
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)





@flax.struct.dataclass
class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    42
					SCREAMING_SNAKE_CASE			    =    42





class       __A(				nn.Module       ):
					SCREAMING_SNAKE_CASE			    =    42
					SCREAMING_SNAKE_CASE			    =    (1_6, 3_2, 9_6, 2_5_6)
					SCREAMING_SNAKE_CASE			    =    jnp.floataa

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					nn.Conv(
												    self.block_out_channels[0]   ,					kernel_size=(3, 3)   ,					padding=((1, 1), (1, 1))   ,					dtype=self.dtype   ,					)

												lowerCamelCase_							  =					[]
												for i in range(len(self.block_out_channels   ) - 1   ):
																			lowerCamelCase_							  =					self.block_out_channels[i]
																			lowerCamelCase_							  =					self.block_out_channels[i + 1]
																			lowerCamelCase_							  =					nn.Conv(
																			    __UpperCamelCase   ,					kernel_size=(3, 3)   ,					padding=((1, 1), (1, 1))   ,					dtype=self.dtype   ,					)
																			blocks.append(__UpperCamelCase   )
																			lowerCamelCase_							  =					nn.Conv(
																			    __UpperCamelCase   ,					kernel_size=(3, 3)   ,					strides=(2, 2)   ,					padding=((1, 1), (1, 1))   ,					dtype=self.dtype   ,					)
																			blocks.append(__UpperCamelCase   )
												lowerCamelCase_							  =					blocks

												lowerCamelCase_							  =					nn.Conv(
												    self.conditioning_embedding_channels   ,					kernel_size=(3, 3)   ,					padding=((1, 1), (1, 1))   ,					kernel_init=nn.initializers.zeros_init()   ,					bias_init=nn.initializers.zeros_init()   ,					dtype=self.dtype   ,					)



					def __call__(	self					:     str   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.conv_in(__UpperCamelCase   )
												lowerCamelCase_							  =					nn.silu(__UpperCamelCase   )

												for block in self.blocks:
																			lowerCamelCase_							  =					block(__UpperCamelCase   )
																			lowerCamelCase_							  =					nn.silu(__UpperCamelCase   )

												lowerCamelCase_							  =					self.conv_out(__UpperCamelCase   )

												return embedding





@flax_register_to_config
class       __A(				nn.Module     ,		UpperCAmelCase     ,		UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    3_2
					SCREAMING_SNAKE_CASE			    =    4
					SCREAMING_SNAKE_CASE			    =    (
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "DownBlock2D",
					)
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
					SCREAMING_SNAKE_CASE			    =    2
					SCREAMING_SNAKE_CASE			    =    8
					SCREAMING_SNAKE_CASE			    =    None
					SCREAMING_SNAKE_CASE			    =    1_2_8_0
					SCREAMING_SNAKE_CASE			    =    0.0
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    jnp.floataa
					SCREAMING_SNAKE_CASE			    =    True
					SCREAMING_SNAKE_CASE			    =    0
					SCREAMING_SNAKE_CASE			    =    "rgb"
					SCREAMING_SNAKE_CASE			    =    (1_6, 3_2, 9_6, 2_5_6)

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     jax.random.KeyArray   ):
												# init input tensors
												lowerCamelCase_							  =					(1, self.in_channels, self.sample_size, self.sample_size)
												lowerCamelCase_							  =					jnp.zeros(__UpperCamelCase   ,					dtype=jnp.floataa   )
												lowerCamelCase_							  =					jnp.ones((1,)   ,					dtype=jnp.intaa   )
												lowerCamelCase_							  =					jnp.zeros((1, 1, self.cross_attention_dim)   ,					dtype=jnp.floataa   )
												lowerCamelCase_							  =					(1, 3, self.sample_size * 8, self.sample_size * 8)
												lowerCamelCase_							  =					jnp.zeros(__UpperCamelCase   ,					dtype=jnp.floataa   )

												lowerCamelCase_  ,   lowerCamelCase_							  =					jax.random.split(__UpperCamelCase   )
												lowerCamelCase_							  =					{"""params""": params_rng, """dropout""": dropout_rng}

												return self.init(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )["params"]

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.block_out_channels
												lowerCamelCase_							  =					block_out_channels[0] * 4

												# If `num_attention_heads` is not defined (which is the case for most models)
												# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
												# The reason for this behavior is to correct for incorrectly named variables that were introduced
												# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
												# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
												# which is why we correct for the naming here.
												lowerCamelCase_							  =					self.num_attention_heads or self.attention_head_dim

