



"""simple docstring"""


import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_ = 4096		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					key.shape[-3:]
						UpperCAmelCase			 =					value.shape[-1]
						UpperCAmelCase			 =					min(lowercase_						,      lowercase_		)
						UpperCAmelCase			 =					query / jnp.sqrt(lowercase_		)

						@functools.partial(jax.checkpoint						,      prevent_cse=lowercase_		)
						def summarize_chunk(lowercase_						,      lowercase_						,      lowercase_		):
												UpperCAmelCase			 =					jnp.einsum('...qhd,...khd->...qhk'						,      lowercase_						,      lowercase_						,      precision=lowercase_		)

												UpperCAmelCase			 =					jnp.max(lowercase_						,      axis=-1						,      keepdims=lowercase_		)
												UpperCAmelCase			 =					jax.lax.stop_gradient(lowercase_		)
												UpperCAmelCase			 =					jnp.exp(attn_weights - max_score		)

												UpperCAmelCase			 =					jnp.einsum('...vhf,...qhv->...qhf'						,      lowercase_						,      lowercase_						,      precision=lowercase_		)
												UpperCAmelCase			 =					jnp.einsum('...qhk->...qh'						,      lowercase_		)

												return (exp_values, exp_weights.sum(axis=-1		), max_score)

						def chunk_scanner(lowercase_		):
												# julienne key array
												UpperCAmelCase			 =					jax.lax.dynamic_slice(
												    operand=lowercase_						,      start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]						,      slice_sizes=list(key.shape[:-3]		) + [key_chunk_size, num_heads, k_features]						,      )

												# julienne value array
												UpperCAmelCase			 =					jax.lax.dynamic_slice(
												    operand=lowercase_						,      start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]						,      slice_sizes=list(value.shape[:-3]		) + [key_chunk_size, num_heads, v_features]						,      )

												return summarize_chunk(lowercase_						,      lowercase_						,      lowercase_		)

						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					jax.lax.map(f=lowercase_						,      xs=jnp.arange(0						,      lowercase_						,      lowercase_		)		)

						UpperCAmelCase			 =					jnp.max(lowercase_						,      axis=0						,      keepdims=lowercase_		)
						UpperCAmelCase			 =					jnp.exp(chunk_max - global_max		)

						chunk_values *= jnp.expand_dims(lowercase_						,      axis=-1		)
						chunk_weights *= max_diffs

						UpperCAmelCase			 =					chunk_values.sum(axis=0		)
						UpperCAmelCase			 =					jnp.expand_dims(lowercase_						,      -1		).sum(axis=0		)

						return all_values / all_weights

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_=jax.lax.Precision.HIGHEST						,      lowercase_ = 1024						,      lowercase_ = 4096		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					query.shape[-3:]

						def chunk_scanner(lowercase_						,      lowercase_		):
												# julienne query array
												UpperCAmelCase			 =					jax.lax.dynamic_slice(
												    operand=lowercase_						,      start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]						,      slice_sizes=list(query.shape[:-3]		) + [min(lowercase_						,      lowercase_		), num_heads, q_features]						,      )

												return (
												    chunk_idx + query_chunk_size,  # unused ignore it
												    _query_chunk_attention(
												        query=lowercase_						,      key=lowercase_						,      value=lowercase_						,      precision=lowercase_						,      key_chunk_size=lowercase_		),
												)

						UpperCAmelCase ,       UpperCAmelCase			 =					jax.lax.scan(
						    f=lowercase_						,      init=0						,      xs=lowercase_						,      length=math.ceil(num_q / query_chunk_size		)		)

						return jnp.concatenate(lowercase_						,      axis=-3		)  # fuse the chunked result back







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				8
						__UpperCamelCase													=				64
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Any   )							->     Dict:
												UpperCAmelCase			 =					self.dim_head * self.heads
												UpperCAmelCase			 =					self.dim_head**-0.5

												# Weights were exported with old names {to_q, to_k, to_v, to_out}
												UpperCAmelCase			 =					nn.Dense(lowercase_  ,    use_bias=lowercase_  ,    dtype=self.dtype  ,    name='to_q'   )
												UpperCAmelCase			 =					nn.Dense(lowercase_  ,    use_bias=lowercase_  ,    dtype=self.dtype  ,    name='to_k'   )
												UpperCAmelCase			 =					nn.Dense(lowercase_  ,    use_bias=lowercase_  ,    dtype=self.dtype  ,    name='to_v'   )

