from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class      __lowercase					(    _A    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      TransformeraDModel							,							__lowerCamelCase				:      AutoencoderKL							,							__lowerCamelCase				:      KarrasDiffusionSchedulers							,							__lowerCamelCase				:      Optional[Dict[int, str]] = None							,							)							->		List[Any]:


									'''simple docstring'''

									super().__init__()
									self.register_modules(transformer=__lowerCamelCase							,							vae=__lowerCamelCase							,							scheduler=__lowerCamelCase   )

									# create a imagenet -> id dictionary for easier use
									lowercase							=		{}
									if idalabel is not None:
												for key, value in idalabel.items():
															for label in value.split(''','''   ):
																		lowercase							=		int(__lowerCamelCase   )
												lowercase							=		dict(sorted(self.labels.items()   )   )

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, List[str]]   )							->		List[int]:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												lowercase							=		list(__lowerCamelCase   )

									for l in label:
												if l not in self.labels:
															raise ValueError(
															    f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'   )

									return [self.labels[l] for l in label]



						@torch.no_grad()
						def __call__(	self				:      List[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      float = 4.0							,							__lowerCamelCase				:      Optional[Union[torch.Generator, List[torch.Generator]]] = None							,							__lowerCamelCase				:      int = 50							,							__lowerCamelCase				:      Optional[str] = "pil"							,							__lowerCamelCase				:      bool = True							,							)							->		Union[ImagePipelineOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		len(__lowerCamelCase   )
									lowercase							=		self.transformer.config.sample_size
									lowercase							=		self.transformer.config.in_channels

									lowercase							=		randn_tensor(
									    shape=(batch_size, latent_channels, latent_size, latent_size)							,							generator=__lowerCamelCase							,							device=self.device							,							dtype=self.transformer.dtype							,							)
									lowercase							=		torch.cat([latents] * 2   ) if guidance_scale > 1 else latents

									lowercase							=		torch.tensor(__lowerCamelCase							,							device=self.device   ).reshape(-1   )
									lowercase							=		torch.tensor([10_00] * batch_size							,							device=self.device   )
									lowercase							=		torch.cat([class_labels, class_null]							,							0   ) if guidance_scale > 1 else class_labels

									# set step values
									self.scheduler.set_timesteps(__lowerCamelCase   )

									for t in self.progress_bar(self.scheduler.timesteps   ):
												if guidance_scale > 1:
															lowercase							=		latent_model_input[: len(__lowerCamelCase   ) // 2]
															lowercase							=		torch.cat([half, half]							,							dim=0   )
												lowercase							=		self.scheduler.scale_model_input(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		t
												if not torch.is_tensor(__lowerCamelCase   ):
															# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
															# This would be a good case for the `match` statement (Python 3.10+)
															lowercase							=		latent_model_input.device.type == '''mps'''
															if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
																		lowercase							=		torch.floataa if is_mps else torch.floataa
															else:
																		lowercase							=		torch.intaa if is_mps else torch.intaa
															lowercase							=		torch.tensor([timesteps]							,							dtype=__lowerCamelCase							,							device=latent_model_input.device   )
												elif len(timesteps.shape   ) == 0:
															lowercase							=		timesteps[None].to(latent_model_input.device   )
												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												lowercase							=		timesteps.expand(latent_model_input.shape[0]   )
												# predict noise model_output
												lowercase							=		self.transformer(
												    __lowerCamelCase							,							timestep=__lowerCamelCase							,							class_labels=__lowerCamelCase   ).sample

												# perform guidance
												if guidance_scale > 1:
															lowercase		,lowercase							=		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
															lowercase		,lowercase							=		torch.split(__lowerCamelCase							,							len(__lowerCamelCase   ) // 2							,							dim=0   )

															lowercase							=		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
															lowercase							=		torch.cat([half_eps, half_eps]							,							dim=0   )

															lowercase							=		torch.cat([eps, rest]							,							dim=1   )

												# learned sigma
												if self.transformer.config.out_channels // 2 == latent_channels:
															lowercase		,lowercase							=		torch.split(__lowerCamelCase							,							__lowerCamelCase							,							dim=1   )
												else:
															lowercase							=		noise_pred

