






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class      lowercase__     :
	a_         =PegasusConfig
	a_         ={}
	a_         ="""gelu"""

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=7		,    __UpperCAmelCase=True		,    __UpperCAmelCase=False		,    __UpperCAmelCase=99		,    __UpperCAmelCase=32		,    __UpperCAmelCase=2		,    __UpperCAmelCase=4		,    __UpperCAmelCase=37		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=40		,    __UpperCAmelCase=2		,    __UpperCAmelCase=1		,    __UpperCAmelCase=0		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						seq_length
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						vocab_size
							lowerCAmelCase__       =						hidden_size
							lowerCAmelCase__       =						num_hidden_layers
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						intermediate_size

							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						max_position_embeddings
							lowerCAmelCase__       =						eos_token_id
							lowerCAmelCase__       =						pad_token_id
							lowerCAmelCase__       =						bos_token_id

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length - 1]		,    self.vocab_size       )
							lowerCAmelCase__       =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )		,    1       )
							lowerCAmelCase__       =						tf.concat([input_ids, eos_tensor]		,    axis=1       )

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

							lowerCAmelCase__       =						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		,    )
							lowerCAmelCase__       =						prepare_pegasus_inputs_dict(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )
							return config, inputs_dict


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						TFPegasusModel(config=__UpperCAmelCase       ).get_decoder()
							lowerCAmelCase__       =						inputs_dict["input_ids"]

							lowerCAmelCase__       =						input_ids[:1, :]
							lowerCAmelCase__       =						inputs_dict["attention_mask"][:1, :]
							lowerCAmelCase__       =						inputs_dict["head_mask"]
							lowerCAmelCase__       =						1

							# first forward pass
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    head_mask=__UpperCAmelCase		,    use_cache=__UpperCAmelCase       )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__       =						ids_tensor((self.batch_size, 3)		,    config.vocab_size       )
							lowerCAmelCase__       =						tf.cast(ids_tensor((self.batch_size, 3)		,    2       )		,    tf.inta       )

							# append to next input_ids and
							lowerCAmelCase__       =						tf.concat([input_ids, next_tokens]		,    axis=-1       )
							lowerCAmelCase__       =						tf.concat([attention_mask, next_attn_mask]		,    axis=-1       )

							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase       )[0]
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    past_key_values=__UpperCAmelCase       )[0]

							self.parent.assertEqual(next_tokens.shape[1]		,    output_from_past.shape[1]       )

							# select random slice
							lowerCAmelCase__       =						int(ids_tensor((1,)		,    output_from_past.shape[-1]       )       )
							lowerCAmelCase__       =						output_from_no_past[:, -3:, random_slice_idx]
							lowerCAmelCase__       =						output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__UpperCAmelCase		,    __UpperCAmelCase		,    rtol=1E-3       )





def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Any=None    ,   UpperCamelCase_					:				int=None    ,   UpperCamelCase_					:				str=None    ,   UpperCamelCase_					:				Optional[Any]=None    ,   UpperCamelCase_					:				int=None    ,   ) ->   int:
						"""simple docstring"""







						if attention_mask is None:
												lowerCAmelCase__       =						tf.cast(tf.math.not_equal(UpperCamelCase_    ,   config.pad_token_id   )    ,   tf.inta   )
						if decoder_attention_mask is None:
												lowerCAmelCase__       =						tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape    ,   dtype=tf.inta   ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,   config.pad_token_id   )    ,   tf.inta   ),
												    ]    ,   axis=-1    ,   )
						if head_mask is None:
												lowerCAmelCase__       =						tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												lowerCAmelCase__       =						tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
						if cross_attn_head_mask is None:
												lowerCAmelCase__       =						tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}


@require_tf
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
	a_         =(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
	a_         =(
	    {
	        """conversational""": TFPegasusForConditionalGeneration,
	        """feature-extraction""": TFPegasusModel,
	        """summarization""": TFPegasusForConditionalGeneration,
	        """text2text-generation""": TFPegasusForConditionalGeneration,
	        """translation""": TFPegasusForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	a_         =True
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						TFPegasusModelTester(self       )
							lowerCAmelCase__       =						ConfigTester(self		,    config_class=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							self.config_tester.run_common_tests()


	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase       )





@require_sentencepiece
@require_tokenizers
@require_tf
class      lowercase__     (			unittest.TestCase	):
	a_         =[
	    """ 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!\" """,
	]
	a_         =[
	    """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
	    """ reduce the risk of wildfires.""",
	    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
	]  # differs slightly from pytorch, likely due to numerical differences in linear layers
	a_         ="""google/pegasus-xsum"""

