


import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return image
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							List[Any]      		=		[image]

    if isinstance(image[0]							, PIL.Image.Image ):
        _lowercase       :							Tuple      		=		[np.array(i.resize((w, h)							, resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
        _lowercase       :							str      		=		np.concatenate(lowerCamelCase_							, axis=0 )
        _lowercase       :							Dict      		=		np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_55.0
        _lowercase       :							Optional[int]      		=		image.transpose(0							, 3							, 1							, 2 )
        _lowercase       :							str      		=		2.0 * image - 1.0
        _lowercase       :							Tuple      		=		torch.from_numpy(lowerCamelCase_ )
    elif isinstance(image[0]							, torch.Tensor ):
        _lowercase       :							Any      		=		torch.cat(lowerCamelCase_							, dim=0 )
    return image



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=0.99_95 )    ->						Tuple:
    if not isinstance(lowerCamelCase_							, np.ndarray ):
        _lowercase       :							List[Any]      		=		True
        _lowercase       :							Any      		=		va.device
        _lowercase       :							Union[str, Any]      		=		va.cpu().numpy()
        _lowercase       :							int      		=		va.cpu().numpy()

    _lowercase       :							int      		=		np.sum(va * va / (np.linalg.norm(lowerCamelCase_ ) * np.linalg.norm(lowerCamelCase_ )) )
    if np.abs(lowerCamelCase_ ) > DOT_THRESHOLD:
        _lowercase       :							Any      		=		(1 - t) * va + t * va
    else:
        _lowercase       :							Dict      		=		np.arccos(lowerCamelCase_ )
        _lowercase       :							str      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							int      		=		theta_a * t
        _lowercase       :							Dict      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							Any      		=		np.sin(theta_a - theta_t ) / sin_theta_a
        _lowercase       :							List[Any]      		=		sin_theta_t / sin_theta_a
        _lowercase       :							Dict      		=		sa * va + sa * va

    if inputs_are_torch:
        _lowercase       :							Optional[Any]      		=		torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )

    return va



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    for param in model.parameters():
        _lowercase       :							Any      		=		value






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=None,      )       ->   Tuple:




           """simple docstring"""

           super().__init__()
           self.register_modules(
               vae=lowerCamelCase,      text_encoder=lowerCamelCase,      clip_model=lowerCamelCase,      tokenizer=lowerCamelCase,      unet=lowerCamelCase,      scheduler=lowerCamelCase,      feature_extractor=lowerCamelCase,      coca_model=lowerCamelCase,      coca_tokenizer=lowerCamelCase,      coca_transform=lowerCamelCase,      )
           _lowercase       :							Tuple      		=		(
               feature_extractor.size
               if isinstance(feature_extractor.size,      lowerCamelCase)
               else feature_extractor.size['shortest_edge']
           )
           _lowercase       :							Union[str, Any]      		=		transforms.Normalize(mean=feature_extractor.image_mean,      std=feature_extractor.image_std)
           set_requires_grad(self.text_encoder,      lowerCamelCase)
           set_requires_grad(self.clip_model,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase = "auto")       ->   Any:




           """simple docstring"""

           if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _lowercase       :							Optional[Any]      		=		self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           self.enable_attention_slicing(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		min(int(num_inference_steps * strength),      lowerCamelCase)

           _lowercase       :							List[Any]      		=		max(num_inference_steps - init_timestep,      0)
           _lowercase       :							int      		=		self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None)       ->   Optional[Any]:




           """simple docstring"""

           if not isinstance(lowerCamelCase,      torch.Tensor):
               raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase)}''')

           _lowercase       :							Any      		=		image.to(device=lowerCamelCase,      dtype=lowerCamelCase)

           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Dict      		=		[
                   self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(lowerCamelCase)
               ]
               _lowercase       :							int      		=		torch.cat(lowerCamelCase,      dim=0)
           else:
               _lowercase       :							int      		=		self.vae.encode(lowerCamelCase).latent_dist.sample(lowerCamelCase)

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							str      		=		0.1_8_2_1_5 * init_latents
           _lowercase       :							List[str]      		=		init_latents.repeat_interleave(lowerCamelCase,      dim=0)

