



'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case_				:			Dict						 =						logging.get_logger(__name__)

snake_case_				:			List[Any]						 =						{
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class 							lowercase__			(							lowercase     ):
	lowercase__						       =    """mbart"""
	lowercase__						       =    ["""past_key_values"""]
	lowercase__						       =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





	def __init__(				self      :       Optional[Any]				,lowerCamelCase__      :       Optional[int]=50265				,lowerCamelCase__      :       int=1024				,lowerCamelCase__      :       Any=12				,lowerCamelCase__      :       Optional[int]=4096				,lowerCamelCase__      :       List[Any]=16				,lowerCamelCase__      :       Union[str, Any]=12				,lowerCamelCase__      :       List[Any]=4096				,lowerCamelCase__      :       Union[str, Any]=16				,lowerCamelCase__      :       List[str]=0.0				,lowerCamelCase__      :       int=0.0				,lowerCamelCase__      :       Dict=True				,lowerCamelCase__      :       Union[str, Any]=True				,lowerCamelCase__      :       Optional[Any]="gelu"				,lowerCamelCase__      :       Any=1024				,lowerCamelCase__      :       List[Any]=0.1				,lowerCamelCase__      :       Union[str, Any]=0.0				,lowerCamelCase__      :       Union[str, Any]=0.0				,lowerCamelCase__      :       Optional[Any]=0.0_2				,lowerCamelCase__      :       str=0.0				,lowerCamelCase__      :       List[Any]=False				,lowerCamelCase__      :       List[Any]=1				,lowerCamelCase__      :       Dict=0				,lowerCamelCase__      :       Union[str, Any]=2				,lowerCamelCase__      :       List[Any]=2				,**lowerCamelCase__      :       Any				,):







							'''simple docstring'''
							_UpperCamelCase      :	Union[str, Any]				       =     vocab_size
							_UpperCamelCase      :	Optional[Any]				       =     max_position_embeddings
							_UpperCamelCase      :	Optional[int]				       =     d_model
							_UpperCamelCase      :	int				       =     encoder_ffn_dim
							_UpperCamelCase      :	str				       =     encoder_layers
							_UpperCamelCase      :	Tuple				       =     encoder_attention_heads
							_UpperCamelCase      :	Optional[Any]				       =     decoder_ffn_dim
							_UpperCamelCase      :	List[Any]				       =     decoder_layers
							_UpperCamelCase      :	Any				       =     decoder_attention_heads
							_UpperCamelCase      :	Union[str, Any]				       =     dropout
							_UpperCamelCase      :	int				       =     attention_dropout
							_UpperCamelCase      :	int				       =     activation_dropout
							_UpperCamelCase      :	Tuple				       =     activation_function
							_UpperCamelCase      :	List[str]				       =     init_std
							_UpperCamelCase      :	List[Any]				       =     encoder_layerdrop
							_UpperCamelCase      :	Tuple				       =     decoder_layerdrop
							_UpperCamelCase      :	Tuple				       =     classifier_dropout
							_UpperCamelCase      :	str				       =     use_cache
							_UpperCamelCase      :	Optional[int]				       =     encoder_layers
							_UpperCamelCase      :	str				       =     scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=lowerCamelCase__				,bos_token_id=lowerCamelCase__				,eos_token_id=lowerCamelCase__				,is_encoder_decoder=lowerCamelCase__				,forced_eos_token_id=lowerCamelCase__				,**lowerCamelCase__				,)






class 							lowercase__			(							lowercase     ):





	@property
	def 			UpperCamelCase_						(				self      :       Optional[Any]				):







							'''simple docstring'''
							if self.task in ["default", "seq2seq-lm"]:
													_UpperCamelCase      :	str				       =     OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]				)

													if self.use_past:
																			_UpperCamelCase      :	Tuple				       =     {0: 'batch'}
																			_UpperCamelCase      :	List[Any]				       =     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																			_UpperCamelCase      :	List[Any]				       =     {0: 'batch', 1: 'decoder_sequence'}
																			_UpperCamelCase      :	int				       =     {0: 'batch', 1: 'decoder_sequence'}

