






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


__UpperCAmelCase								=						logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCAmelCase								=						'\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n        ```\n'



@dataclass
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[PIL.Image.Image, np.ndarray]





class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Any:
							super().__init__()

							self.register_modules(
							    prior=_UpperCamelCase				,		image_encoder=_UpperCamelCase				,		image_processor=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		renderer=_UpperCamelCase				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							if latents is None:
												UpperCAmelCase_    :      str            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=_UpperCamelCase   )
							else:
												if latents.shape != shape:
																	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
												UpperCAmelCase_    :      Tuple            =						latents.to(_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						latents * scheduler.init_noise_sigma
							return latents



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Union[str, Any]:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'   )

							UpperCAmelCase_    :      int            =						torch.device(f"cuda:{gpu_id}"   )

							UpperCAmelCase_    :      int            =						[self.image_encoder, self.prior]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(_UpperCamelCase				,		_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							if self.device != torch.device('meta'   ) or not hasattr(self.image_encoder				,		'_hf_hook'   ):
												return self.device
							for module in self.image_encoder.modules():
												if (
												    hasattr(_UpperCamelCase				,		'_hf_hook'   )
												    and hasattr(module._hf_hook				,		'execution_device'   )
												    and module._hf_hook.execution_device is not None
												):
																	return torch.device(module._hf_hook.execution_device   )
							return self.device



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							str:
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		torch.Tensor   ):
												UpperCAmelCase_    :      int            =						torch.cat(_UpperCamelCase				,		axis=0   ) if image[0].ndim == 4 else torch.stack(_UpperCamelCase				,		axis=0   )

							if not isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      Optional[int]            =						self.image_processor(_UpperCamelCase				,		return_tensors='pt'   ).pixel_values[0].unsqueeze(0   )

							UpperCAmelCase_    :      Tuple            =						image.to(dtype=self.image_encoder.dtype				,		device=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						self.image_encoder(_UpperCamelCase   )['last_hidden_state']
							UpperCAmelCase_    :      Union[str, Any]            =						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

							UpperCAmelCase_    :      List[str]            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      Dict            =						torch.zeros_like(_UpperCamelCase   )

												# 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
												UpperCAmelCase_    :      Optional[int]            =						torch.cat([negative_image_embeds, image_embeds]   )

							return image_embeds



		@torch.no_grad()
		@replace_example_docstring(_UpperCamelCase   )
		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_5				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 6_4				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							Union[str, Any]:

							if isinstance(_UpperCamelCase				,		PIL.Image.Image   ):
												UpperCAmelCase_    :      Tuple            =						1
							elif isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      str            =						image.shape[0]
							elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		(torch.Tensor, PIL.Image.Image)   ):
												UpperCAmelCase_    :      Optional[int]            =						len(_UpperCamelCase   )
							else:
												raise ValueError(
												    f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_UpperCamelCase   )}"   )

							UpperCAmelCase_    :      Tuple            =						self._execution_device

							UpperCAmelCase_    :      str            =						batch_size * num_images_per_prompt

							UpperCAmelCase_    :      str            =						guidance_scale > 1.0
							UpperCAmelCase_    :      str            =						self._encode_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							# prior

							self.scheduler.set_timesteps(_UpperCamelCase				,		device=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.scheduler.timesteps

							UpperCAmelCase_    :      int            =						self.prior.config.num_embeddings
							UpperCAmelCase_    :      Any            =						self.prior.config.embedding_dim

							UpperCAmelCase_    :      List[str]            =						self.prepare_latents(
							    (batch_size, num_embeddings * embedding_dim)				,		image_embeds.dtype				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		self.scheduler				,		)

							# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
							UpperCAmelCase_    :      List[Any]            =						latents.reshape(latents.shape[0]				,		_UpperCamelCase				,		_UpperCamelCase   )

							for i, t in enumerate(self.progress_bar(_UpperCamelCase   )   ):
												# expand the latents if we are doing classifier free guidance
												UpperCAmelCase_    :      Tuple            =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
												UpperCAmelCase_    :      Optional[Any]            =						self.scheduler.scale_model_input(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      int            =						self.prior(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		proj_embedding=_UpperCamelCase				,		).predicted_image_embedding