												UpperCAmelCase			 =					nn.Dense(self.query_dim  ,    dtype=self.dtype  ,    name='to_out_0'   )
												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:str   )							->     str:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					tensor.shape
												UpperCAmelCase			 =					self.heads
												UpperCAmelCase			 =					tensor.reshape(lowercase_  ,    lowercase_  ,    lowercase_  ,    dim // head_size   )
												UpperCAmelCase			 =					jnp.transpose(lowercase_  ,    (0, 2, 1, 3)   )
												UpperCAmelCase			 =					tensor.reshape(batch_size * head_size  ,    lowercase_  ,    dim // head_size   )
												return tensor






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Any   )							->     Optional[Any]:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					tensor.shape
												UpperCAmelCase			 =					self.heads
												UpperCAmelCase			 =					tensor.reshape(batch_size // head_size  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					jnp.transpose(lowercase_  ,    (0, 2, 1, 3)   )
												UpperCAmelCase			 =					tensor.reshape(batch_size // head_size  ,    lowercase_  ,    dim * head_size   )
												return tensor






						def __call__(   self					:Optional[int]  ,    lowercase_					:int  ,    lowercase_					:Tuple=None  ,    lowercase_					:List[Any]=True   )							->     Optional[int]:
												UpperCAmelCase			 =					hidden_states if context is None else context

												UpperCAmelCase			 =					self.query(lowercase_   )
												UpperCAmelCase			 =					self.key(lowercase_   )
												UpperCAmelCase			 =					self.value(lowercase_   )

												UpperCAmelCase			 =					self.reshape_heads_to_batch_dim(lowercase_   )
												UpperCAmelCase			 =					self.reshape_heads_to_batch_dim(lowercase_   )
												UpperCAmelCase			 =					self.reshape_heads_to_batch_dim(lowercase_   )

												if self.use_memory_efficient_attention:
																		UpperCAmelCase			 =					query_states.transpose(1  ,    0  ,    2   )
																		UpperCAmelCase			 =					key_states.transpose(1  ,    0  ,    2   )
																		UpperCAmelCase			 =					value_states.transpose(1  ,    0  ,    2   )

																		# this if statement create a chunk size for each layer of the unet
																		# the chunk size is equal to the query_length dimension of the deepest layer of the unet

																		UpperCAmelCase			 =					query_states.shape[-3]
																		if flatten_latent_dim % 64 == 0:
																								UpperCAmelCase			 =					int(flatten_latent_dim / 64   )
																		elif flatten_latent_dim % 16 == 0:
																								UpperCAmelCase			 =					int(flatten_latent_dim / 16   )
																		elif flatten_latent_dim % 4 == 0:
																								UpperCAmelCase			 =					int(flatten_latent_dim / 4   )
																		else:
																								UpperCAmelCase			 =					int(lowercase_   )

																		UpperCAmelCase			 =					jax_memory_efficient_attention(
																		    lowercase_  ,    lowercase_  ,    lowercase_  ,    query_chunk_size=lowercase_  ,    key_chunk_size=40_96 * 4   )

																		UpperCAmelCase			 =					hidden_states.transpose(1  ,    0  ,    2   )
												else:
																		# compute attentions
																		UpperCAmelCase			 =					jnp.einsum('b i d, b j d->b i j'  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					attention_scores * self.scale
																		UpperCAmelCase			 =					nn.softmax(lowercase_  ,    axis=2   )

																		# attend to values
																		UpperCAmelCase			 =					jnp.einsum('b i j, b j d -> b i d'  ,    lowercase_  ,    lowercase_   )

												UpperCAmelCase			 =					self.reshape_batch_dim_to_heads(lowercase_   )
												UpperCAmelCase			 =					self.proj_attn(lowercase_   )
												return self.dropout_layer(lowercase_  ,    deterministic=lowercase_   )