           _lowercase       :							List[str]      		=		randn_tensor(init_latents.shape,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)

           # get latents
           _lowercase       :							Any      		=		self.scheduler.add_noise(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							str      		=		init_latents

           return latents
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		self.coca_transform(lowerCamelCase).unsqueeze(0)
           with torch.no_grad(), torch.cuda.amp.autocast():
               _lowercase       :							List[str]      		=		self.coca_model.generate(transformed_image.to(device=self.device,      dtype=self.coca_model.dtype))
           _lowercase       :							int      		=		self.coca_tokenizer.decode(generated[0].cpu().numpy())
           return generated.split('<end_of_text>')[0].replace('<start_of_text>',      '').rstrip(' .,')
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.feature_extractor.preprocess(lowerCamelCase)
           _lowercase       :							List[str]      		=		torch.from_numpy(clip_image_input['pixel_values'][0]).unsqueeze(0).to(self.device).half()
           _lowercase       :							int      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							Dict      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)
           _lowercase       :							int      		=		image_embeddings_clip.repeat_interleave(lowerCamelCase,      dim=0)
           return image_embeddings_clip
       @torch.enable_grad()
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		latents.detach().requires_grad_()

           _lowercase       :							Union[str, Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

           # predict the noise residual
           _lowercase       :							Tuple      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

           if isinstance(self.scheduler,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
               _lowercase       :							Any      		=		self.scheduler.alphas_cumprod[timestep]
               _lowercase       :							Any      		=		1 - alpha_prod_t
               # compute predicted original sample from predicted noise also called
               # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
               _lowercase       :							List[Any]      		=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

               _lowercase       :							List[str]      		=		torch.sqrt(lowerCamelCase)
               _lowercase       :							Dict      		=		pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Dict      		=		self.scheduler.sigmas[index]
               _lowercase       :							List[Any]      		=		latents - sigma * noise_pred
           else:
               raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Dict      		=		1 / 0.1_8_2_1_5 * sample
           _lowercase       :							Optional[Any]      		=		self.vae.decode(lowerCamelCase).sample
           _lowercase       :							int      		=		(image / 2 + 0.5).clamp(0,      1)

           _lowercase       :							Any      		=		transforms.Resize(self.feature_extractor_size)(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.normalize(lowerCamelCase).to(latents.dtype)

           _lowercase       :							List[str]      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							List[Any]      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		spherical_dist_loss(lowerCamelCase,      lowerCamelCase).mean() * clip_guidance_scale

           _lowercase       :							str      		=		-torch.autograd.grad(lowerCamelCase,      lowerCamelCase)[0]

           if isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Union[str, Any]      		=		latents.detach() + grads * (sigma**2)
               _lowercase       :							List[str]      		=		noise_pred_original
           else:
               _lowercase       :							List[Any]      		=		noise_pred_original - torch.sqrt(lowerCamelCase) * grads
           return noise_pred, latents




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = 5_12,      lowerCamelCase = 5_12,      lowerCamelCase = 0.6,      lowerCamelCase = 50,      lowerCamelCase = 7.5,      lowerCamelCase = 1,      lowerCamelCase = 0.0,      lowerCamelCase = 1_00,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      lowerCamelCase = 0.8,      lowerCamelCase = 0.1,      lowerCamelCase = 0.1,      )       ->   int:




           """simple docstring"""

           if isinstance(lowerCamelCase,      lowerCamelCase) and len(lowerCamelCase) != batch_size:
               raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowerCamelCase)} generators.''')

           if height % 8 != 0 or width % 8 != 0:
               raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

           if isinstance(lowerCamelCase,      torch.Generator) and batch_size > 1:
               _lowercase       :							Dict      		=		[generator] + [None] * (batch_size - 1)