												# remove the variance
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						noise_pred.split(
												    scaled_model_input.shape[2]				,		dim=2   )  # batch_size, num_embeddings, embedding_dim

												if do_classifier_free_guidance is not None:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_    :      List[Any]            =						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

												UpperCAmelCase_    :      List[str]            =						self.scheduler.step(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		sample=_UpperCamelCase				,		).prev_sample

							if output_type == "latent":
												return ShapEPipelineOutput(images=_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						[]
							for i, latent in enumerate(_UpperCamelCase   ):
												print()
												UpperCAmelCase_    :      List[str]            =						self.renderer.decode(
												    latent[None, :]				,		_UpperCamelCase				,		size=_UpperCamelCase				,		ray_batch_size=4_0_9_6				,		n_coarse_samples=6_4				,		n_fine_samples=1_2_8				,		)

												images.append(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						torch.stack(_UpperCamelCase   )

							if output_type not in ["np", "pil"]:
												raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"   )

							UpperCAmelCase_    :      Dict            =						images.cpu().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[str]            =						[self.numpy_to_pil(_UpperCamelCase   ) for image in images]

							# Offload last model to CPU
							if hasattr(self				,		'final_offload_hook'   ) and self.final_offload_hook is not None:
												self.final_offload_hook.offload()

							if not return_dict:
												return (images,)

							return ShapEPipelineOutput(images=_UpperCamelCase   )














from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder



@dataclass
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    "DiagonalGaussianDistribution"





class   lowerCamelCase      (_snake_case ,  _snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  True



		@register_to_config
		def __init__(							self				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = ("DownEncoderBlock2D",)				,		_UpperCamelCase = ("UpDecoderBlock2D",)				,		_UpperCamelCase = (6_4,)				,		_UpperCamelCase = 1				,		_UpperCamelCase = "silu"				,		_UpperCamelCase = 4				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 0.1_82_15				,		)	->							List[Any]:
							super().__init__()

							# pass init params to Encoder
							UpperCAmelCase_    :      List[str]            =						Encoder(
							    in_channels=_UpperCamelCase				,		out_channels=_UpperCamelCase				,		down_block_types=_UpperCamelCase				,		block_out_channels=_UpperCamelCase				,		layers_per_block=_UpperCamelCase				,		act_fn=_UpperCamelCase				,		norm_num_groups=_UpperCamelCase				,		double_z=_UpperCamelCase				,		)

							# pass init params to Decoder
							UpperCAmelCase_    :      Dict            =						Decoder(
							    in_channels=_UpperCamelCase				,		out_channels=_UpperCamelCase				,		up_block_types=_UpperCamelCase				,		block_out_channels=_UpperCamelCase				,		layers_per_block=_UpperCamelCase				,		norm_num_groups=_UpperCamelCase				,		act_fn=_UpperCamelCase				,		)

							UpperCAmelCase_    :      Any            =						nn.Convad(2 * latent_channels				,		2 * latent_channels				,		1   )
							UpperCAmelCase_    :      List[Any]            =						nn.Convad(_UpperCamelCase				,		_UpperCamelCase				,		1   )

							UpperCAmelCase_    :      Any            =						False
							UpperCAmelCase_    :      int            =						False

							# only relevant if vae tiling is enabled
							UpperCAmelCase_    :      Optional[int]            =						self.config.sample_size
							UpperCAmelCase_    :      int            =						(
							    self.config.sample_size[0]
							    if isinstance(self.config.sample_size				,		(list, tuple)   )
							    else self.config.sample_size
							)
							UpperCAmelCase_    :      Union[str, Any]            =						int(sample_size / (2 ** (len(self.config.block_out_channels   ) - 1))   )
							UpperCAmelCase_    :      Optional[Any]            =						0.25