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Tuple:
												# self attention (or cross_attention if only_cross_attention is True)
												UpperCAmelCase			 =					FlaxAttention(
												    self.dim  ,    self.n_heads  ,    self.d_head  ,    self.dropout  ,    self.use_memory_efficient_attention  ,    dtype=self.dtype   )
												# cross attention
												UpperCAmelCase			 =					FlaxAttention(
												    self.dim  ,    self.n_heads  ,    self.d_head  ,    self.dropout  ,    self.use_memory_efficient_attention  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					FlaxFeedForward(dim=self.dim  ,    dropout=self.dropout  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.LayerNorm(epsilon=1E-5  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.LayerNorm(epsilon=1E-5  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.LayerNorm(epsilon=1E-5  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def __call__(   self					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Tuple=True   )							->     Tuple:
												# self attention
												UpperCAmelCase			 =					hidden_states
												if self.only_cross_attention:
																		UpperCAmelCase			 =					self.attna(self.norma(lowercase_   )  ,    lowercase_  ,    deterministic=lowercase_   )
												else:
																		UpperCAmelCase			 =					self.attna(self.norma(lowercase_   )  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					hidden_states + residual

												# cross attention
												UpperCAmelCase			 =					hidden_states
												UpperCAmelCase			 =					self.attna(self.norma(lowercase_   )  ,    lowercase_  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					hidden_states + residual

												# feed forward
												UpperCAmelCase			 =					hidden_states
												UpperCAmelCase			 =					self.ff(self.norma(lowercase_   )  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					hidden_states + residual

												return self.dropout_layer(lowercase_  ,    deterministic=lowercase_   )







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				1
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:int   )							->     Dict:
												UpperCAmelCase			 =					nn.GroupNorm(num_groups=32  ,    epsilon=1E-5   )

												UpperCAmelCase			 =					self.n_heads * self.d_head
												if self.use_linear_projection:
																		UpperCAmelCase			 =					nn.Dense(lowercase_  ,    dtype=self.dtype   )
												else:
																		UpperCAmelCase			 =					nn.Conv(
																		    lowercase_  ,    kernel_size=(1, 1)  ,    strides=(1, 1)  ,    padding='VALID'  ,    dtype=self.dtype  ,    )

												UpperCAmelCase			 =					[
												    FlaxBasicTransformerBlock(
												        lowercase_  ,    self.n_heads  ,    self.d_head  ,    dropout=self.dropout  ,    only_cross_attention=self.only_cross_attention  ,    dtype=self.dtype  ,    use_memory_efficient_attention=self.use_memory_efficient_attention  ,    )
												    for _ in range(self.depth   )
												]

												if self.use_linear_projection:
																		UpperCAmelCase			 =					nn.Dense(lowercase_  ,    dtype=self.dtype   )
												else:
																		UpperCAmelCase			 =					nn.Conv(
																		    lowercase_  ,    kernel_size=(1, 1)  ,    strides=(1, 1)  ,    padding='VALID'  ,    dtype=self.dtype  ,    )

												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def __call__(   self					:List[Any]  ,    lowercase_					:int  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[int]=True   )							->     int:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					hidden_states.shape
												UpperCAmelCase			 =					hidden_states
												UpperCAmelCase			 =					self.norm(lowercase_   )
												if self.use_linear_projection:
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    height * width  ,    lowercase_   )
																		UpperCAmelCase			 =					self.proj_in(lowercase_   )
												else:
																		UpperCAmelCase			 =					self.proj_in(lowercase_   )
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    height * width  ,    lowercase_   )

												for transformer_block in self.transformer_blocks:
																		UpperCAmelCase			 =					transformer_block(lowercase_  ,    lowercase_  ,    deterministic=lowercase_   )

												if self.use_linear_projection:
																		UpperCAmelCase			 =					self.proj_out(lowercase_   )
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
												else:
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					self.proj_out(lowercase_   )

												UpperCAmelCase			 =					hidden_states + residual
												return self.dropout_layer(lowercase_  ,    deterministic=lowercase_   )







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     int:
												# The second linear layer needs to be called
												# net_2 for now to match the index of the Sequential layer
												UpperCAmelCase			 =					FlaxGEGLU(self.dim  ,    self.dropout  ,    self.dtype   )
												UpperCAmelCase			 =					nn.Dense(self.dim  ,    dtype=self.dtype   )






						def __call__(   self					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[Any]=True   )							->     Any:
												UpperCAmelCase			 =					self.net_a(lowercase_  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					self.net_a(lowercase_   )
												return hidden_states







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												UpperCAmelCase			 =					self.dim * 4
												UpperCAmelCase			 =					nn.Dense(inner_dim * 2  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def __call__(   self					:str  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Any=True   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.proj(lowercase_   )
												UpperCAmelCase ,       UpperCAmelCase			 =					jnp.split(lowercase_  ,    2  ,    axis=2   )
												return self.dropout_layer(hidden_linear * nn.gelu(lowercase_   )  ,    deterministic=lowercase_   )