													if self.use_past:
																			self.fill_with_past_key_values_(lowerCamelCase__				,direction='inputs'				)
							elif self.task == "causal-lm":
													# TODO: figure this case out.
													_UpperCamelCase      :	Optional[int]				       =     OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]				)
													if self.use_past:
																			_UpperCamelCase       ,    _UpperCamelCase      :	List[str]				       =     self.num_layers
																			for i in range(lowerCamelCase__				):
																									_UpperCamelCase      :	List[Any]				       =     {0: 'batch', 2: 'past_sequence + sequence'}
																									_UpperCamelCase      :	List[Any]				       =     {0: 'batch', 2: 'past_sequence + sequence'}
							else:
													_UpperCamelCase      :	int				       =     OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
													        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
													    ]				)

							return common_inputs





	@property
	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							if self.task in ["default", "seq2seq-lm"]:
													_UpperCamelCase      :	Optional[Any]				       =     super().outputs
							else:
													_UpperCamelCase      :	List[str]				       =     super(lowerCamelCase__				,self				).outputs
													if self.use_past:
																			_UpperCamelCase       ,    _UpperCamelCase      :	Any				       =     self.num_layers
																			for i in range(lowerCamelCase__				):
																									_UpperCamelCase      :	List[Any]				       =     {0: 'batch', 2: 'past_sequence + sequence'}
																									_UpperCamelCase      :	Optional[Any]				       =     {0: 'batch', 2: 'past_sequence + sequence'}
							return common_outputs





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       PreTrainedTokenizer				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       bool = False				,lowerCamelCase__      :       Optional[TensorType] = None				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)

							# Generate decoder inputs
							_UpperCamelCase      :	str				       =     seq_length if not self.use_past else 1
							_UpperCamelCase      :	Any				       =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)
							_UpperCamelCase      :	Tuple				       =     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
							_UpperCamelCase      :	List[Any]				       =     dict(**lowerCamelCase__				,**lowerCamelCase__				)

							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
													else:
																			import torch
													_UpperCamelCase       ,    _UpperCamelCase      :	Any				       =     common_inputs['input_ids'].shape
													_UpperCamelCase      :	int				       =     common_inputs['decoder_input_ids'].shape[1]
													_UpperCamelCase       ,    _UpperCamelCase      :	Optional[Any]				       =     self.num_attention_heads
													_UpperCamelCase      :	List[str]				       =     (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													_UpperCamelCase      :	str				       =     decoder_seq_length + 3
													_UpperCamelCase      :	Optional[int]				       =     (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													_UpperCamelCase      :	Optional[int]				       =     torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(lowerCamelCase__				,lowerCamelCase__				)]				,dim=1				)

													_UpperCamelCase      :	Any				       =     []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													_UpperCamelCase       ,    _UpperCamelCase      :	int				       =     self.num_layers
													_UpperCamelCase      :	Optional[int]				       =     min(lowerCamelCase__				,lowerCamelCase__				)
													_UpperCamelCase      :	Tuple				       =     max(lowerCamelCase__				,lowerCamelCase__				) - min_num_layers
													_UpperCamelCase      :	List[Any]				       =     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

													for _ in range(lowerCamelCase__				):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(lowerCamelCase__				),
																			        torch.zeros(lowerCamelCase__				),
																			        torch.zeros(lowerCamelCase__				),
																			        torch.zeros(lowerCamelCase__				),
																			    )				)
													# TODO: test this.
													_UpperCamelCase      :	Optional[Any]				       =     encoder_shape if remaining_side_name == 'encoder' else decoder_shape
													for _ in range(lowerCamelCase__				,lowerCamelCase__				):
																			common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase__				), torch.zeros(lowerCamelCase__				))				)
							return common_inputs





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       PreTrainedTokenizer				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       bool = False				,lowerCamelCase__      :       Optional[TensorType] = None				,):







							'''simple docstring'''
							_UpperCamelCase      :	Union[str, Any]				       =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)

							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
													else:
																			import torch
													_UpperCamelCase       ,    _UpperCamelCase      :	int				       =     common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													_UpperCamelCase      :	List[str]				       =     seqlen + 2
													_UpperCamelCase       ,    _UpperCamelCase      :	List[Any]				       =     self.num_layers
													_UpperCamelCase       ,    _UpperCamelCase      :	List[str]				       =     self.num_attention_heads
													_UpperCamelCase      :	Tuple				       =     (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													_UpperCamelCase      :	List[str]				       =     common_inputs['attention_mask'].dtype
													_UpperCamelCase      :	Any				       =     torch.cat(
													    [common_inputs['attention_mask'], torch.ones(lowerCamelCase__				,lowerCamelCase__				,dtype=lowerCamelCase__				)]				,dim=1				)
													_UpperCamelCase      :	Any				       =     [
													    (torch.zeros(lowerCamelCase__				), torch.zeros(lowerCamelCase__				)) for _ in range(lowerCamelCase__				)
													]
							return common_inputs





