



'''simple docstring'''




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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)






@flax.struct.dataclass
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	jnp.ndarray






@flax_register_to_config
class 		_UpperCamelCase						(							nn.Module					,      lowerCamelCase__					,      lowerCamelCase__					):


			'''simple docstring'''

			_A   :	int    					=	32
			_A   :	int    					=	4
			_A   :	int    					=	4
			_A   :	Tuple[str]    					=	(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			_A   :	Tuple[str]    					=	("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
			_A   :	Union[bool, Tuple[bool]]    					=	False
			_A   :	Tuple[int]    					=	(320, 640, 1280, 1280)
			_A   :	int    					=	2
			_A   :	Union[int, Tuple[int]]    					=	8
			_A   :	Optional[Union[int, Tuple[int]]]    					=	None
			_A   :	int    					=	1280
			_A   :	float    					=	0.0
			_A   :	bool    					=	False
			_A   :	jnp.dtype    					=	jnp.floataa
			_A   :	bool    					=	True
			_A   :	int    					=	0
			_A   :	bool    					=	False

			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				jax.random.KeyArray		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	(1, self.in_channels, self.sample_size, self.sample_size)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jnp.zeros(lowerCAmelCase__							,  dtype=jnp.floataa		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jnp.ones((1,)							,  dtype=jnp.intaa		)
					__SCREAMING_SNAKE_CASE :	int					 =	jnp.zeros((1, 1, self.cross_attention_dim)							,  dtype=jnp.floataa		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	jax.random.split(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{"""params""": params_rng, """dropout""": dropout_rng}

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

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.block_out_channels
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	block_out_channels[0] * 4

					if self.num_attention_heads is not None:
							raise ValueError(
							    """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."""		)

					# 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.
					__SCREAMING_SNAKE_CASE :	Any					 =	self.num_attention_heads or self.attention_head_dim

					# input
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	nn.Conv(
					    block_out_channels[0]							,  kernel_size=(3, 3)							,  strides=(1, 1)							,  padding=((1, 1), (1, 1))							,  dtype=self.dtype							,  )

					# time
					__SCREAMING_SNAKE_CASE :	Dict					 =	FlaxTimesteps(
					    block_out_channels[0]							,  flip_sin_to_cos=self.flip_sin_to_cos							,  freq_shift=self.config.freq_shift		)
					__SCREAMING_SNAKE_CASE :	int					 =	FlaxTimestepEmbedding(lowerCAmelCase__							,  dtype=self.dtype		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	self.only_cross_attention
					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							__SCREAMING_SNAKE_CASE :	int					 =	(only_cross_attention,) * len(self.down_block_types		)

					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	(num_attention_heads,) * len(self.down_block_types		)

					# down
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	block_out_channels[0]
					for i, down_block_type in enumerate(self.down_block_types		):
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	output_channel
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	block_out_channels[i]
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	i == len(lowerCAmelCase__		) - 1

							if down_block_type == "CrossAttnDownBlock2D":
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	FlaxCrossAttnDownBlockaD(
									    in_channels=lowerCAmelCase__							,  out_channels=lowerCAmelCase__							,  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]							,  use_memory_efficient_attention=self.use_memory_efficient_attention							,  dtype=self.dtype							,  )
							else:
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	FlaxDownBlockaD(
									    in_channels=lowerCAmelCase__							,  out_channels=lowerCAmelCase__							,  dropout=self.dropout							,  num_layers=self.layers_per_block							,  add_downsample=not is_final_block							,  dtype=self.dtype							,  )

							down_blocks.append(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	down_blocks

					# mid
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	FlaxUNetMidBlockaDCrossAttn(
					    in_channels=block_out_channels[-1]							,  dropout=self.dropout							,  num_attention_heads=num_attention_heads[-1]							,  use_linear_projection=self.use_linear_projection							,  use_memory_efficient_attention=self.use_memory_efficient_attention							,  dtype=self.dtype							,  )

					# up
					__SCREAMING_SNAKE_CASE :	str					 =	[]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	list(reversed(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	list(reversed(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	list(reversed(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	reversed_block_out_channels[0]
					for i, up_block_type in enumerate(self.up_block_types		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	output_channel
							__SCREAMING_SNAKE_CASE :	Any					 =	reversed_block_out_channels[i]
							__SCREAMING_SNAKE_CASE :	str					 =	reversed_block_out_channels[min(i + 1							,  len(lowerCAmelCase__		) - 1		)]