           _lowercase       :							Optional[int]      		=		[
               ('model', self.coca_model is None),
               ('tokenizer', self.coca_tokenizer is None),
               ('transform', self.coca_transform is None),
           ]
           _lowercase       :							Optional[int]      		=		[x[0] for x in coca_is_none if x[1]]
           _lowercase       :							str      		=		', '.join(lowerCamelCase)
           # generate prompts with coca model if prompt is None
           if content_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							List[Any]      		=		self.get_image_description(lowerCamelCase)
           if style_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							Dict      		=		self.get_image_description(lowerCamelCase)

           # get prompt text embeddings for content and style
           _lowercase       :							Optional[int]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							Optional[int]      		=		self.text_encoder(content_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Union[str, Any]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							List[Any]      		=		self.text_encoder(style_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Any      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # duplicate text embeddings for each generation per prompt
           _lowercase       :							Dict      		=		text_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

           # set timesteps
           _lowercase       :							Dict      		=		'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_offset:
               _lowercase       :							Any      		=		1

           self.scheduler.set_timesteps(lowerCamelCase,      **lowerCamelCase)
           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           self.scheduler.timesteps.to(self.device)

           _lowercase		,				_lowercase       :							List[Any]      		=		self.get_timesteps(lowerCamelCase,      lowerCamelCase,      self.device)
           _lowercase       :							str      		=		timesteps[:1].repeat(lowerCamelCase)

           # Preprocess image
           _lowercase       :							str      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							int      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							Optional[int]      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           if clip_guidance_scale > 0:
               _lowercase       :							Optional[int]      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Dict      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Optional[int]      		=		slerp(
                   lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           _lowercase       :							Dict      		=		guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
               _lowercase       :							Tuple      		=		content_text_input.input_ids.shape[-1]
               _lowercase       :							Union[str, Any]      		=		self.tokenizer([''],      padding='max_length',      max_length=lowerCamelCase,      return_tensors='pt')
               _lowercase       :							int      		=		self.text_encoder(uncond_input.input_ids.to(self.device))[0]
               # duplicate unconditional embeddings for each generation per prompt
               _lowercase       :							Union[str, Any]      		=		uncond_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               _lowercase       :							Optional[Any]      		=		torch.cat([uncond_embeddings, text_embeddings])

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           _lowercase       :							Tuple      		=		(batch_size, self.unet.config.in_channels, height // 8, width // 8)
           _lowercase       :							Optional[int]      		=		text_embeddings.dtype
           if latents is None:
               if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   _lowercase       :							List[Any]      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device='cpu',      dtype=lowerCamelCase).to(
                       self.device)
               else:
                   _lowercase       :							Any      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device=self.device,      dtype=lowerCamelCase)
           else:
               if latents.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
               _lowercase       :							Tuple      		=		latents.to(self.device)

           # scale the initial noise by the standard deviation required by the scheduler
           _lowercase       :							List[Any]      		=		latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           _lowercase       :							Dict      		=		'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_eta:
               _lowercase       :							List[Any]      		=		eta

           # check if the scheduler accepts generator
           _lowercase       :							Dict      		=		'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
           if accepts_generator:
               _lowercase       :							str      		=		generator

           with self.progress_bar(total=lowerCamelCase):
               for i, t in enumerate(lowerCamelCase):
                   # expand the latents if we are doing classifier free guidance
                   _lowercase       :							List[str]      		=		torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                   _lowercase       :							List[Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

                   # predict the noise residual
                   _lowercase       :							Dict      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

                   # perform classifier free guidance
                   if do_classifier_free_guidance:
                       _lowercase		,				_lowercase       :							Optional[Any]      		=		noise_pred.chunk(2)
                       _lowercase       :							Optional[Any]      		=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   # perform clip guidance
                   if clip_guidance_scale > 0:
                       _lowercase       :							Tuple      		=		(
                           text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                       )
                       _lowercase		,				_lowercase       :							List[Any]      		=		self.cond_fn(
                           lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )

                   # compute the previous noisy sample x_t -> x_t-1
                   _lowercase       :							Optional[Any]      		=		self.scheduler.step(lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Any      		=		1 / 0.1_8_2_1_5 * latents
           _lowercase       :							List[str]      		=		self.vae.decode(lowerCamelCase).sample