	@cached_property
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							return AutoTokenizer.from_pretrained(self.model_name       )

	@cached_property
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
							return model

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.translate_src_text(**__UpperCAmelCase       )
							assert self.expected_text == generated_words

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						self.tokenizer(self.src_text		,    **__UpperCAmelCase		,    padding=__UpperCAmelCase		,    return_tensors="tf"       )
							lowerCAmelCase__       =						self.model.generate(
							    model_inputs.input_ids		,    attention_mask=model_inputs.attention_mask		,    num_beams=2		,    use_cache=__UpperCAmelCase		,    )
							lowerCAmelCase__       =						self.tokenizer.batch_decode(generated_ids.numpy()		,    skip_special_tokens=__UpperCAmelCase       )
							return generated_words


	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							self._assert_generated_batch_equal_expected()














import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Tuple=0.999    ,   UpperCamelCase_					:				Union[str, Any]="cosine"    ,   ) ->   str:
						"""simple docstring"""







						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCamelCase_					:				List[Any]   ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCamelCase_					:				int   ):
																		return math.exp(t * -12.0   )

						else:
												raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"   )

						lowerCAmelCase__       =						[]
						for i in range(UpperCamelCase_   ):
												lowerCAmelCase__       =						i / num_diffusion_timesteps
												lowerCAmelCase__       =						(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCamelCase_   ) / alpha_bar_fn(UpperCamelCase_   )    ,   UpperCamelCase_   )   )
						return torch.tensor(UpperCamelCase_    ,   dtype=torch.floataa   )





class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase	):
	a_         =[e.name for e in KarrasDiffusionSchedulers]
	a_         =2

	@register_to_config
	def __init__(							self		,    __UpperCAmelCase = 1000		,    __UpperCAmelCase = 0.00_085		,    __UpperCAmelCase = 0.012		,    __UpperCAmelCase = "linear"		,    __UpperCAmelCase = None		,    __UpperCAmelCase = "epsilon"		,    __UpperCAmelCase = "linspace"		,    __UpperCAmelCase = 0		,    )->					int:







							'''simple docstring'''





							if trained_betas is not None:
													lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    dtype=torch.floataa       )
							elif beta_schedule == "linear":
													lowerCAmelCase__       =						torch.linspace(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    dtype=torch.floataa       )
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													lowerCAmelCase__       =						(
													    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    __UpperCAmelCase		,    dtype=torch.floataa       ) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													lowerCAmelCase__       =						betas_for_alpha_bar(__UpperCAmelCase       )
							else:
													raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"       )

							lowerCAmelCase__       =						1.0 - self.betas
							lowerCAmelCase__       =						torch.cumprod(self.alphas		,    dim=0       )

							#  set all values
							self.set_timesteps(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=None       )->					str:







							'''simple docstring'''





							if schedule_timesteps is None:
													lowerCAmelCase__       =						self.timesteps

							lowerCAmelCase__       =						(schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter       ) == 0:
													lowerCAmelCase__       =						1 if len(__UpperCAmelCase       ) > 1 else 0
							else:
													lowerCAmelCase__       =						timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase       ) else timestep
													lowerCAmelCase__       =						self._index_counter[timestep_int]

							return indices[pos].item()

	@property
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					torch.FloatTensor:







							'''simple docstring'''





							lowerCAmelCase__       =						self.index_for_timestep(__UpperCAmelCase       )

							if self.state_in_first_order:
													lowerCAmelCase__       =						self.sigmas[step_index]
							else:
													lowerCAmelCase__       =						self.sigmas_interpol[step_index]

							lowerCAmelCase__       =						sample / ((sigma**2 + 1) ** 0.5)
							return sample

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						num_inference_steps

							lowerCAmelCase__       =						num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													lowerCAmelCase__       =						np.linspace(0		,    num_train_timesteps - 1		,    __UpperCAmelCase		,    dtype=__UpperCAmelCase       )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
													lowerCAmelCase__       =						num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													lowerCAmelCase__       =						(np.arange(0		,    __UpperCAmelCase       ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase       )
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													lowerCAmelCase__       =						num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													lowerCAmelCase__       =						(np.arange(__UpperCAmelCase		,    0		,    -step_ratio       )).round().copy().astype(__UpperCAmelCase       )
													timesteps -= 1
							else:
													raise ValueError(
													    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."       )

							lowerCAmelCase__       =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
							lowerCAmelCase__       =						torch.from_numpy(np.log(__UpperCAmelCase       )       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						np.interp(__UpperCAmelCase		,    np.arange(0		,    len(__UpperCAmelCase       )       )		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
							lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(device=__UpperCAmelCase       )