												# input
												lowerCamelCase_							  =					nn.Conv(
												    block_out_channels[0]   ,					kernel_size=(3, 3)   ,					strides=(1, 1)   ,					padding=((1, 1), (1, 1))   ,					dtype=self.dtype   ,					)

												# time
												lowerCamelCase_							  =					FlaxTimesteps(
												    block_out_channels[0]   ,					flip_sin_to_cos=self.flip_sin_to_cos   ,					freq_shift=self.config.freq_shift   )
												lowerCamelCase_							  =					FlaxTimestepEmbedding(__UpperCamelCase   ,					dtype=self.dtype   )

												lowerCamelCase_							  =					FlaxControlNetConditioningEmbedding(
												    conditioning_embedding_channels=block_out_channels[0]   ,					block_out_channels=self.conditioning_embedding_out_channels   ,					)

												lowerCamelCase_							  =					self.only_cross_attention
												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					(only_cross_attention,) * len(self.down_block_types   )

												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					(num_attention_heads,) * len(self.down_block_types   )

												# down
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					[]

												lowerCamelCase_							  =					block_out_channels[0]

												lowerCamelCase_							  =					nn.Conv(
												    __UpperCamelCase   ,					kernel_size=(1, 1)   ,					padding="""VALID"""   ,					kernel_init=nn.initializers.zeros_init()   ,					bias_init=nn.initializers.zeros_init()   ,					dtype=self.dtype   ,					)
												controlnet_down_blocks.append(__UpperCamelCase   )

												for i, down_block_type in enumerate(self.down_block_types   ):
																			lowerCamelCase_							  =					output_channel
																			lowerCamelCase_							  =					block_out_channels[i]
																			lowerCamelCase_							  =					i == len(__UpperCamelCase   ) - 1

																			if down_block_type == "CrossAttnDownBlock2D":
																										lowerCamelCase_							  =					FlaxCrossAttnDownBlockaD(
																										    in_channels=__UpperCamelCase   ,					out_channels=__UpperCamelCase   ,					dropout=self.dropout   ,					num_layers=self.layers_per_block   ,					num_attention_heads=num_attention_heads[i]   ,					add_downsample=not is_final_block   ,					use_linear_projection=self.use_linear_projection   ,					only_cross_attention=only_cross_attention[i]   ,					dtype=self.dtype   ,					)
																			else:
																										lowerCamelCase_							  =					FlaxDownBlockaD(
																										    in_channels=__UpperCamelCase   ,					out_channels=__UpperCamelCase   ,					dropout=self.dropout   ,					num_layers=self.layers_per_block   ,					add_downsample=not is_final_block   ,					dtype=self.dtype   ,					)

																			down_blocks.append(__UpperCamelCase   )

																			for _ in range(self.layers_per_block   ):
																										lowerCamelCase_							  =					nn.Conv(
																										    __UpperCamelCase   ,					kernel_size=(1, 1)   ,					padding="""VALID"""   ,					kernel_init=nn.initializers.zeros_init()   ,					bias_init=nn.initializers.zeros_init()   ,					dtype=self.dtype   ,					)
																										controlnet_down_blocks.append(__UpperCamelCase   )

																			if not is_final_block:
																										lowerCamelCase_							  =					nn.Conv(
																										    __UpperCamelCase   ,					kernel_size=(1, 1)   ,					padding="""VALID"""   ,					kernel_init=nn.initializers.zeros_init()   ,					bias_init=nn.initializers.zeros_init()   ,					dtype=self.dtype   ,					)
																										controlnet_down_blocks.append(__UpperCamelCase   )

												lowerCamelCase_							  =					down_blocks
												lowerCamelCase_							  =					controlnet_down_blocks

												# mid
												lowerCamelCase_							  =					block_out_channels[-1]
												lowerCamelCase_							  =					FlaxUNetMidBlockaDCrossAttn(
												    in_channels=__UpperCamelCase   ,					dropout=self.dropout   ,					num_attention_heads=num_attention_heads[-1]   ,					use_linear_projection=self.use_linear_projection   ,					dtype=self.dtype   ,					)

												lowerCamelCase_							  =					nn.Conv(
												    __UpperCamelCase   ,					kernel_size=(1, 1)   ,					padding="""VALID"""   ,					kernel_init=nn.initializers.zeros_init()   ,					bias_init=nn.initializers.zeros_init()   ,					dtype=self.dtype   ,					)



					def __call__(	self					:     Dict   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     float = 1.0   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     bool = False   ,					):
												lowerCamelCase_							  =					self.controlnet_conditioning_channel_order
												if channel_order == "bgr":
																			lowerCamelCase_							  =					jnp.flip(__UpperCamelCase   ,					axis=1   )

