






"""simple docstring"""

import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowercase    			=       logging.get_logger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     SwinConfig.from_pretrained(
						    "microsoft/swin-tiny-patch4-window7-224" ,						out_features=["stage1", "stage2", "stage3", "stage4"]   )
						__magic_name__            =     MaskFormerConfig(backbone_config=__lowerCamelCase   )

						__magic_name__            =     "huggingface/label-files"
						if "ade20k-full" in model_name:
												# this should be ok
												__magic_name__            =     8_4_7
												__magic_name__            =     "maskformer-ade20k-full-id2label.json"
						elif "ade" in model_name:
												# this should be ok
												__magic_name__            =     1_5_0
												__magic_name__            =     "ade20k-id2label.json"
						elif "coco-stuff" in model_name:
												# this should be ok
												__magic_name__            =     1_7_1
												__magic_name__            =     "maskformer-coco-stuff-id2label.json"
						elif "coco" in model_name:
												# TODO
												__magic_name__            =     1_3_3
												__magic_name__            =     "coco-panoptic-id2label.json"
						elif "cityscapes" in model_name:
												# this should be ok
												__magic_name__            =     1_9
												__magic_name__            =     "cityscapes-id2label.json"
						elif "vistas" in model_name:
												# this should be ok
												__magic_name__            =     6_5
												__magic_name__            =     "mapillary-vistas-id2label.json"

						__magic_name__            =     json.load(open(hf_hub_download(__lowerCamelCase ,						__lowerCamelCase ,						repo_type="dataset"   ) ,						"r"   )   )
						__magic_name__            =     {int(__lowerCamelCase   ): v for k, v in idalabel.items()}

						return config






def _lowerCAmelCase	(  __lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     []
						# stem
						# fmt: off
						rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")   )
						rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")   )
						rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")   )
						rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")   )
						# stages
						for i in range(len(config.backbone_config.depths   )   ):
												for j in range(config.backbone_config.depths[i]   ):
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')   )
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')   )

												if i < 3:
																		rename_keys.append((f'''backbone.layers.{i}.downsample.reduction.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')   )
																		rename_keys.append((f'''backbone.layers.{i}.downsample.norm.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')   )
																		rename_keys.append((f'''backbone.layers.{i}.downsample.norm.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')   )
												rename_keys.append((f'''backbone.norm{i}.weight''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')   )
												rename_keys.append((f'''backbone.norm{i}.bias''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')   )

						# FPN
						rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")   )
						rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")   )
						rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")   )
						for source_index, target_index in zip(range(3 ,						0 ,						-1   ) ,						range(0 ,						3   )   ):
												rename_keys.append((f'''sem_seg_head.adapter_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')   )
												rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')   )
												rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')   )
												rename_keys.append((f'''sem_seg_head.layer_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')   )
												rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')   )
												rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')   )
						rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")   )
						rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")   )

						# Transformer decoder
						for idx in range(config.decoder_config.decoder_layers   ):
												# self-attention out projection
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')   )
												# cross-attention out projection
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')   )
												# MLP 1
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')   )
												# MLP 2
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')   )
												# layernorm 1 (self-attention layernorm)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')   )
												# layernorm 2 (cross-attention layernorm)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')   )
												# layernorm 3 (final layernorm)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')   )

						rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")   )
						rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")   )

						# heads on top
						rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")   )

						rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")   )
						rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")   )

						rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")   )
						rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")   )

						for i in range(3   ):
												rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', f'''mask_embedder.{i}.0.weight''')   )
												rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', f'''mask_embedder.{i}.0.bias''')   )
						# fmt: on

						return rename_keys






def _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						__magic_name__            =     dct.pop(__lowerCamelCase   )
						__magic_name__            =     val






def _lowerCAmelCase	(  __lowerCamelCase:Any ,						__lowerCamelCase:Dict   ):

						'''simple docstring'''






						__magic_name__            =     [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
						for i in range(len(backbone_config.depths   )   ):
												__magic_name__            =     num_features[i]
												for j in range(backbone_config.depths[i]   ):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		__magic_name__            =     state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''   )
																		__magic_name__            =     state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''   )
																		# next, add query, keys and values (in that order) to the state dict
																		__magic_name__            =     in_proj_weight[:dim, :]
																		__magic_name__            =     in_proj_bias[: dim]
																		__magic_name__            =     in_proj_weight[
																		    dim : dim * 2, :
																		]
																		__magic_name__            =     in_proj_bias[
																		    dim : dim * 2
																		]
																		__magic_name__            =     in_proj_weight[
																		    -dim :, :
																		]
																		__magic_name__            =     in_proj_bias[-dim :]
																		# fmt: on






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     config.decoder_config.hidden_size
						for idx in range(config.decoder_config.decoder_layers   ):
												# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
												__magic_name__            =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''   )
												__magic_name__            =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''   )
												# next, add query, keys and values (in that order) to the state dict
												__magic_name__            =     in_proj_weight[: hidden_size, :]
												__magic_name__            =     in_proj_bias[:config.hidden_size]
												__magic_name__            =     in_proj_weight[hidden_size : hidden_size * 2, :]
												__magic_name__            =     in_proj_bias[hidden_size : hidden_size * 2]
												__magic_name__            =     in_proj_weight[-hidden_size :, :]
												__magic_name__            =     in_proj_bias[-hidden_size :]
												# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
												__magic_name__            =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''   )
												__magic_name__            =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''   )
												# next, add query, keys and values (in that order) to the state dict
												__magic_name__            =     in_proj_weight[: hidden_size, :]
												__magic_name__            =     in_proj_bias[:config.hidden_size]
												__magic_name__            =     in_proj_weight[hidden_size : hidden_size * 2, :]
												__magic_name__            =     in_proj_bias[hidden_size : hidden_size * 2]
												__magic_name__            =     in_proj_weight[-hidden_size :, :]
												__magic_name__            =     in_proj_bias[-hidden_size :]
						# fmt: on






