												_A								= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__A   )

												if len(self.ets   ) == 1:
																	_A								= self.ets[-1]
												elif len(self.ets   ) == 2:
																	_A								= (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																	_A								= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																	_A								= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												_A								= self._get_prev_sample(__A		,     __A		,     __A		,     __A   )

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__A   )

							def       __A (  self:   Optional[int]		,     __A:   torch.FloatTensor		,     *__A:   Tuple		,     **__A:   List[Any]   )    ->				torch.FloatTensor:
												return sample

							def       __A (  self:   List[str]		,     __A:   Optional[Any]		,     __A:   Optional[Any]		,     __A:   Any		,     __A:   List[Any]   )    ->				List[Any]:
												_A								= self.alphas[timestep_index]
												_A								= self.betas[timestep_index]

												_A								= self.alphas[prev_timestep_index]
												_A								= self.betas[prev_timestep_index]

												_A								= (sample - sigma * ets) / max(__A		,     1e-8   )
												_A								= next_alpha * pred + ets * next_sigma

												return prev_sample



							def __len__(  self:   List[str]   )    ->				Dict:
												return self.config.num_train_timesteps



