"""simple docstring"""






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_A   					=    'scheduler_config.json'

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		1
					a   	=		2
					a   	=		3
					a   	=		4
					a   	=		5



@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		4_2



class lowerCamelCase		:






					'''simple docstring'''


					a   	=		SCHEDULER_CONFIG_NAME
					a   	=		["dtype"]
					a   	=		[]
					a   	=		True



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Dict   , _snake_case			:				Dict[str, Any] = None   , _snake_case			:				Optional[str] = None   , _snake_case			:				Union[str, Any]=False   , **_snake_case			:				Optional[int]   , )							->       int:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				cls.load_config(
									    pretrained_model_name_or_path=_snake_case   , subfolder=_snake_case   , return_unused_kwargs=_snake_case   , **_snake_case   , )
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				cls.from_config(_snake_case   , return_unused_kwargs=_snake_case   , **_snake_case		)

									if hasattr(_snake_case   , "create_state"		) and getattr(_snake_case   , "has_state"   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				scheduler.create_state()

									if return_unused_kwargs:
													return scheduler, state, unused_kwargs

									return scheduler, state



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				Union[str, os.PathLike]   , _snake_case			:				bool = False   , **_snake_case			:				Union[str, Any]		)							->       Dict:
									self.save_config(save_directory=_snake_case   , push_to_hub=_snake_case   , **_snake_case		)



					@property
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									return self._get_compatibles()



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				List[Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				list(set([cls.__name__] + cls._compatibles		)		)
									SCREAMING_SNAKE_CASE__		  =				importlib.import_module(__name__.split("."		)[0]		)
									SCREAMING_SNAKE_CASE__		  =				[
									    getattr(_snake_case   , _snake_case		) for c in compatible_classes_str if hasattr(_snake_case   , _snake_case		)
									]
									return compatible_classes




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   jnp.ndarray:
				assert len(__UpperCAmelCase       ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__UpperCAmelCase       ) - x.ndim)       )  ,					__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=0.999  ,					__UpperCAmelCase=jnp.floataa       )							->   jnp.ndarray:

				def alpha_bar(__UpperCAmelCase       ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2       ) ** 2

				SCREAMING_SNAKE_CASE__		  =				[]
				for i in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				i / num_diffusion_timesteps
								SCREAMING_SNAKE_CASE__		  =				(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(__UpperCAmelCase       ) / alpha_bar(__UpperCAmelCase       )  ,					__UpperCAmelCase       )       )
				return jnp.array(__UpperCAmelCase  ,					dtype=__UpperCAmelCase       )

@flax.struct.dataclass
class lowerCamelCase		:






					'''simple docstring'''


					a   	=		4_2
					a   	=		4_2
					a   	=		4_2



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Dict   , _snake_case			:				Any		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				scheduler.config

									if config.trained_betas is not None:
													SCREAMING_SNAKE_CASE__		  =				jnp.asarray(config.trained_betas   , dtype=scheduler.dtype		)
									elif config.beta_schedule == "linear":
													SCREAMING_SNAKE_CASE__		  =				jnp.linspace(config.beta_start   , config.beta_end   , config.num_train_timesteps   , dtype=scheduler.dtype		)
									elif config.beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													SCREAMING_SNAKE_CASE__		  =				(
													    jnp.linspace(
													        config.beta_start**0.5   , config.beta_end**0.5   , config.num_train_timesteps   , dtype=scheduler.dtype		)
													    ** 2
													)
									elif config.beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													SCREAMING_SNAKE_CASE__		  =				betas_for_alpha_bar(config.num_train_timesteps   , dtype=scheduler.dtype		)
									else:
													raise NotImplementedError(
													    F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""		)

									SCREAMING_SNAKE_CASE__		  =				1.0 - betas

									SCREAMING_SNAKE_CASE__		  =				jnp.cumprod(_snake_case   , axis=0		)

									return cls(
									    alphas=_snake_case   , betas=_snake_case   , alphas_cumprod=_snake_case   , )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				state.alphas_cumprod

				SCREAMING_SNAKE_CASE__		  =				alphas_cumprod[timesteps] ** 0.5
				SCREAMING_SNAKE_CASE__		  =				sqrt_alpha_prod.flatten()
				SCREAMING_SNAKE_CASE__		  =				broadcast_to_shape_from_left(__UpperCAmelCase  ,					original_samples.shape       )

				SCREAMING_SNAKE_CASE__		  =				(1 - alphas_cumprod[timesteps]) ** 0.5
				SCREAMING_SNAKE_CASE__		  =				sqrt_one_minus_alpha_prod.flatten()
				SCREAMING_SNAKE_CASE__		  =				broadcast_to_shape_from_left(__UpperCAmelCase  ,					original_samples.shape       )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				get_sqrt_alpha_prod(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				get_sqrt_alpha_prod(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity





