


'''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   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							42
		_a       					=							42



class   lowercase      (				A__      ,						A__   ):

		"""simple docstring"""

		_a       					=							1




		@register_to_config
		def __init__(						self       ,  UpperCamelCase_ = 2000       ,  UpperCamelCase_ = 0.15       ,  UpperCamelCase_ = 0.01       ,  UpperCamelCase_ = 1348.0       ,  UpperCamelCase_ = 1e-5       ,  UpperCamelCase_ = 1       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					sigma_max

							# setable values
							UpperCamelCase__  :Optional[Any]				   =					None

							self.set_sigmas(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							return sample




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

							UpperCamelCase__  :List[Any]				   =					torch.linspace(1       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  device=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					sigma_min if sigma_min is not None else self.config.sigma_min
							UpperCamelCase__  :Optional[int]				   =					sigma_max if sigma_max is not None else self.config.sigma_max
							UpperCamelCase__  :str				   =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
												self.set_timesteps(UpperCamelCase_       ,  UpperCamelCase_				)

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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = 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'''				)

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

							UpperCamelCase__  :int				   =					self.discrete_sigmas[timesteps].to(sample.device				)
							UpperCamelCase__  :List[Any]				   =					self.get_adjacent_sigma(UpperCamelCase_       ,  UpperCamelCase_				).to(sample.device				)
							UpperCamelCase__  :Any				   =					torch.zeros_like(UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					(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__  :List[str]				   =					diffusion.flatten()
							while len(diffusion.shape				) < len(sample.shape				):
												UpperCamelCase__  :Tuple				   =					diffusion.unsqueeze(-1				)
							UpperCamelCase__  :List[str]				   =					drift - diffusion**2 * model_output

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

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

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  ):

							'''simple docstring'''


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




		def __len__(						self				):

							'''simple docstring'''


							return self.config.num_train_timesteps



'''simple docstring'''
import unittest

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


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Union[str, Any]				   =					torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor(
							    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Any				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-large'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Any				   =					torch.Size((1, 12, 1024)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :List[Any]				   =					torch.tensor(
							    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)