           _lowercase       :							Tuple      		=		(image / 2 + 0.5).clamp(0,      1)
           _lowercase       :							List[Any]      		=		image.cpu().permute(0,      2,      3,      1).numpy()

           if output_type == "pil":
               _lowercase       :							List[Any]      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image, None)

           return StableDiffusionPipelineOutput(images=lowerCamelCase,      nsfw_content_detected=lowerCamelCase)






from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class        _a							(			UpperCamelCase__			):
							_lowercase	:			torch.FloatTensor


class        _a							(			nn.Module			):

							def __init__(							self:	int    ,						UpperCamelCase_:	int=3    ,						UpperCamelCase_:	Optional[Any]=3    ,						UpperCamelCase_:	Union[str, Any]=("DownEncoderBlock2D",)    ,						UpperCamelCase_:	Optional[int]=(64,)    ,						UpperCamelCase_:	Tuple=2    ,						UpperCamelCase_:	Dict=32    ,						UpperCamelCase_:	Any="silu"    ,						UpperCamelCase_:	Optional[int]=True    ,						)			->   Union[str, Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      layers_per_block

									lowercase__            =      torch.nn.Convad(
									    UpperCamelCase_    ,						block_out_channels[0]    ,						kernel_size=3    ,						stride=1    ,						padding=1    ,						)

									lowercase__            =      None
									lowercase__            =      nn.ModuleList([]							)

									# down
									lowercase__            =      block_out_channels[0]
									for i, down_block_type in enumerate(UpperCamelCase_							):
											lowercase__            =      output_channel
											lowercase__            =      block_out_channels[i]
											lowercase__            =      i == len(UpperCamelCase_							) - 1

											lowercase__            =      get_down_block(
											    UpperCamelCase_    ,						num_layers=self.layers_per_block    ,						in_channels=UpperCamelCase_    ,						out_channels=UpperCamelCase_    ,						add_downsample=not is_final_block    ,						resnet_eps=1E-6    ,						downsample_padding=0    ,						resnet_act_fn=UpperCamelCase_    ,						resnet_groups=UpperCamelCase_    ,						attention_head_dim=UpperCamelCase_    ,						temb_channels=UpperCamelCase_    ,						)
											self.down_blocks.append(UpperCamelCase_							)

									# mid
									lowercase__            =      UNetMidBlockaD(
									    in_channels=block_out_channels[-1]    ,						resnet_eps=1E-6    ,						resnet_act_fn=UpperCamelCase_    ,						output_scale_factor=1    ,						resnet_time_scale_shift='''default'''    ,						attention_head_dim=block_out_channels[-1]    ,						resnet_groups=UpperCamelCase_    ,						temb_channels=UpperCamelCase_    ,						)

									# out
									lowercase__            =      nn.GroupNorm(num_channels=block_out_channels[-1]    ,						num_groups=UpperCamelCase_    ,						eps=1E-6							)
									lowercase__            =      nn.SiLU()

									lowercase__            =      2 * out_channels if double_z else out_channels
									lowercase__            =      nn.Convad(block_out_channels[-1]    ,						UpperCamelCase_    ,						3    ,						padding=1							)

									lowercase__            =      False


							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	str							)			->   str:





									"""simple docstring"""







									lowercase__            =      x
									lowercase__            =      self.conv_in(UpperCamelCase_							)

									if self.training and self.gradient_checkpointing:

											def create_custom_forward(UpperCamelCase_:	Dict							):
													def custom_forward(*UpperCamelCase_:	List[str]							):
															return module(*UpperCamelCase_							)

													return custom_forward

											# down
											if is_torch_version('''>='''    ,						'''1.11.0'''							):
													for down_block in self.down_blocks:
															lowercase__            =      torch.utils.checkpoint.checkpoint(
															    create_custom_forward(UpperCamelCase_							)    ,						UpperCamelCase_    ,						use_reentrant=UpperCamelCase_							)
													# middle
													lowercase__            =      torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block							)    ,						UpperCamelCase_    ,						use_reentrant=UpperCamelCase_							)
											else:
													for down_block in self.down_blocks:
															lowercase__            =      torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase_							)    ,						UpperCamelCase_							)
													# middle
													lowercase__            =      torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)    ,						UpperCamelCase_							)

									else:
											# down
											for down_block in self.down_blocks:
													lowercase__            =      down_block(UpperCamelCase_							)