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       PreTrainedTokenizer				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       bool = False				,lowerCamelCase__      :       Optional[TensorType] = None				,):







							'''simple docstring'''
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							_UpperCamelCase      :	Optional[int]				       =     compute_effective_axis_dimension(
							    lowerCamelCase__				,fixed_dimension=OnnxConfig.default_fixed_batch				,num_token_to_add=0				)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							_UpperCamelCase      :	Any				       =     tokenizer.num_special_tokens_to_add(lowerCamelCase__				)
							_UpperCamelCase      :	Union[str, Any]				       =     compute_effective_axis_dimension(
							    lowerCamelCase__				,fixed_dimension=OnnxConfig.default_fixed_sequence				,num_token_to_add=lowerCamelCase__				)

							# Generate dummy inputs according to compute batch and sequence
							_UpperCamelCase      :	Tuple				       =     [' '.join([tokenizer.unk_token]				) * seq_length] * batch_size
							_UpperCamelCase      :	Union[str, Any]				       =     dict(tokenizer(lowerCamelCase__				,return_tensors=lowerCamelCase__				)				)
							return common_inputs





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       PreTrainedTokenizer				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       int = -1				,lowerCamelCase__      :       bool = False				,lowerCamelCase__      :       Optional[TensorType] = None				,):







							'''simple docstring'''
							if self.task in ["default", "seq2seq-lm"]:
													_UpperCamelCase      :	Any				       =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    lowerCamelCase__				,batch_size=lowerCamelCase__				,seq_length=lowerCamelCase__				,is_pair=lowerCamelCase__				,framework=lowerCamelCase__				)

							elif self.task == "causal-lm":
													_UpperCamelCase      :	List[str]				       =     self._generate_dummy_inputs_for_causal_lm(
													    lowerCamelCase__				,batch_size=lowerCamelCase__				,seq_length=lowerCamelCase__				,is_pair=lowerCamelCase__				,framework=lowerCamelCase__				)
							else:
													_UpperCamelCase      :	List[str]				       =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCamelCase__				,batch_size=lowerCamelCase__				,seq_length=lowerCamelCase__				,is_pair=lowerCamelCase__				,framework=lowerCamelCase__				)

							return common_inputs





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       Union[str, Any]				,lowerCamelCase__      :       Tuple				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							if self.task in ["default", "seq2seq-lm"]:
													_UpperCamelCase      :	Optional[int]				       =     super()._flatten_past_key_values_(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)
							else:
													_UpperCamelCase      :	str				       =     super(lowerCamelCase__				,self				)._flatten_past_key_values_(
													    lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)







'''simple docstring'''
import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							lowercase__			(							lowercase     ):
	lowercase__						       =    42
	lowercase__						       =    None






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_=0.999						,						UpperCAmelCase_="cosine"						,						):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCAmelCase_							):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

						elif alpha_transform_type == "exp":

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

						else:
												raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

						_UpperCamelCase      :	Tuple				       =     []
						for i in range(UpperCAmelCase_							):
												_UpperCamelCase      :	List[str]				       =     i / num_diffusion_timesteps
												_UpperCamelCase      :	Optional[int]				       =     (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__			(							lowercase		,      lowercase     ):
	lowercase__						       =    [e.name for e in KarrasDiffusionSchedulers]
	lowercase__						       =    1





	@register_to_config
	def __init__(				self      :       int				,lowerCamelCase__      :       int = 1000				,lowerCamelCase__      :       float = 0.0_0_0_1				,lowerCamelCase__      :       float = 0.0_2				,lowerCamelCase__      :       str = "linear"				,lowerCamelCase__      :       Optional[Union[np.ndarray, List[float]]] = None				,lowerCamelCase__      :       Optional[bool] = False				,lowerCamelCase__      :       str = "epsilon"				,lowerCamelCase__      :       str = "linspace"				,lowerCamelCase__      :       int = 0				,):