												# 1. time
												if not isinstance(__UpperCamelCase   ,					jnp.ndarray   ):
																			lowerCamelCase_							  =					jnp.array([timesteps]   ,					dtype=jnp.intaa   )
												elif isinstance(__UpperCamelCase   ,					jnp.ndarray   ) and len(timesteps.shape   ) == 0:
																			lowerCamelCase_							  =					timesteps.astype(dtype=jnp.floataa   )
																			lowerCamelCase_							  =					jnp.expand_dims(__UpperCamelCase   ,					0   )

												lowerCamelCase_							  =					self.time_proj(__UpperCamelCase   )
												lowerCamelCase_							  =					self.time_embedding(__UpperCamelCase   )

												# 2. pre-process
												lowerCamelCase_							  =					jnp.transpose(__UpperCamelCase   ,					(0, 2, 3, 1)   )
												lowerCamelCase_							  =					self.conv_in(__UpperCamelCase   )

												lowerCamelCase_							  =					jnp.transpose(__UpperCamelCase   ,					(0, 2, 3, 1)   )
												lowerCamelCase_							  =					self.controlnet_cond_embedding(__UpperCamelCase   )
												sample += controlnet_cond

												# 3. down
												lowerCamelCase_							  =					(sample,)
												for down_block in self.down_blocks:
																			if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																										lowerCamelCase_  ,   lowerCamelCase_							  =					down_block(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					deterministic=not train   )
																			else:
																										lowerCamelCase_  ,   lowerCamelCase_							  =					down_block(__UpperCamelCase   ,					__UpperCamelCase   ,					deterministic=not train   )
																			down_block_res_samples += res_samples

												# 4. mid
												lowerCamelCase_							  =					self.mid_block(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					deterministic=not train   )

												# 5. contronet blocks
												lowerCamelCase_							  =					()
												for down_block_res_sample, controlnet_block in zip(__UpperCamelCase   ,					self.controlnet_down_blocks   ):
																			lowerCamelCase_							  =					controlnet_block(__UpperCamelCase   )
																			controlnet_down_block_res_samples += (down_block_res_sample,)

												lowerCamelCase_							  =					controlnet_down_block_res_samples

												lowerCamelCase_							  =					self.controlnet_mid_block(__UpperCamelCase   )

												# 6. scaling
												lowerCamelCase_							  =					[sample * conditioning_scale for sample in down_block_res_samples]
												mid_block_res_sample *= conditioning_scale

												if not return_dict:
																			return (down_block_res_samples, mid_block_res_sample)

												return FlaxControlNetOutput(
												    down_block_res_samples=__UpperCamelCase   ,					mid_block_res_sample=__UpperCamelCase   )







import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class       __A(				UpperCAmelCase     ,		UpperCAmelCase       ):

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     int = 1_2_8   ,					__UpperCamelCase					:     int = 2_5_6   ,					__UpperCamelCase					:     float = 2000.0   ,					__UpperCamelCase					:     int = 7_6_8   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 2_0_4_8   ,					__UpperCamelCase					:     float = 0.1   ,					):
												super().__init__()

												lowerCamelCase_							  =					nn.Sequential(
												    nn.Linear(__UpperCamelCase   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					nn.Linear(d_model * 4   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					)

												lowerCamelCase_							  =					nn.Embedding(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					False

												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.ModuleList()
												for lyr_num in range(__UpperCamelCase   ):
																			# FiLM conditional T5 decoder
																			lowerCamelCase_							  =					DecoderLayer(d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )
																			self.decoders.append(__UpperCamelCase   )

												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					torch.mul(query_input.unsqueeze(-1   )   ,					key_input.unsqueeze(-2   )   )
												return mask.unsqueeze(-3   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					decoder_input_tokens.shape
												assert decoder_noise_time.shape == (batch,)

												# decoder_noise_time is in [0, 1), so rescale to expected timing range.
												lowerCamelCase_							  =					get_timestep_embedding(
												    decoder_noise_time * self.config.max_decoder_noise_time   ,					embedding_dim=self.config.d_model   ,					max_period=self.config.max_decoder_noise_time   ,					).to(dtype=self.dtype   )

												lowerCamelCase_							  =					self.conditioning_emb(__UpperCamelCase   ).unsqueeze(1   )

												assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

												lowerCamelCase_							  =					decoder_input_tokens.shape[1]