'''simple docstring'''







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

import numpy as np
import torch

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


_lowerCamelCase				:		Optional[Any]				      =    logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					42
					lowercase__       =					None






def 						_lowercase						(      lowercase_		,				lowercase_=0.999		,				lowercase_="cosine"		,				):



		'''simple docstring'''







		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

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





class 	lowerCamelCase_				(			UpperCAmelCase_,			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					[e.name for e in KarrasDiffusionSchedulers]
					lowercase__       =					1






					@register_to_config
					def __init__(       self,    __UpperCamelCase = 1_000,    __UpperCamelCase = 0.00_01,    __UpperCamelCase = 0.02,    __UpperCamelCase = "linear",    __UpperCamelCase = None,    __UpperCamelCase = "epsilon",    __UpperCamelCase = "linspace",    __UpperCamelCase = 0,    ):
							if trained_betas is not None:
									_snake_case      			=			torch.tensor(__UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "linear":
									_snake_case      			=			torch.linspace(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_snake_case      			=			(
									    torch.linspace(beta_start**0.5,    beta_end**0.5,    __UpperCamelCase,    dtype=torch.floataa					) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_snake_case      			=			betas_for_alpha_bar(__UpperCamelCase					)
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

							_snake_case      			=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							_snake_case      			=			np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					)

							# setable values
							_snake_case      			=			None
							_snake_case      			=			np.linspace(0,    num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[::-1].copy()
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					)
							_snake_case      			=			False






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# 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 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							if isinstance(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)
							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			sample / ((sigma**2 + 1) ** 0.5)
							_snake_case      			=			True
							return sample






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None					):
							_snake_case      			=			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":
									_snake_case      			=			np.linspace(0,    self.config.num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[
									    ::-1
									].copy()
							elif self.config.timestep_spacing == "leading":
									_snake_case      			=			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
									_snake_case      			=			(np.arange(0,    __UpperCamelCase					) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase					)
									timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
									_snake_case      			=			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
									_snake_case      			=			(np.arange(self.config.num_train_timesteps,    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\'.'''					)

							_snake_case      			=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							_snake_case      			=			np.interp(__UpperCamelCase,    np.arange(0,    len(__UpperCamelCase					)					),    __UpperCamelCase					)
							_snake_case      			=			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)
							if str(__UpperCamelCase					).startswith("mps"					):
									# mps does not support float64
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(__UpperCamelCase,    dtype=torch.floataa					)
							else:
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = True,    ):

							if (
							    isinstance(__UpperCamelCase,    __UpperCamelCase					)
							    or isinstance(__UpperCamelCase,    torch.IntTensor					)
							    or isinstance(__UpperCamelCase,    torch.LongTensor					)
							):
									raise ValueError(
									    (
									        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
									        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
									        " one of the `scheduler.timesteps` as a timestep."
									    ),    )

							if not self.is_scale_input_called:
									logger.warning(
									    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
									    "See `StableDiffusionPipeline` for a usage example."					)

							if isinstance(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)

							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
									_snake_case      			=			sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									_snake_case      			=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''					)

							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			self.sigmas[step_index + 1]
							_snake_case      			=			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							_snake_case      			=			(sigma_to**2 - sigma_up**2) ** 0.5

							# 2. Convert to an ODE derivative
							_snake_case      			=			(sample - pred_original_sample) / sigma

							_snake_case      			=			sigma_down - sigma

							_snake_case      			=			sample + derivative * dt

							_snake_case      			=			model_output.device
							_snake_case      			=			randn_tensor(model_output.shape,    dtype=model_output.dtype,    device=__UpperCamelCase,    generator=__UpperCamelCase					)

							_snake_case      			=			prev_sample + noise * sigma_up

							if not return_dict:
									return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=__UpperCamelCase,    pred_original_sample=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    ):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_snake_case      			=			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
									_snake_case      			=			self.timesteps.to(original_samples.device,    dtype=torch.floataa					)
									_snake_case      			=			timesteps.to(original_samples.device,    dtype=torch.floataa					)
							else:
									_snake_case      			=			self.timesteps.to(original_samples.device					)
									_snake_case      			=			timesteps.to(original_samples.device					)

							_snake_case      			=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

							_snake_case      			=			original_samples + noise * sigma
							return noisy_samples






					def __len__(       self					):
							return self.config.num_train_timesteps