												# compute previous image: x_t -> x_t-1
												lowercase							=		self.scheduler.step(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   ).prev_sample

									if guidance_scale > 1:
												lowercase		,lowercase							=		latent_model_input.chunk(2							,							dim=0   )
									else:
												lowercase							=		latent_model_input

									lowercase							=		1 / self.vae.config.scaling_factor * latents
									lowercase							=		self.vae.decode(__lowerCamelCase   ).sample

									lowercase							=		(samples / 2 + 0.5).clamp(0							,							1   )

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									lowercase							=		samples.cpu().permute(0							,							2							,							3							,							1   ).float().numpy()

									if output_type == "pil":
												lowercase							=		self.numpy_to_pil(__lowerCamelCase   )

									if not return_dict:
												return (samples,)

									return ImagePipelineOutput(images=__lowerCamelCase   )






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      __lowercase					(    _A       ,     _A    ):

						@register_to_config
						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      int = 1_28							,							__lowerCamelCase				:      int = 2_56							,							__lowerCamelCase				:      float = 2000.0							,							__lowerCamelCase				:      int = 7_68							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 64							,							__lowerCamelCase				:      int = 20_48							,							__lowerCamelCase				:      float = 0.1							,							)							->		Optional[int]:


									'''simple docstring'''

									super().__init__()

									lowercase							=		nn.Sequential(
									    nn.Linear(__lowerCamelCase							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							nn.Linear(d_model * 4							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							)

									lowercase							=		nn.Embedding(__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		False

									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )

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

									lowercase							=		TaLayerNorm(__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		torch.mul(query_input.unsqueeze(-1   )							,							key_input.unsqueeze(-2   )   )
									return mask.unsqueeze(-3   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Any   )							->		List[Any]:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

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

									lowercase							=		self.conditioning_emb(__lowerCamelCase   ).unsqueeze(1   )

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

									lowercase							=		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.
									lowercase							=		torch.broadcast_to(
									    torch.arange(__lowerCamelCase							,							device=decoder_input_tokens.device   )							,							(batch, seq_length)							,							)

									lowercase							=		self.position_encoding(__lowerCamelCase   )

									lowercase							=		self.continuous_inputs_projection(__lowerCamelCase   )
									inputs += position_encodings
									lowercase							=		self.dropout(__lowerCamelCase   )

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

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

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

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

									lowercase							=		self.decoder_norm(__lowerCamelCase   )
									lowercase							=		self.post_dropout(__lowerCamelCase   )

									lowercase							=		self.spec_out(__lowerCamelCase   )
									return spec_out






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[str]=1E-6   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		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        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[Any]=None							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int=None							,							)							->		Any:


									'''simple docstring'''

									lowercase							=		self.layer[0](
									    __lowerCamelCase							,							conditioning_emb=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

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

												lowercase							=		self.layer[1](
												    __lowerCamelCase							,							key_value_states=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

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

									return (hidden_states,)






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaLayerNorm(__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )

									if conditioning_emb is not None:
												lowercase							=		self.FiLMLayer(__lowerCamelCase							,							__lowerCamelCase   )

									# Self-attention block
									lowercase							=		self.attention(__lowerCamelCase   )

									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )

									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									super().__init__()
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									lowercase							=		self.attention(
									    __lowerCamelCase							,							encoder_hidden_states=__lowerCamelCase							,							attention_mask=attention_mask.squeeze(1   )							,							)
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return layer_output






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Tuple   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaDenseGatedActDense(d_model=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      str=None   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									if conditioning_emb is not None:
												lowercase							=		self.film(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		self.DenseReluDense(__lowerCamelCase   )
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )
									lowercase							=		NewGELUActivation()



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.act(self.wi_a(__lowerCamelCase   )   )
									lowercase							=		self.wi_a(__lowerCamelCase   )
									lowercase							=		hidden_gelu * hidden_linear
									lowercase							=		self.dropout(__lowerCamelCase   )

									lowercase							=		self.wo(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict=1E-6   )							->		Optional[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Parameter(torch.ones(__lowerCamelCase   )   )
									lowercase							=		eps



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

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

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

									return self.weight * hidden_states






class      __lowercase					(    nn.Module    ):

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      torch.Tensor   )							->		torch.Tensor:


									'''simple docstring'''

									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_4715 * torch.pow(__lowerCamelCase							,							3.0   ))   ))






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							out_features * 2							,							bias=__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self.scale_bias(__lowerCamelCase   )
									lowercase		,lowercase							=		torch.chunk(__lowerCamelCase							,							2							,							-1   )
									lowercase							=		x * (1 + scale) + shift
									return x