"""simple docstring"""






from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Dict   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				Optional[Any]=0.0   , _snake_case			:				Optional[int] = None   , _snake_case			:				str = "geglu"   , _snake_case			:				Optional[int] = None   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				bool = True   , _snake_case			:				str = "layer_norm"   , _snake_case			:				bool = False   , )							->       Union[str, Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				only_cross_attention

									SCREAMING_SNAKE_CASE__		  =				(num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
									SCREAMING_SNAKE_CASE__		  =				(num_embeds_ada_norm is not None) and norm_type == "ada_norm"

									if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
													raise ValueError(
													    F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
													    F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""		)

									# Define 3 blocks. Each block has its own normalization layer.
									# 1. Self-Attn
									if self.use_ada_layer_norm:
													SCREAMING_SNAKE_CASE__		  =				AdaLayerNorm(_snake_case   , _snake_case		)
									elif self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				AdaLayerNormZero(_snake_case   , _snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				Attention(
									    query_dim=_snake_case   , heads=_snake_case   , dim_head=_snake_case   , dropout=_snake_case   , bias=_snake_case   , cross_attention_dim=cross_attention_dim if only_cross_attention else None   , upcast_attention=_snake_case   , )

									# 2. Cross-Attn
									if cross_attention_dim is not None or double_self_attention:
													# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
													# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
													# the second cross attention block.
													SCREAMING_SNAKE_CASE__		  =				(
													    AdaLayerNorm(_snake_case   , _snake_case		)
													    if self.use_ada_layer_norm
													    else nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)
													)
													SCREAMING_SNAKE_CASE__		  =				Attention(
													    query_dim=_snake_case   , cross_attention_dim=cross_attention_dim if not double_self_attention else None   , heads=_snake_case   , dim_head=_snake_case   , dropout=_snake_case   , bias=_snake_case   , upcast_attention=_snake_case   , )  # is self-attn if encoder_hidden_states is none
									else:
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None

									# 3. Feed-forward
									SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				FeedForward(_snake_case   , dropout=_snake_case   , activation_fn=_snake_case   , final_dropout=_snake_case		)

									# let chunk size default to None
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				0



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[int]   , _snake_case			:				int		)							->       List[str]:
									# Sets chunk feed-forward
									SCREAMING_SNAKE_CASE__		  =				chunk_size
									SCREAMING_SNAKE_CASE__		  =				dim



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.FloatTensor] = None   , _snake_case			:				Optional[torch.FloatTensor] = None   , _snake_case			:				Optional[torch.FloatTensor] = None   , _snake_case			:				Optional[torch.LongTensor] = None   , _snake_case			:				Dict[str, Any] = None   , _snake_case			:				Optional[torch.LongTensor] = None   , )							->       Union[str, Any]:
									# Notice that normalization is always applied before the real computation in the following blocks.
									# 1. Self-Attention
									if self.use_ada_layer_norm:
													SCREAMING_SNAKE_CASE__		  =				self.norma(_snake_case   , _snake_case		)
									elif self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.norma(
													    _snake_case   , _snake_case   , _snake_case   , hidden_dtype=hidden_states.dtype		)
									else:
													SCREAMING_SNAKE_CASE__		  =				self.norma(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				cross_attention_kwargs if cross_attention_kwargs is not None else {}

									SCREAMING_SNAKE_CASE__		  =				self.attna(
									    _snake_case   , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None   , attention_mask=_snake_case   , **_snake_case   , )
									if self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				gate_msa.unsqueeze(1		) * attn_output
									SCREAMING_SNAKE_CASE__		  =				attn_output + hidden_states

									# 2. Cross-Attention
									if self.attna is not None:
													SCREAMING_SNAKE_CASE__		  =				(
													    self.norma(_snake_case   , _snake_case		) if self.use_ada_layer_norm else self.norma(_snake_case		)
													)

													SCREAMING_SNAKE_CASE__		  =				self.attna(
													    _snake_case   , encoder_hidden_states=_snake_case   , attention_mask=_snake_case   , **_snake_case   , )
													SCREAMING_SNAKE_CASE__		  =				attn_output + hidden_states

									# 3. Feed-forward
									SCREAMING_SNAKE_CASE__		  =				self.norma(_snake_case		)

									if self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

									if self._chunk_size is not None:
													# "feed_forward_chunk_size" can be used to save memory
													if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																	raise ValueError(
																	    F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""		)