							'''simple docstring'''
							if trained_betas is not None:
													_UpperCamelCase      :	Union[str, Any]				       =     torch.tensor(lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "linear":
													_UpperCamelCase      :	Union[str, Any]				       =     torch.linspace(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCamelCase      :	Optional[int]				       =     (
													    torch.linspace(beta_start**0.5				,beta_end**0.5				,lowerCamelCase__				,dtype=torch.floataa				) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													_UpperCamelCase      :	Dict				       =     betas_for_alpha_bar(lowerCamelCase__				)
							else:
													raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

							_UpperCamelCase      :	List[Any]				       =     1.0 - self.betas
							_UpperCamelCase      :	int				       =     torch.cumprod(self.alphas				,dim=0				)

							_UpperCamelCase      :	Optional[int]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	Any				       =     np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
							_UpperCamelCase      :	Optional[int]				       =     torch.from_numpy(lowerCamelCase__				)

							# setable values
							_UpperCamelCase      :	List[str]				       =     None
							_UpperCamelCase      :	List[Any]				       =     use_karras_sigmas
							self.set_timesteps(lowerCamelCase__				,lowerCamelCase__				)
							_UpperCamelCase      :	Any				       =     []
							_UpperCamelCase      :	Tuple				       =     False





	@property
	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

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





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				):







							'''simple docstring'''
							if isinstance(lowerCamelCase__				,torch.Tensor				):
													_UpperCamelCase      :	List[str]				       =     timestep.to(self.timesteps.device				)
							_UpperCamelCase      :	Optional[Any]				       =     (self.timesteps == timestep).nonzero().item()
							_UpperCamelCase      :	Any				       =     self.sigmas[step_index]
							_UpperCamelCase      :	Dict				       =     sample / ((sigma**2 + 1) ** 0.5)
							_UpperCamelCase      :	Optional[Any]				       =     True
							return sample





	def 			UpperCamelCase_						(				self      :       List[Any]				,lowerCamelCase__      :       Optional[int]				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''

							def lms_derivative(lowerCamelCase__      :       str				):
													_UpperCamelCase      :	Any				       =     1.0
													for k in range(lowerCamelCase__				):
																			if current_order == k:
																									continue
																			prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
													return prod

							_UpperCamelCase      :	Union[str, Any]				       =     integrate.quad(lowerCamelCase__				,self.sigmas[t]				,self.sigmas[t + 1]				,epsrel=1E-4				)[0]

							return integrated_coeff





	def 			UpperCamelCase_						(				self      :       Dict				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Union[str, torch.device] = None				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[int]				       =     num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													_UpperCamelCase      :	int				       =     np.linspace(0				,self.config.num_train_timesteps - 1				,lowerCamelCase__				,dtype=lowerCamelCase__				)[
													    ::-1
													].copy()
							elif self.config.timestep_spacing == "leading":
													_UpperCamelCase      :	Optional[Any]				       =     self.config.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
													_UpperCamelCase      :	str				       =     (np.arange(0				,lowerCamelCase__				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__				)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase      :	Optional[Any]				       =     self.config.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
													_UpperCamelCase      :	Optional[Any]				       =     (np.arange(self.config.num_train_timesteps				,0				,-step_ratio				)).round().copy().astype(lowerCamelCase__				)
													timesteps -= 1
							else:
													raise ValueError(
													    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

							_UpperCamelCase      :	Optional[Any]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	Optional[Any]				       =     np.log(lowerCamelCase__				)
							_UpperCamelCase      :	Dict				       =     np.interp(lowerCamelCase__				,np.arange(0				,len(lowerCamelCase__				)				)				,lowerCamelCase__				)

							if self.use_karras_sigmas:
													_UpperCamelCase      :	Optional[int]				       =     self._convert_to_karras(in_sigmas=lowerCamelCase__				)
													_UpperCamelCase      :	str				       =     np.array([self._sigma_to_t(lowerCamelCase__				,lowerCamelCase__				) for sigma in sigmas]				)

							_UpperCamelCase      :	List[str]				       =     np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)

							_UpperCamelCase      :	Any				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)
							if str(lowerCamelCase__				).startswith('mps'				):
													# mps does not support float64
													_UpperCamelCase      :	Optional[Any]				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Any				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)

							_UpperCamelCase      :	Union[str, Any]				       =     []





	def 			UpperCamelCase_						(				self      :       Any				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       List[str]				):