											# middle
											lowercase__            =      self.mid_block(UpperCamelCase_							)

									# post-process
									lowercase__            =      self.conv_norm_out(UpperCamelCase_							)
									lowercase__            =      self.conv_act(UpperCamelCase_							)
									lowercase__            =      self.conv_out(UpperCamelCase_							)

									return sample


class        _a							(			nn.Module			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	int=3    ,						UpperCamelCase_:	Optional[Any]=3    ,						UpperCamelCase_:	int=("UpDecoderBlock2D",)    ,						UpperCamelCase_:	Any=(64,)    ,						UpperCamelCase_:	Optional[Any]=2    ,						UpperCamelCase_:	List[Any]=32    ,						UpperCamelCase_:	Optional[Any]="silu"    ,						UpperCamelCase_:	Dict="group"    ,						)			->   Any:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      layers_per_block

									lowercase__            =      nn.Convad(
									    UpperCamelCase_    ,						block_out_channels[-1]    ,						kernel_size=3    ,						stride=1    ,						padding=1    ,						)

									lowercase__            =      None
									lowercase__            =      nn.ModuleList([]							)

									lowercase__            =      in_channels if norm_type == '''spatial''' else None

									# mid
									lowercase__            =      UNetMidBlockaD(
									    in_channels=block_out_channels[-1]    ,						resnet_eps=1E-6    ,						resnet_act_fn=UpperCamelCase_    ,						output_scale_factor=1    ,						resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type    ,						attention_head_dim=block_out_channels[-1]    ,						resnet_groups=UpperCamelCase_    ,						temb_channels=UpperCamelCase_    ,						)

									# up
									lowercase__            =      list(reversed(UpperCamelCase_							)							)
									lowercase__            =      reversed_block_out_channels[0]
									for i, up_block_type in enumerate(UpperCamelCase_							):
											lowercase__            =      output_channel
											lowercase__            =      reversed_block_out_channels[i]

											lowercase__            =      i == len(UpperCamelCase_							) - 1

											lowercase__            =      get_up_block(
											    UpperCamelCase_    ,						num_layers=self.layers_per_block + 1    ,						in_channels=UpperCamelCase_    ,						out_channels=UpperCamelCase_    ,						prev_output_channel=UpperCamelCase_    ,						add_upsample=not is_final_block    ,						resnet_eps=1E-6    ,						resnet_act_fn=UpperCamelCase_    ,						resnet_groups=UpperCamelCase_    ,						attention_head_dim=UpperCamelCase_    ,						temb_channels=UpperCamelCase_    ,						resnet_time_scale_shift=UpperCamelCase_    ,						)
											self.up_blocks.append(UpperCamelCase_							)
											lowercase__            =      output_channel

									# out
									if norm_type == "spatial":
											lowercase__            =      SpatialNorm(block_out_channels[0]    ,						UpperCamelCase_							)
									else:
											lowercase__            =      nn.GroupNorm(num_channels=block_out_channels[0]    ,						num_groups=UpperCamelCase_    ,						eps=1E-6							)
									lowercase__            =      nn.SiLU()
									lowercase__            =      nn.Convad(block_out_channels[0]    ,						UpperCamelCase_    ,						3    ,						padding=1							)

									lowercase__            =      False


							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	Union[str, Any]    ,						UpperCamelCase_:	List[str]=None							)			->   int:





									"""simple docstring"""







									lowercase__            =      z
									lowercase__            =      self.conv_in(UpperCamelCase_							)

									lowercase__            =      next(iter(self.up_blocks.parameters()							)							).dtype
									if self.training and self.gradient_checkpointing:

											def create_custom_forward(UpperCamelCase_:	List[str]							):
													def custom_forward(*UpperCamelCase_:	int							):
															return module(*UpperCamelCase_							)

													return custom_forward

											if is_torch_version('''>='''    ,						'''1.11.0'''							):
													# middle
													lowercase__            =      torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block							)    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						use_reentrant=UpperCamelCase_							)
													lowercase__            =      sample.to(UpperCamelCase_							)