												# If we want to use relative positions for audio context, we can just offset
												# this sequence by the length of encodings_and_masks.
												lowerCamelCase_							  =					torch.broadcast_to(
												    torch.arange(__UpperCamelCase   ,					device=decoder_input_tokens.device   )   ,					(batch, seq_length)   ,					)

												lowerCamelCase_							  =					self.position_encoding(__UpperCamelCase   )

												lowerCamelCase_							  =					self.continuous_inputs_projection(__UpperCamelCase   )
												inputs += position_encodings
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												# decoder: No padding present.
												lowerCamelCase_							  =					torch.ones(
												    decoder_input_tokens.shape[:2]   ,					device=decoder_input_tokens.device   ,					dtype=inputs.dtype   )

												# Translate encoding masks to encoder-decoder masks.
												lowerCamelCase_							  =					[(x, self.encoder_decoder_mask(__UpperCamelCase   ,					__UpperCamelCase   )) for x, y in encodings_and_masks]

												# cross attend style: concat encodings
												lowerCamelCase_							  =					torch.cat([x[0] for x in encodings_and_encdec_masks]   ,					dim=1   )
												lowerCamelCase_							  =					torch.cat([x[1] for x in encodings_and_encdec_masks]   ,					dim=-1   )

												for lyr in self.decoders:
																			lowerCamelCase_							  =					lyr(
																			    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					encoder_attention_mask=__UpperCamelCase   ,					)[0]

												lowerCamelCase_							  =					self.decoder_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.post_dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.spec_out(__UpperCamelCase   )
												return spec_out





class       __A(				nn.Module       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.ModuleList()

												# cond self attention: layer 0
												self.layer.append(
												    TaLayerSelfAttentionCond(d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )   )

												# cross attention: layer 1
												self.layer.append(
												    TaLayerCrossAttention(
												        d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   ,					layer_norm_epsilon=__UpperCamelCase   ,					)   )

												# Film Cond MLP + dropout: last layer
												self.layer.append(
												    TaLayerFFCond(d_model=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   ,					layer_norm_epsilon=__UpperCamelCase   )   )



					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_							  =					self.layer[0](
												    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												if encoder_hidden_states is not None:
																			lowerCamelCase_							  =					torch.where(encoder_attention_mask > 0   ,					0   ,					-1E10   ).to(
																			    encoder_hidden_states.dtype   )

																			lowerCamelCase_							  =					self.layer[1](
																			    __UpperCamelCase   ,					key_value_states=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												# Apply Film Conditional Feed Forward layer
												lowerCamelCase_							  =					self.layer[-1](__UpperCamelCase   ,					__UpperCamelCase   )

												return (hidden_states,)





class       __A(				nn.Module       ):

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
												super().__init__()
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												# pre_self_attention_layer_norm
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )

												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.FiLMLayer(__UpperCamelCase   ,					__UpperCamelCase   )

												# Self-attention block
												lowerCamelCase_							  =					self.attention(__UpperCamelCase   )

												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )

												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ):
												super().__init__()
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.attention(
												    __UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					attention_mask=attention_mask.squeeze(1   )   ,					)
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return layer_output





class       __A(				nn.Module       ):

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					TaDenseGatedActDense(d_model=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.film(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.DenseReluDense(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )
												lowerCamelCase_							  =					NewGELUActivation()



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.act(self.wi_a(__UpperCamelCase   )   )
												lowerCamelCase_							  =					self.wi_a(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_gelu * hidden_linear
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.wo(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Parameter(torch.ones(__UpperCamelCase   )   )
												lowerCamelCase_							  =					eps



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any   ):
												# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
												# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
												# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
												# half-precision inputs is done in fp32

												lowerCamelCase_							  =					hidden_states.to(torch.floataa   ).pow(2   ).mean(-1   ,					keepdim=__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

												# convert into half-precision if necessary
												if self.weight.dtype in [torch.floataa, torch.bfloataa]:
																			lowerCamelCase_							  =					hidden_states.to(self.weight.dtype   )

												return self.weight * hidden_states





class       __A(				nn.Module       ):

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     torch.Tensor   ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__UpperCamelCase   ,					3.0   ))   ))





class       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					out_features * 2   ,					bias=__UpperCamelCase   )



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.scale_bias(__UpperCamelCase   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					torch.chunk(__UpperCamelCase   ,					2   ,					-1   )
												lowerCamelCase_							  =					x * (1 + scale) + shift
												return x