							'''simple docstring'''
							# get log sigma
							_UpperCamelCase      :	Tuple				       =     np.log(lowerCamelCase__				)

							# get distribution
							_UpperCamelCase      :	Dict				       =     log_sigma - log_sigmas[:, np.newaxis]

							# get sigmas range
							_UpperCamelCase      :	Optional[int]				       =     np.cumsum((dists >= 0)				,axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
							_UpperCamelCase      :	str				       =     low_idx + 1

							_UpperCamelCase      :	Optional[int]				       =     log_sigmas[low_idx]
							_UpperCamelCase      :	Union[str, Any]				       =     log_sigmas[high_idx]

							# interpolate sigmas
							_UpperCamelCase      :	List[Any]				       =     (low - log_sigma) / (low - high)
							_UpperCamelCase      :	int				       =     np.clip(lowerCamelCase__				,0				,1				)

							# transform interpolation to time range
							_UpperCamelCase      :	Dict				       =     (1 - w) * low_idx + w * high_idx
							_UpperCamelCase      :	Dict				       =     t.reshape(sigma.shape				)
							return t





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       torch.FloatTensor				):







							'''simple docstring'''
							_UpperCamelCase      :	float				       =     in_sigmas[-1].item()
							_UpperCamelCase      :	float				       =     in_sigmas[0].item()

							_UpperCamelCase      :	List[str]				       =     7.0  # 7.0 is the value used in the paper
							_UpperCamelCase      :	str				       =     np.linspace(0				,1				,self.num_inference_steps				)
							_UpperCamelCase      :	Dict				       =     sigma_min ** (1 / rho)
							_UpperCamelCase      :	Optional[Any]				       =     sigma_max ** (1 / rho)
							_UpperCamelCase      :	int				       =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       int = 4				,lowerCamelCase__      :       bool = True				,):







							'''simple docstring'''
							if not self.is_scale_input_called:
													warnings.warn(
													    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
													    'See `StableDiffusionPipeline` for a usage example.'				)

							if isinstance(lowerCamelCase__				,torch.Tensor				):
													_UpperCamelCase      :	Union[str, Any]				       =     timestep.to(self.timesteps.device				)
							_UpperCamelCase      :	Any				       =     (self.timesteps == timestep).nonzero().item()
							_UpperCamelCase      :	Optional[int]				       =     self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													_UpperCamelCase      :	Dict				       =     sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													_UpperCamelCase      :	str				       =     model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							elif self.config.prediction_type == "sample":
													_UpperCamelCase      :	Any				       =     model_output
							else:
													raise ValueError(
													    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

							# 2. Convert to an ODE derivative
							_UpperCamelCase      :	List[Any]				       =     (sample - pred_original_sample) / sigma
							self.derivatives.append(lowerCamelCase__				)
							if len(self.derivatives				) > order:
													self.derivatives.pop(0				)

							# 3. Compute linear multistep coefficients
							_UpperCamelCase      :	List[Any]				       =     min(step_index + 1				,lowerCamelCase__				)
							_UpperCamelCase      :	Optional[int]				       =     [self.get_lms_coefficient(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				) for curr_order in range(lowerCamelCase__				)]

							# 4. Compute previous sample based on the derivatives path
							_UpperCamelCase      :	int				       =     sample + sum(
							    coeff * derivative for coeff, derivative in zip(lowerCamelCase__				,reversed(self.derivatives				)				)				)

							if not return_dict:
													return (prev_sample,)

							return LMSDiscreteSchedulerOutput(prev_sample=lowerCamelCase__				,pred_original_sample=lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,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
							_UpperCamelCase      :	Tuple				       =     self.sigmas.to(device=original_samples.device				,dtype=original_samples.dtype				)
							if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__				):
													# mps does not support float64
													_UpperCamelCase      :	str				       =     self.timesteps.to(original_samples.device				,dtype=torch.floataa				)
													_UpperCamelCase      :	Optional[Any]				       =     timesteps.to(original_samples.device				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Union[str, Any]				       =     self.timesteps.to(original_samples.device				)
													_UpperCamelCase      :	Union[str, Any]				       =     timesteps.to(original_samples.device				)

							_UpperCamelCase      :	Union[str, Any]				       =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

							_UpperCamelCase      :	str				       =     original_samples + noise * sigma
							return noisy_samples





	def __len__(				self      :       List[Any]				):







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


