






import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							A__			:    Tuple         =				'platform'
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= PegasusConfig
				lowercase__       							= {}
				lowercase__       							= """gelu"""




				def __init__(  self		:					Dict,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					List[Any]=13,						lowerCamelCase		:					Any=7,						lowerCamelCase		:					Dict=True,						lowerCamelCase		:					Tuple=False,						lowerCamelCase		:					List[str]=99,						lowerCamelCase		:					Optional[Any]=32,						lowerCamelCase		:					Any=5,						lowerCamelCase		:					List[str]=4,						lowerCamelCase		:					Tuple=37,						lowerCamelCase		:					Optional[int]=0.1,						lowerCamelCase		:					Optional[Any]=0.1,						lowerCamelCase		:					int=20,						lowerCamelCase		:					str=2,						lowerCamelCase		:					List[Any]=1,						lowerCamelCase		:					Dict=0,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    seq_length
								lowercase__     				=    is_training
								lowercase__     				=    use_labels
								lowercase__     				=    vocab_size
								lowercase__     				=    hidden_size
								lowercase__     				=    num_hidden_layers
								lowercase__     				=    num_attention_heads
								lowercase__     				=    intermediate_size

								lowercase__     				=    hidden_dropout_prob
								lowercase__     				=    attention_probs_dropout_prob
								lowercase__     				=    max_position_embeddings
								lowercase__     				=    eos_token_id
								lowercase__     				=    pad_token_id
								lowercase__     				=    bos_token_id




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size						).clip(3,						self.vocab_size						)
								lowercase__     				=    np.expand_dims(np.array([self.eos_token_id] * self.batch_size						),						1						)
								lowercase__     				=    np.concatenate([input_ids, eos_tensor],						axis=1						)

								lowercase__     				=    ids_tensor([self.batch_size, self.seq_length],						self.vocab_size						)

								lowercase__     				=    self.config_cls(
								    vocab_size=self.vocab_size,						d_model=self.hidden_size,						encoder_layers=self.num_hidden_layers,						decoder_layers=self.num_hidden_layers,						encoder_attention_heads=self.num_attention_heads,						decoder_attention_heads=self.num_attention_heads,						encoder_ffn_dim=self.intermediate_size,						decoder_ffn_dim=self.intermediate_size,						dropout=self.hidden_dropout_prob,						attention_dropout=self.attention_probs_dropout_prob,						max_position_embeddings=self.max_position_embeddings,						eos_token_ids=[2],						bos_token_id=self.bos_token_id,						pad_token_id=self.pad_token_id,						decoder_start_token_id=self.pad_token_id,						**self.config_updates,						)
								lowercase__     				=    prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						)
								return config, inputs_dict




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					int,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    20
								lowercase__     				=    model_class_name(SCREAMING_SNAKE_CASE_						)

								lowercase__     				=    model.encode(inputs_dict['''input_ids''']						)

								lowercase__				,		lowercase__     				=    (
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowercase__     				=    model.init_cache(decoder_input_ids.shape[0],						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						)
								lowercase__     				=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length),						dtype='''i4'''						)

								lowercase__     				=    jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :],						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),						)
								lowercase__     				=    model.decode(
								    decoder_input_ids[:, :-1],						SCREAMING_SNAKE_CASE_,						decoder_attention_mask=SCREAMING_SNAKE_CASE_,						past_key_values=SCREAMING_SNAKE_CASE_,						decoder_position_ids=SCREAMING_SNAKE_CASE_,						)

								lowercase__     				=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],						dtype='''i4'''						)
								lowercase__     				=    model.decode(
								    decoder_input_ids[:, -1:],						SCREAMING_SNAKE_CASE_,						decoder_attention_mask=SCREAMING_SNAKE_CASE_,						past_key_values=outputs_cache.past_key_values,						decoder_position_ids=SCREAMING_SNAKE_CASE_,						)

								lowercase__     				=    model.decode(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						)

								lowercase__     				=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
								self.parent.assertTrue(diff < 1E-3,						msg=F"""Max diff is {diff}"""						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    20
								lowercase__     				=    model_class_name(SCREAMING_SNAKE_CASE_						)

								lowercase__     				=    model.encode(inputs_dict['''input_ids''']						)

								lowercase__				,		lowercase__     				=    (
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowercase__     				=    jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
								    ],						axis=-1,						)

								lowercase__     				=    model.init_cache(decoder_input_ids.shape[0],						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						)
								lowercase__     				=    jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :],						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),						)