							# interpolate sigmas
							lowerCAmelCase__       =						sigmas.log().lerp(sigmas.roll(1       ).log()		,    0.5       ).exp()

							lowerCAmelCase__       =						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
							lowerCAmelCase__       =						torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

							if str(__UpperCAmelCase       ).startswith("mps"       ):
													# mps does not support float64
													lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(__UpperCAmelCase		,    dtype=torch.floataa       )
							else:
													lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(__UpperCAmelCase       )

							# interpolate timesteps
							lowerCAmelCase__       =						self.sigma_to_t(__UpperCAmelCase       ).to(__UpperCAmelCase		,    dtype=timesteps.dtype       )
							lowerCAmelCase__       =						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,    dim=-1       ).flatten()

							lowerCAmelCase__       =						torch.cat([timesteps[:1], interleaved_timesteps]       )

							lowerCAmelCase__       =						None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							lowerCAmelCase__       =						defaultdict(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						sigma.log()

							# get distribution
							lowerCAmelCase__       =						log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							lowerCAmelCase__       =						dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
							lowerCAmelCase__       =						low_idx + 1

							lowerCAmelCase__       =						self.log_sigmas[low_idx]
							lowerCAmelCase__       =						self.log_sigmas[high_idx]

							# interpolate sigmas
							lowerCAmelCase__       =						(low - log_sigma) / (low - high)
							lowerCAmelCase__       =						w.clamp(0		,    1       )

							# transform interpolation to time range
							lowerCAmelCase__       =						(1 - w) * low_idx + w * high_idx
							lowerCAmelCase__       =						t.view(sigma.shape       )
							return t

	@property
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							return self.sample is None

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = True		,    )->					Union[SchedulerOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.index_for_timestep(__UpperCAmelCase       )

							# advance index counter by 1
							lowerCAmelCase__       =						timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase       ) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
													lowerCAmelCase__       =						self.sigmas[step_index]
													lowerCAmelCase__       =						self.sigmas_interpol[step_index + 1]
													lowerCAmelCase__       =						self.sigmas[step_index + 1]
							else:
													# 2nd order / KDPM2's method
													lowerCAmelCase__       =						self.sigmas[step_index - 1]
													lowerCAmelCase__       =						self.sigmas_interpol[step_index]
													lowerCAmelCase__       =						self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							lowerCAmelCase__       =						0
							lowerCAmelCase__       =						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													lowerCAmelCase__       =						sigma_hat if self.state_in_first_order else sigma_interpol
													lowerCAmelCase__       =						sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
													lowerCAmelCase__       =						sigma_hat if self.state_in_first_order else sigma_interpol
													lowerCAmelCase__       =						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
							elif self.config.prediction_type == "sample":
													raise NotImplementedError("prediction_type not implemented yet: sample"       )
							else:
													raise ValueError(
													    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"       )

							if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													lowerCAmelCase__       =						(sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													lowerCAmelCase__       =						sigma_interpol - sigma_hat

													# store for 2nd order step
													lowerCAmelCase__       =						sample
							else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													lowerCAmelCase__       =						(sample - pred_original_sample) / sigma_interpol

													# 3. delta timestep
													lowerCAmelCase__       =						sigma_next - sigma_hat

													lowerCAmelCase__       =						self.sample
													lowerCAmelCase__       =						None

							lowerCAmelCase__       =						sample + derivative * dt

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					torch.FloatTensor:







							'''simple docstring'''





							lowerCAmelCase__       =						self.sigmas.to(device=original_samples.device		,    dtype=original_samples.dtype       )
							if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase       ):
													# mps does not support float64
													lowerCAmelCase__       =						self.timesteps.to(original_samples.device		,    dtype=torch.floataa       )
													lowerCAmelCase__       =						timesteps.to(original_samples.device		,    dtype=torch.floataa       )
							else:
													lowerCAmelCase__       =						self.timesteps.to(original_samples.device       )
													lowerCAmelCase__       =						timesteps.to(original_samples.device       )

							lowerCAmelCase__       =						[self.index_for_timestep(__UpperCAmelCase		,    __UpperCAmelCase       ) for t in timesteps]

							lowerCAmelCase__       =						sigmas[step_indices].flatten()
							while len(sigma.shape       ) < len(original_samples.shape       ):
													lowerCAmelCase__       =						sigma.unsqueeze(-1       )

							lowerCAmelCase__       =						original_samples + noise * sigma
							return noisy_samples


	def __len__(							self       )->					List[Any]:







							'''simple docstring'''





							return self.config.num_train_timesteps