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False   )	->							List[str]:
							if isinstance(_UpperCamelCase				,		(Encoder, Decoder)   ):
												UpperCAmelCase_    :      Union[str, Any]            =						value



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase = True   )	->							int:
							UpperCAmelCase_    :      Tuple            =						use_tiling



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							self.enable_tiling(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      str            =						True



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      Optional[int]            =						False



		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def 					__UpperCAmelCase			(							self   )	->							Dict[str, AttentionProcessor]:
							UpperCAmelCase_    :      Optional[int]            =						{}

							def fn_recursive_add_processors(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
												if hasattr(_UpperCamelCase				,		'set_processor'   ):
																	UpperCAmelCase_    :      Optional[int]            =						module.processor

												for sub_name, child in module.named_children():
																	fn_recursive_add_processors(f"{name}.{sub_name}"				,		_UpperCamelCase				,		_UpperCamelCase   )

												return processors

							for name, module in self.named_children():
												fn_recursive_add_processors(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							return processors



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Union[str, Any]            =						len(self.attn_processors.keys()   )

							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and len(_UpperCamelCase   ) != count:
												raise ValueError(
												    f"A dict of processors was passed, but the number of processors {len(_UpperCamelCase   )} does not match the"
												    f" number of attention layers: {count}. Please make sure to pass {count} processor classes."   )

							def fn_recursive_attn_processor(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
												if hasattr(_UpperCamelCase				,		'set_processor'   ):
																	if not isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
																						module.set_processor(_UpperCamelCase   )
																	else:
																						module.set_processor(processor.pop(f"{name}.processor"   )   )

												for sub_name, child in module.named_children():
																	fn_recursive_attn_processor(f"{name}.{sub_name}"				,		_UpperCamelCase				,		_UpperCamelCase   )

							for name, module in self.named_children():
												fn_recursive_attn_processor(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							self.set_attn_processor(AttnProcessor()   )



		@apply_forward_hook
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							AutoencoderKLOutput:
							if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
												return self.tiled_encode(_UpperCamelCase				,		return_dict=_UpperCamelCase   )

							if self.use_slicing and x.shape[0] > 1:
												UpperCAmelCase_    :      Union[str, Any]            =						[self.encoder(_UpperCamelCase   ) for x_slice in x.split(1   )]
												UpperCAmelCase_    :      Tuple            =						torch.cat(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      List[Any]            =						self.encoder(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						self.quant_conv(_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						DiagonalGaussianDistribution(_UpperCamelCase   )

							if not return_dict:
												return (posterior,)

							return AutoencoderKLOutput(latent_dist=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							Union[DecoderOutput, torch.FloatTensor]:
							if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
												return self.tiled_decode(_UpperCamelCase				,		return_dict=_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						self.post_quant_conv(_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						self.decoder(_UpperCamelCase   )

							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=_UpperCamelCase   )



		@apply_forward_hook
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							Union[DecoderOutput, torch.FloatTensor]:
							if self.use_slicing and z.shape[0] > 1:
												UpperCAmelCase_    :      List[str]            =						[self._decode(_UpperCamelCase   ).sample for z_slice in z.split(1   )]
												UpperCAmelCase_    :      Dict            =						torch.cat(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Any            =						self._decode(_UpperCamelCase   ).sample

							if not return_dict:
												return (decoded,)

							return DecoderOutput(sample=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Any:
							UpperCAmelCase_    :      Tuple            =						min(a.shape[2]				,		b.shape[2]				,		_UpperCamelCase   )
							for y in range(_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Tuple            =						min(a.shape[3]				,		b.shape[3]				,		_UpperCamelCase   )
							for x in range(_UpperCamelCase   ):
												UpperCAmelCase_    :      int            =						a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							AutoencoderKLOutput:
							UpperCAmelCase_    :      Any            =						int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)   )
							UpperCAmelCase_    :      Tuple            =						int(self.tile_latent_min_size * self.tile_overlap_factor   )
							UpperCAmelCase_    :      Optional[int]            =						self.tile_latent_min_size - blend_extent