								lowercase__     				=    model.decode(
								    decoder_input_ids[:, :-1],						SCREAMING_SNAKE_CASE_,						decoder_attention_mask=SCREAMING_SNAKE_CASE_,						past_key_values=SCREAMING_SNAKE_CASE_,						decoder_position_ids=SCREAMING_SNAKE_CASE_,						)
								lowercase__     				=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],						dtype='''i4'''						)
								lowercase__     				=    model.decode(
								    decoder_input_ids[:, -1:],						SCREAMING_SNAKE_CASE_,						past_key_values=outputs_cache.past_key_values,						decoder_attention_mask=SCREAMING_SNAKE_CASE_,						decoder_position_ids=SCREAMING_SNAKE_CASE_,						)

								lowercase__     				=    model.decode(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						decoder_attention_mask=SCREAMING_SNAKE_CASE_						)

								lowercase__     				=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
								self.parent.assertTrue(diff < 1E-3,						msg=F"""Max diff is {diff}"""						)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=None	,     lowerCamelCase_=None	,     ):






				'''simple docstring'''




				if attention_mask is None:
								lowercase__     				=    np.not_equal(__a	,     config.pad_token_id       ).astype(np.inta       )
				if decoder_attention_mask is None:
								lowercase__     				=    np.concatenate(
								    [
								        np.ones(decoder_input_ids[:, :1].shape	,     dtype=np.inta       ),
								        np.not_equal(decoder_input_ids[:, 1:]	,     config.pad_token_id       ).astype(np.inta       ),
								    ]	,     axis=-1	,     )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				}




@require_flax
class    _UpperCAmelCase      ( __lowerCamelCase	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= (
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				lowercase__       							= (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				lowercase__       							= True
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    FlaxPegasusModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=SCREAMING_SNAKE_CASE_						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__						):
																lowercase__     				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						)
																lowercase__     				=    model_class(SCREAMING_SNAKE_CASE_						)

																@jax.jit
																def encode_jitted(lowerCamelCase		:					Tuple,						lowerCamelCase		:					Optional[int]=None,						**lowerCamelCase		:					Union[str, Any]						):
																				return model.encode(input_ids=SCREAMING_SNAKE_CASE_,						attention_mask=SCREAMING_SNAKE_CASE_						)

																with self.subTest('''JIT Enabled'''						):
																				lowercase__     				=    encode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																with self.subTest('''JIT Disabled'''						):
																				with jax.disable_jit():
																								lowercase__     				=    encode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																self.assertEqual(len(SCREAMING_SNAKE_CASE_						),						len(SCREAMING_SNAKE_CASE_						)						)
																for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						):
																				self.assertEqual(jitted_output.shape,						output.shape						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__						):
																lowercase__     				=    model_class(SCREAMING_SNAKE_CASE_						)
																lowercase__     				=    model.encode(inputs_dict['''input_ids'''],						inputs_dict['''attention_mask''']						)

																lowercase__     				=    {
																    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																    '''encoder_outputs''': encoder_outputs,
																}

																@jax.jit
																def decode_jitted(lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					str						):
																				return model.decode(
																				    decoder_input_ids=SCREAMING_SNAKE_CASE_,						decoder_attention_mask=SCREAMING_SNAKE_CASE_,						encoder_outputs=SCREAMING_SNAKE_CASE_,						)

																with self.subTest('''JIT Enabled'''						):
																				lowercase__     				=    decode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																with self.subTest('''JIT Disabled'''						):
																				with jax.disable_jit():
																								lowercase__     				=    decode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																self.assertEqual(len(SCREAMING_SNAKE_CASE_						),						len(SCREAMING_SNAKE_CASE_						)						)
																for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_						):
																				self.assertEqual(jitted_output.shape,						output.shape						)




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								for model_class_name in self.all_model_classes:
												lowercase__     				=    model_class_name.from_pretrained('''google/pegasus-large''',						from_pt=SCREAMING_SNAKE_CASE_						)
												lowercase__     				=    np.ones((1, 1)						)
												lowercase__     				=    model(SCREAMING_SNAKE_CASE_						)
												self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)




				@slow
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''						)
								lowercase__     				=    PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''						)

								lowercase__     				=    [
								    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
								    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ''',
								]

								lowercase__     				=    [
								    '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
								    '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
								]

								lowercase__     				=    tokenizer(SCREAMING_SNAKE_CASE_,						return_tensors='''np''',						truncation=SCREAMING_SNAKE_CASE_,						max_length=512,						padding=SCREAMING_SNAKE_CASE_						)
								lowercase__     				=    model.generate(**SCREAMING_SNAKE_CASE_,						num_beams=2						).sequences
								lowercase__     				=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,						skip_special_tokens=SCREAMING_SNAKE_CASE_						)
								assert tgt_text == decoded












# 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      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = 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'''						)

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (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
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (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
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

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

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

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