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	i == len(lowerCAmelCase__		) - 1

							if up_block_type == "CrossAttnUpBlock2D":
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	FlaxCrossAttnUpBlockaD(
									    in_channels=lowerCAmelCase__							,  out_channels=lowerCAmelCase__							,  prev_output_channel=lowerCAmelCase__							,  num_layers=self.layers_per_block + 1							,  num_attention_heads=reversed_num_attention_heads[i]							,  add_upsample=not is_final_block							,  dropout=self.dropout							,  use_linear_projection=self.use_linear_projection							,  only_cross_attention=only_cross_attention[i]							,  use_memory_efficient_attention=self.use_memory_efficient_attention							,  dtype=self.dtype							,  )
							else:
									__SCREAMING_SNAKE_CASE :	Any					 =	FlaxUpBlockaD(
									    in_channels=lowerCAmelCase__							,  out_channels=lowerCAmelCase__							,  prev_output_channel=lowerCAmelCase__							,  num_layers=self.layers_per_block + 1							,  add_upsample=not is_final_block							,  dropout=self.dropout							,  dtype=self.dtype							,  )

							up_blocks.append(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	output_channel
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	up_blocks

					# out
					__SCREAMING_SNAKE_CASE :	str					 =	nn.GroupNorm(num_groups=3_2							,  epsilon=1E-5		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Conv(
					    self.out_channels							,  kernel_size=(3, 3)							,  strides=(1, 1)							,  padding=((1, 1), (1, 1))							,  dtype=self.dtype							,  )



			def __call__(							self  :				Tuple							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				bool = True							,  lowerCAmelCase__  :				bool = False							,  ):





					"""simple docstring"""






					if not isinstance(lowerCAmelCase__							,  jnp.ndarray		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	jnp.array([timesteps]							,  dtype=jnp.intaa		)
					elif isinstance(lowerCAmelCase__							,  jnp.ndarray		) and len(timesteps.shape		) == 0:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	timesteps.astype(dtype=jnp.floataa		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jnp.expand_dims(lowerCAmelCase__							,  0		)

					__SCREAMING_SNAKE_CASE :	Any					 =	self.time_proj(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.time_embedding(lowerCAmelCase__		)

					# 2. pre-process
					__SCREAMING_SNAKE_CASE :	List[str]					 =	jnp.transpose(lowerCAmelCase__							,  (0, 2, 3, 1)		)
					__SCREAMING_SNAKE_CASE :	str					 =	self.conv_in(lowerCAmelCase__		)

					# 3. down
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(sample,)
					for down_block in self.down_blocks:
							if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	down_block(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  deterministic=not train		)
							else:
									__SCREAMING_SNAKE_CASE :	List[str]					 =	down_block(lowerCAmelCase__							,  lowerCAmelCase__							,  deterministic=not train		)
							down_block_res_samples += res_samples

					if down_block_additional_residuals is not None:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	()

							for down_block_res_sample, down_block_additional_residual in zip(
							    lowerCAmelCase__							,  lowerCAmelCase__		):
									down_block_res_sample += down_block_additional_residual
									new_down_block_res_samples += (down_block_res_sample,)

							__SCREAMING_SNAKE_CASE :	int					 =	new_down_block_res_samples

					# 4. mid
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.mid_block(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  deterministic=not train		)

					if mid_block_additional_residual is not None:
							sample += mid_block_additional_residual

					# 5. up
					for up_block in self.up_blocks:
							__SCREAMING_SNAKE_CASE :	str					 =	down_block_res_samples[-(self.layers_per_block + 1) :]
							__SCREAMING_SNAKE_CASE :	int					 =	down_block_res_samples[: -(self.layers_per_block + 1)]
							if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
									__SCREAMING_SNAKE_CASE :	Dict					 =	up_block(
									    lowerCAmelCase__							,  temb=lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  res_hidden_states_tuple=lowerCAmelCase__							,  deterministic=not train							,  )
							else:
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	up_block(lowerCAmelCase__							,  temb=lowerCAmelCase__							,  res_hidden_states_tuple=lowerCAmelCase__							,  deterministic=not train		)

        # 6. post-process
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.conv_norm_out(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	nn.silu(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.conv_out(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	jnp.transpose(lowerCAmelCase__							,  (0, 3, 1, 2)		)

					if not return_dict:
							return (sample,)

					return FlaxUNetaDConditionOutput(sample=lowerCAmelCase__		)




