


"""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      A(  lowerCamelCase__				,							lowerCamelCase__		):

		"""simple docstring"""





		@register_to_config
		def __init__( self					,				SCREAMING_SNAKE_CASE__ = 1_28					,				SCREAMING_SNAKE_CASE__ = 2_56					,				SCREAMING_SNAKE_CASE__ = 2_0_0_0.0					,				SCREAMING_SNAKE_CASE__ = 7_68					,				SCREAMING_SNAKE_CASE__ = 12					,				SCREAMING_SNAKE_CASE__ = 12					,				SCREAMING_SNAKE_CASE__ = 64					,				SCREAMING_SNAKE_CASE__ = 20_48					,				SCREAMING_SNAKE_CASE__ = 0.1					,				)      ->	int:

					"""simple docstring"""



					super().__init__()

					_UpperCamelCase					:List[str]					     =	nn.Sequential(
					    nn.Linear(SCREAMING_SNAKE_CASE__					,				d_model * 4					,				bias=SCREAMING_SNAKE_CASE__   )					,				nn.SiLU()					,				nn.Linear(d_model * 4					,				d_model * 4					,				bias=SCREAMING_SNAKE_CASE__   )					,				nn.SiLU()					,				)

					_UpperCamelCase					:int					     =	nn.Embedding(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	False

					_UpperCamelCase					:List[Any]					     =	nn.Linear(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				bias=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	nn.Dropout(p=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	nn.ModuleList()
					for lyr_num in range(SCREAMING_SNAKE_CASE__   ):
								# FiLM conditional T5 decoder
								_UpperCamelCase					:Union[str, Any]					     =	DecoderLayer(d_model=SCREAMING_SNAKE_CASE__					,				d_kv=SCREAMING_SNAKE_CASE__					,				num_heads=SCREAMING_SNAKE_CASE__					,				d_ff=SCREAMING_SNAKE_CASE__					,				dropout_rate=SCREAMING_SNAKE_CASE__   )
								self.decoders.append(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	TaLayerNorm(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:int					     =	nn.Dropout(p=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	nn.Linear(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				bias=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



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





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					: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.
					_UpperCamelCase					:int					     =	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   )

					_UpperCamelCase					:List[str]					     =	self.conditioning_emb(SCREAMING_SNAKE_CASE__   ).unsqueeze(1   )

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

					_UpperCamelCase					: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.
					_UpperCamelCase					:List[Any]					     =	torch.broadcast_to(
					    torch.arange(SCREAMING_SNAKE_CASE__					,				device=decoder_input_tokens.device   )					,				(batch, seq_length)					,				)

					_UpperCamelCase					:Any					     =	self.position_encoding(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	self.continuous_inputs_projection(SCREAMING_SNAKE_CASE__   )
					inputs += position_encodings
					_UpperCamelCase					:Dict					     =	self.dropout(SCREAMING_SNAKE_CASE__   )

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

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

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

					for lyr in self.decoders:
								_UpperCamelCase					:Union[str, Any]					     =	lyr(
								    SCREAMING_SNAKE_CASE__					,				conditioning_emb=SCREAMING_SNAKE_CASE__					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__					,				encoder_attention_mask=SCREAMING_SNAKE_CASE__					,				)[0]

					_UpperCamelCase					:int					     =	self.decoder_norm(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	self.post_dropout(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	self.spec_out(SCREAMING_SNAKE_CASE__   )
					return spec_out






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=1E-6   )      ->	List[str]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase					:Optional[Any]					     =	nn.ModuleList()

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

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

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





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				)      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.layer[0](
					    SCREAMING_SNAKE_CASE__					,				conditioning_emb=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				)

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

								_UpperCamelCase					:Tuple					     =	self.layer[1](
								    SCREAMING_SNAKE_CASE__					,				key_value_states=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				)

					# Apply Film Conditional Feed Forward layer
					_UpperCamelCase					:Optional[Any]					     =	self.layer[-1](SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					return (hidden_states,)






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



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





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				)      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.layer_norm(SCREAMING_SNAKE_CASE__   )

					if conditioning_emb is not None:
								_UpperCamelCase					:Tuple					     =	self.FiLMLayer(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					# Self-attention block
					_UpperCamelCase					:List[str]					     =	self.attention(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Tuple					     =	hidden_states + self.dropout(SCREAMING_SNAKE_CASE__   )

					return hidden_states






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase					:List[Any]					     =	Attention(query_dim=SCREAMING_SNAKE_CASE__					,				heads=SCREAMING_SNAKE_CASE__					,				dim_head=SCREAMING_SNAKE_CASE__					,				out_bias=SCREAMING_SNAKE_CASE__					,				scale_qk=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	TaLayerNorm(SCREAMING_SNAKE_CASE__					,				eps=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	nn.Dropout(SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				)      ->	Union[str, Any]:

					"""simple docstring"""



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






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase					:int					     =	TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE__					,				d_ff=SCREAMING_SNAKE_CASE__					,				dropout_rate=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	TaFiLMLayer(in_features=d_model * 4					,				out_features=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	TaLayerNorm(SCREAMING_SNAKE_CASE__					,				eps=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	nn.Dropout(SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.layer_norm(SCREAMING_SNAKE_CASE__   )
					if conditioning_emb is not None:
								_UpperCamelCase					:List[Any]					     =	self.film(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	self.DenseReluDense(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	hidden_states + self.dropout(SCREAMING_SNAKE_CASE__   )
					return hidden_states






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase					:int					     =	nn.Linear(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				bias=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	nn.Linear(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				bias=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	nn.Linear(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				bias=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	nn.Dropout(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	NewGELUActivation()





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.act(self.wi_a(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:Optional[int]					     =	self.wi_a(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	hidden_gelu * hidden_linear
					_UpperCamelCase					:Optional[int]					     =	self.dropout(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	self.wo(SCREAMING_SNAKE_CASE__   )
					return hidden_states






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=1E-6   )      ->	Dict:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase					:Tuple					     =	nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:Dict					     =	eps





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	hidden_states.to(torch.floataa   ).pow(2   ).mean(-1					,				keepdim=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

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

					return self.weight * hidden_states






class      A(  nn.Module		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	torch.Tensor:

					"""simple docstring"""



					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.0_4_4_7_1_5 * torch.pow(SCREAMING_SNAKE_CASE__					,				3.0   ))   ))






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase					:List[Any]					     =	nn.Linear(SCREAMING_SNAKE_CASE__					,				out_features * 2					,				bias=SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	self.scale_bias(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase     ,      _UpperCamelCase					:str					     =	torch.chunk(SCREAMING_SNAKE_CASE__					,				2					,				-1   )
					_UpperCamelCase					:Union[str, Any]					     =	x * (1 + scale) + shift
					return x





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	UNetaDModel(
					    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=3					,				out_channels=3					,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,				)
					return model





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