													# up
													for up_block in self.up_blocks:
															lowercase__            =      torch.utils.checkpoint.checkpoint(
															    create_custom_forward(UpperCamelCase_							)    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						use_reentrant=UpperCamelCase_							)
											else:
													# middle
													lowercase__            =      torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block							)    ,						UpperCamelCase_    ,						UpperCamelCase_							)
													lowercase__            =      sample.to(UpperCamelCase_							)

													# up
													for up_block in self.up_blocks:
															lowercase__            =      torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase_							)    ,						UpperCamelCase_    ,						UpperCamelCase_							)
									else:
											# middle
											lowercase__            =      self.mid_block(UpperCamelCase_    ,						UpperCamelCase_							)
											lowercase__            =      sample.to(UpperCamelCase_							)

											# up
											for up_block in self.up_blocks:
													lowercase__            =      up_block(UpperCamelCase_    ,						UpperCamelCase_							)

        # post-process
									if latent_embeds is None:
											lowercase__            =      self.conv_norm_out(UpperCamelCase_							)
									else:
											lowercase__            =      self.conv_norm_out(UpperCamelCase_    ,						UpperCamelCase_							)
									lowercase__            =      self.conv_act(UpperCamelCase_							)
									lowercase__            =      self.conv_out(UpperCamelCase_							)

									return sample


class        _a							(			nn.Module			):

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[int]=None    ,						UpperCamelCase_:	List[str]="random"    ,						UpperCamelCase_:	Union[str, Any]=False    ,						UpperCamelCase_:	Dict=True							)			->   List[Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      n_e
									lowercase__            =      vq_embed_dim
									lowercase__            =      beta
									lowercase__            =      legacy

									lowercase__            =      nn.Embedding(self.n_e    ,						self.vq_embed_dim							)
									self.embedding.weight.data.uniform_(-1.0 / self.n_e    ,						1.0 / self.n_e							)

									lowercase__            =      remap
									if self.remap is not None:
											self.register_buffer('''used'''    ,						torch.tensor(np.load(self.remap							)							)							)
											lowercase__            =      self.used.shape[0]
											lowercase__            =      unknown_index  # "random" or "extra" or integer
											if self.unknown_index == "extra":
													lowercase__            =      self.re_embed
													lowercase__            =      self.re_embed + 1
											print(
											    f'Remapping {self.n_e} indices to {self.re_embed} indices. '
											    f'Using {self.unknown_index} for unknown indices.'							)
									else:
											lowercase__            =      n_e

									lowercase__            =      sane_index_shape

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Any							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      inds.shape
									assert len(UpperCamelCase_							) > 1
									lowercase__            =      inds.reshape(ishape[0]    ,						-1							)
									lowercase__            =      self.used.to(UpperCamelCase_							)
									lowercase__            =      (inds[:, :, None] == used[None, None, ...]).long()
									lowercase__            =      match.argmax(-1							)
									lowercase__            =      match.sum(2							) < 1
									if self.unknown_index == "random":
											lowercase__            =      torch.randint(0    ,						self.re_embed    ,						size=new[unknown].shape							).to(device=new.device							)
									else:
											lowercase__            =      self.unknown_index
									return new.reshape(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Tuple							)			->   str:





									"""simple docstring"""







									lowercase__            =      inds.shape
									assert len(UpperCamelCase_							) > 1
									lowercase__            =      inds.reshape(ishape[0]    ,						-1							)
									lowercase__            =      self.used.to(UpperCamelCase_							)
									if self.re_embed > self.used.shape[0]:  # extra token
											lowercase__            =      0  # simply set to zero
									lowercase__            =      torch.gather(used[None, :][inds.shape[0] * [0], :]    ,						1    ,						UpperCamelCase_							)
									return back.reshape(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Any							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      z.permute(0    ,						2    ,						3    ,						1							).contiguous()
									lowercase__            =      z.view(-1    ,						self.vq_embed_dim							)

									# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
									lowercase__            =      torch.argmin(torch.cdist(UpperCamelCase_    ,						self.embedding.weight							)    ,						dim=1							)

									lowercase__            =      self.embedding(UpperCamelCase_							).view(z.shape							)
									lowercase__            =      None
									lowercase__            =      None

									# compute loss for embedding
									if not self.legacy:
											lowercase__            =      self.beta * torch.mean((z_q.detach() - z) ** 2							) + torch.mean((z_q - z.detach()) ** 2							)
									else:
											lowercase__            =      torch.mean((z_q.detach() - z) ** 2							) + self.beta * torch.mean((z_q - z.detach()) ** 2							)

									# preserve gradients
									lowercase__            =      z + (z_q - z).detach()

									# reshape back to match original input shape
									lowercase__            =      z_q.permute(0    ,						3    ,						1    ,						2							).contiguous()

									if self.remap is not None:
											lowercase__            =      min_encoding_indices.reshape(z.shape[0]    ,						-1							)  # add batch axis
											lowercase__            =      self.remap_to_used(UpperCamelCase_							)
											lowercase__            =      min_encoding_indices.reshape(-1    ,						1							)  # flatten

									if self.sane_index_shape:
											lowercase__            =      min_encoding_indices.reshape(z_q.shape[0]    ,						z_q.shape[2]    ,						z_q.shape[3]							)

									return z_q, loss, (perplexity, min_encodings, min_encoding_indices)


							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	List[Any]							)			->   List[str]:





									"""simple docstring"""







									if self.remap is not None:
											lowercase__            =      indices.reshape(shape[0]    ,						-1							)  # add batch axis
											lowercase__            =      self.unmap_to_all(UpperCamelCase_							)
											lowercase__            =      indices.reshape(-1							)  # flatten again

									# get quantized latent vectors
									lowercase__            =      self.embedding(UpperCamelCase_							)

									if shape is not None:
											lowercase__            =      z_q.view(UpperCamelCase_							)
											# reshape back to match original input shape
											lowercase__            =      z_q.permute(0    ,						3    ,						1    ,						2							).contiguous()

									return z_q


class        _a							(			UpperCamelCase__			):

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]=False							)			->   str:





									"""simple docstring"""







									lowercase__            =      parameters
									lowercase__     ,		lowercase__            =      torch.chunk(UpperCamelCase_    ,						2    ,						dim=1							)
									lowercase__            =      torch.clamp(self.logvar    ,						-30.0    ,						20.0							)
									lowercase__            =      deterministic
									lowercase__            =      torch.exp(0.5 * self.logvar							)
									lowercase__            =      torch.exp(self.logvar							)
									if self.deterministic:
											lowercase__            =      lowercase__            =      torch.zeros_like(
											    self.mean    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype							)

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Optional[torch.Generator] = None							)			->   torch.FloatTensor:





									"""simple docstring"""







									lowercase__            =      randn_tensor(
									    self.mean.shape    ,						generator=UpperCamelCase_    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype							)
									lowercase__            =      self.mean + self.std * sample
									return x

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	str=None							)			->   Tuple:





									"""simple docstring"""







									if self.deterministic:
											return torch.Tensor([0.0]							)
									else:
											if other is None:
													return 0.5 * torch.sum(torch.pow(self.mean    ,						2							) + self.var - 1.0 - self.logvar    ,						dim=[1, 2, 3]							)
											else:
													return 0.5 * torch.sum(
													    torch.pow(self.mean - other.mean    ,						2							) / other.var
													    + self.var / other.var
													    - 1.0
													    - self.logvar
													    + other.logvar    ,						dim=[1, 2, 3]    ,						)

							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	Any=[1, 2, 3]							)			->   Tuple:





									"""simple docstring"""







									if self.deterministic:
											return torch.Tensor([0.0]							)
									lowercase__            =      np.log(2.0 * np.pi							)
									return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean    ,						2							) / self.var    ,						dim=UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	str							)			->   Any:





									"""simple docstring"""







									return self.mean