							# Split the image into 512x512 tiles and encode them separately.
							UpperCAmelCase_    :      List[str]            =						[]
							for i in range(0				,		x.shape[2]				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Any            =						[]
												for j in range(0				,		x.shape[3]				,		_UpperCamelCase   ):
																	UpperCAmelCase_    :      Any            =						x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																	UpperCAmelCase_    :      Dict            =						self.encoder(_UpperCamelCase   )
																	UpperCAmelCase_    :      List[str]            =						self.quant_conv(_UpperCamelCase   )
																	row.append(_UpperCamelCase   )
												rows.append(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						[]
							for i, row in enumerate(_UpperCamelCase   ):
												UpperCAmelCase_    :      List[Any]            =						[]
												for j, tile in enumerate(_UpperCamelCase   ):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																						UpperCAmelCase_    :      Dict            =						self.blend_v(rows[i - 1][j]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	if j > 0:
																						UpperCAmelCase_    :      List[str]            =						self.blend_h(row[j - 1]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	result_row.append(tile[:, :, :row_limit, :row_limit]   )
												result_rows.append(torch.cat(_UpperCamelCase				,		dim=3   )   )

							UpperCAmelCase_    :      Union[str, Any]            =						torch.cat(_UpperCamelCase				,		dim=2   )
							UpperCAmelCase_    :      List[Any]            =						DiagonalGaussianDistribution(_UpperCamelCase   )

							if not return_dict:
												return (posterior,)

							return AutoencoderKLOutput(latent_dist=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							Union[DecoderOutput, torch.FloatTensor]:
							UpperCAmelCase_    :      str            =						int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)   )
							UpperCAmelCase_    :      Dict            =						int(self.tile_sample_min_size * self.tile_overlap_factor   )
							UpperCAmelCase_    :      Dict            =						self.tile_sample_min_size - blend_extent

							# Split z into overlapping 64x64 tiles and decode them separately.
							# The tiles have an overlap to avoid seams between tiles.
							UpperCAmelCase_    :      Union[str, Any]            =						[]
							for i in range(0				,		z.shape[2]				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      List[str]            =						[]
												for j in range(0				,		z.shape[3]				,		_UpperCamelCase   ):
																	UpperCAmelCase_    :      List[str]            =						z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																	UpperCAmelCase_    :      Optional[Any]            =						self.post_quant_conv(_UpperCamelCase   )
																	UpperCAmelCase_    :      Tuple            =						self.decoder(_UpperCamelCase   )
																	row.append(_UpperCamelCase   )
												rows.append(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						[]
							for i, row in enumerate(_UpperCamelCase   ):
												UpperCAmelCase_    :      List[Any]            =						[]
												for j, tile in enumerate(_UpperCamelCase   ):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																						UpperCAmelCase_    :      Union[str, Any]            =						self.blend_v(rows[i - 1][j]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	if j > 0:
																						UpperCAmelCase_    :      Optional[Any]            =						self.blend_h(row[j - 1]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	result_row.append(tile[:, :, :row_limit, :row_limit]   )
												result_rows.append(torch.cat(_UpperCamelCase				,		dim=3   )   )

							UpperCAmelCase_    :      Dict            =						torch.cat(_UpperCamelCase				,		dim=2   )
							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = False				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		)	->							Union[DecoderOutput, torch.FloatTensor]:
							UpperCAmelCase_    :      Optional[Any]            =						sample
							UpperCAmelCase_    :      Union[str, Any]            =						self.encode(_UpperCamelCase   ).latent_dist
							if sample_posterior:
												UpperCAmelCase_    :      str            =						posterior.sample(generator=_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      int            =						posterior.mode()
							UpperCAmelCase_    :      Dict            =						self.decode(_UpperCamelCase   ).sample

							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=_UpperCamelCase   )