'''simple docstring'''




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 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					):


			'''simple docstring'''

			@register_to_config
			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 1_2_8							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 20_00.0							,  lowerCAmelCase__  :				int = 7_6_8							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 6_4							,  lowerCAmelCase__  :				int = 2_0_4_8							,  lowerCAmelCase__  :				float = 0.1							,  ):





					"""simple docstring"""






					super().__init__()

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Sequential(
					    nn.Linear(lowerCAmelCase__							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  nn.Linear(d_model * 4							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  )

					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Embedding(lowerCAmelCase__							,  lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	False

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	nn.Dropout(p=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.ModuleList()
					for lyr_num in range(lowerCAmelCase__		):
							# FiLM conditional T5 decoder
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	DecoderLayer(d_model=lowerCAmelCase__							,  d_kv=lowerCAmelCase__							,  num_heads=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)
							self.decoders.append(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	TaLayerNorm(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	nn.Dropout(p=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mul(query_input.unsqueeze(-1		)							,  key_input.unsqueeze(-2		)		)
					return mask.unsqueeze(-3		)



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	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		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.conditioning_emb(lowerCAmelCase__		).unsqueeze(1		)

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

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.broadcast_to(
					    torch.arange(lowerCAmelCase__							,  device=decoder_input_tokens.device		)							,  (batch, seq_length)							,  )

					__SCREAMING_SNAKE_CASE :	str					 =	self.position_encoding(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.continuous_inputs_projection(lowerCAmelCase__		)
					inputs += position_encodings
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dropout(lowerCAmelCase__		)

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

					# Translate encoding masks to encoder-decoder masks.
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[(x, self.encoder_decoder_mask(lowerCAmelCase__							,  lowerCAmelCase__		)) for x, y in encodings_and_masks]

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

					for lyr in self.decoders:
							__SCREAMING_SNAKE_CASE :	Dict					 =	lyr(
							    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  encoder_attention_mask=lowerCAmelCase__							,  )[0]

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.decoder_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.post_dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	self.spec_out(lowerCAmelCase__		)
					return spec_out






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Tuple=1E-6		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.ModuleList()

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

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

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



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Optional[int]=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer[0](
					    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					if encoder_hidden_states is not None:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.where(encoder_attention_mask > 0							,  0							,  -1E10		).to(
							    encoder_hidden_states.dtype		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[1](
							    lowerCAmelCase__							,  key_value_states=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					# Apply Film Conditional Feed Forward layer
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[-1](lowerCAmelCase__							,  lowerCAmelCase__		)

					return (hidden_states,)






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	TaLayerNorm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	Attention(query_dim=lowerCAmelCase__							,  heads=lowerCAmelCase__							,  dim_head=lowerCAmelCase__							,  out_bias=lowerCAmelCase__							,  scale_qk=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				int=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.layer_norm(lowerCAmelCase__		)

					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	str					 =	self.FiLMLayer(lowerCAmelCase__							,  lowerCAmelCase__		)

					# Self-attention block
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.attention(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	hidden_states + self.dropout(lowerCAmelCase__		)

					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	Attention(query_dim=lowerCAmelCase__							,  heads=lowerCAmelCase__							,  dim_head=lowerCAmelCase__							,  out_bias=lowerCAmelCase__							,  scale_qk=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Any=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.layer_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.attention(
					    lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  attention_mask=attention_mask.squeeze(1		)							,  )
					__SCREAMING_SNAKE_CASE :	Dict					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return layer_output






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Any					 =	TaDenseGatedActDense(d_model=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Tuple=None		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer_norm(lowerCAmelCase__		)
					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.film(lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	self.DenseReluDense(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Tuple							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Dropout(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	NewGELUActivation()



			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.act(self.wi_a(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Any					 =	self.wi_a(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	hidden_gelu * hidden_linear
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.wo(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Tuple=1E-6		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Parameter(torch.ones(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	str					 =	eps



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	hidden_states.to(torch.floataa		).pow(2		).mean(-1							,  keepdim=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

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

					return self.weight * hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				torch.Tensor		):





					"""simple docstring"""






					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__							,  3.0		))		))






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  out_features * 2							,  bias=lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scale_bias(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.chunk(lowerCAmelCase__							,  2							,  -1		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	x * (1 + scale) + shift
					return x