													SCREAMING_SNAKE_CASE__		  =				norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
													SCREAMING_SNAKE_CASE__		  =				torch.cat(
													    [self.ff(_snake_case		) for hid_slice in norm_hidden_states.chunk(_snake_case   , dim=self._chunk_dim		)]   , dim=self._chunk_dim   , )
									else:
													SCREAMING_SNAKE_CASE__		  =				self.ff(_snake_case		)

									if self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				gate_mlp.unsqueeze(1		) * ff_output

									SCREAMING_SNAKE_CASE__		  =				ff_output + hidden_states

									return hidden_states



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				int   , _snake_case			:				Optional[int] = None   , _snake_case			:				int = 4   , _snake_case			:				float = 0.0   , _snake_case			:				str = "geglu"   , _snake_case			:				bool = False   , )							->       Optional[Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				int(dim * mult		)
									SCREAMING_SNAKE_CASE__		  =				dim_out if dim_out is not None else dim

									if activation_fn == "gelu":
													SCREAMING_SNAKE_CASE__		  =				GELU(_snake_case   , _snake_case		)
									if activation_fn == "gelu-approximate":
													SCREAMING_SNAKE_CASE__		  =				GELU(_snake_case   , _snake_case   , approximate="tanh"		)
									elif activation_fn == "geglu":
													SCREAMING_SNAKE_CASE__		  =				GEGLU(_snake_case   , _snake_case		)
									elif activation_fn == "geglu-approximate":
													SCREAMING_SNAKE_CASE__		  =				ApproximateGELU(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				nn.ModuleList([]		)
									# project in
									self.net.append(_snake_case		)
									# project dropout
									self.net.append(nn.Dropout(_snake_case		)		)
									# project out
									self.net.append(nn.Linear(_snake_case   , _snake_case		)		)
									# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
									if final_dropout:
													self.net.append(nn.Dropout(_snake_case		)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				List[str]		)							->       str:
									for module in self.net:
													SCREAMING_SNAKE_CASE__		  =				module(_snake_case		)
									return hidden_states



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				str   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				str = "none"		)							->       Dict:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				approximate



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[Any]		)							->       Tuple:
									if gate.device.type != "mps":
													return F.gelu(_snake_case   , approximate=self.approximate		)
									# mps: gelu is not implemented for float16
									return F.gelu(gate.to(dtype=torch.floataa		)   , approximate=self.approximate		).to(dtype=gate.dtype		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				List[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.proj(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.gelu(_snake_case		)
									return hidden_states



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[int]   , _snake_case			:				int   , _snake_case			:				int		)							->       Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , dim_out * 2		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]		)							->       Optional[Any]:
									if gate.device.type != "mps":
													return F.gelu(_snake_case		)
									# mps: gelu is not implemented for float16
									return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Tuple		)							->       Any:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.proj(_snake_case		).chunk(2   , dim=-1		)
									return hidden_states * self.gelu(_snake_case		)



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				int   , _snake_case			:				int		)							->       List[Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Any		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.proj(_snake_case		)
									return x * torch.sigmoid(1.702 * x		)



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Any		)							->       Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Embedding(_snake_case   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				nn.SiLU()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , embedding_dim * 2		)
									SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				int   , _snake_case			:				Tuple		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.linear(self.silu(self.emb(_snake_case		)		)		)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				torch.chunk(_snake_case   , 2		)
									SCREAMING_SNAKE_CASE__		  =				self.norm(_snake_case		) * (1 + scale) + shift
									return x



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Any:
									super().__init__()

									SCREAMING_SNAKE_CASE__		  =				CombinedTimestepLabelEmbeddings(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				nn.SiLU()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , 6 * embedding_dim   , bias=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case   , eps=1e-6		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str=None		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.linear(self.silu(self.emb(_snake_case   , _snake_case   , hidden_dtype=_snake_case		)		)		)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				emb.chunk(6   , dim=1		)
									SCREAMING_SNAKE_CASE__		  =				self.norm(_snake_case		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
									return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				Optional[str] = None   , _snake_case			:				float = 1e-5		)							->       Optional[Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				num_groups
									SCREAMING_SNAKE_CASE__		  =				eps

									if act_fn is None:
													SCREAMING_SNAKE_CASE__		  =				None
									else:
													SCREAMING_SNAKE_CASE__		  =				get_activation(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , out_dim * 2		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				Optional[Any]		)							->       Optional[int]:
									if self.act:
													SCREAMING_SNAKE_CASE__		  =				self.act(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.linear(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				emb[:, :, None, None]
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				emb.chunk(2   , dim=1		)

									SCREAMING_SNAKE_CASE__		  =				F.group_norm(_snake_case   , self.num_groups   , eps=self.eps		)
									SCREAMING_SNAKE_CASE__		  =				x * (1 + scale) + shift
									return x