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     "http://images.cocodataset.org/val2017/000000039769.jpg"
						__magic_name__            =     Image.open(requests.get(__lowerCamelCase ,						stream=__lowerCamelCase   ).raw   )
						return im






@torch.no_grad()
def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:bool = False   ):

						'''simple docstring'''






						__magic_name__            =     get_maskformer_config(__lowerCamelCase   )

						# load original state_dict
						with open(__lowerCamelCase ,						"rb"   ) as f:
												__magic_name__            =     pickle.load(__lowerCamelCase   )
						__magic_name__            =     data["model"]

						# for name, param in state_dict.items():
						#     print(name, param.shape)

						# rename keys
						__magic_name__            =     create_rename_keys(__lowerCamelCase   )
						for src, dest in rename_keys:
												rename_key(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
						read_in_swin_q_k_v(__lowerCamelCase ,						config.backbone_config   )
						read_in_decoder_q_k_v(__lowerCamelCase ,						__lowerCamelCase   )

						# update to torch tensors
						for key, value in state_dict.items():
												__magic_name__            =     torch.from_numpy(__lowerCamelCase   )

						# load 🤗 model
						__magic_name__            =     MaskFormerForInstanceSegmentation(__lowerCamelCase   )
						model.eval()

						for name, param in model.named_parameters():
												print(__lowerCamelCase ,						param.shape   )

						__magic_name__      ,    __magic_name__            =     model.load_state_dict(__lowerCamelCase ,						strict=__lowerCamelCase   )
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(__lowerCamelCase   ) == 0, f'''Unexpected keys: {unexpected_keys}'''

						# verify results
						__magic_name__            =     prepare_img()
						if "vistas" in model_name:
												__magic_name__            =     6_5
						elif "cityscapes" in model_name:
												__magic_name__            =     6_5_5_3_5
						else:
												__magic_name__            =     2_5_5
						__magic_name__            =     True if "ade" in model_name else False
						__magic_name__            =     MaskFormerImageProcessor(ignore_index=__lowerCamelCase ,						reduce_labels=__lowerCamelCase   )

						__magic_name__            =     image_processor(__lowerCamelCase ,						return_tensors="pt"   )

						__magic_name__            =     model(**__lowerCamelCase   )

						print("Logits:" ,						outputs.class_queries_logits[0, :3, :3]   )

						if model_name == "maskformer-swin-tiny-ade":
												__magic_name__            =     torch.tensor(
												    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]   )
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,						__lowerCamelCase ,						atol=1E-4   )
						print("Looks ok!"   )

						if pytorch_dump_folder_path is not None:
												print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''   )
												Path(__lowerCamelCase   ).mkdir(exist_ok=__lowerCamelCase   )
												model.save_pretrained(__lowerCamelCase   )
												image_processor.save_pretrained(__lowerCamelCase   )

						if push_to_hub:
												print("Pushing model and image processor to the hub..."   )
												model.push_to_hub(f'''nielsr/{model_name}'''   )
												image_processor.push_to_hub(f'''nielsr/{model_name}'''   )


if __name__ == "__main__":
			lowercase    			=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''maskformer-swin-tiny-ade''',
			    type=str,
			    help=('''Name of the MaskFormer model you\'d like to convert''',),
			)
			parser.add_argument(
			    '''--checkpoint_path''',
			    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
			    type=str,
			    help='''Path to the original state dict (.pth file).''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			lowercase    			=       parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)











"""simple docstring"""

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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				42





class 						A_							(      snake_case_					,							snake_case_  ):
				UpperCAmelCase__        =				True







				@register_to_config
				def __init__(							self							:						Any						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						Tuple[str] = ("DownEncoderBlock2D",)						,    __lowerCamelCase							:						Tuple[str] = ("UpDecoderBlock2D",)						,    __lowerCamelCase							:						Tuple[int] = (6_4,)						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						str = "silu"						,    __lowerCamelCase							:						int = 4						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						float = 0.1_8215						,    )				->					Any:
										super().__init__()

										# pass init params to Encoder
										__magic_name__            =     Encoder(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    down_block_types=__lowerCamelCase						,    block_out_channels=__lowerCamelCase						,    layers_per_block=__lowerCamelCase						,    act_fn=__lowerCamelCase						,    norm_num_groups=__lowerCamelCase						,    double_z=__lowerCamelCase						,    )

										# pass init params to Decoder
										__magic_name__            =     Decoder(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    up_block_types=__lowerCamelCase						,    block_out_channels=__lowerCamelCase						,    layers_per_block=__lowerCamelCase						,    norm_num_groups=__lowerCamelCase						,    act_fn=__lowerCamelCase						,    )

										__magic_name__            =     nn.Convad(2 * latent_channels						,    2 * latent_channels						,    1	)
										__magic_name__            =     nn.Convad(__lowerCamelCase						,    __lowerCamelCase						,    1	)

										__magic_name__            =     False
										__magic_name__            =     False

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







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=False	)				->					Optional[int]:
										if isinstance(__lowerCamelCase						,    (Encoder, Decoder)	):
																__magic_name__            =     value







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						bool = True	)				->					int:
										__magic_name__            =     use_tiling







				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										self.enable_tiling(__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     True







				def     _snake_case					(							self							:						Optional[Any]	)				->					Tuple:
										__magic_name__            =     False







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

										def fn_recursive_add_processors(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						Dict[str, AttentionProcessor]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						__magic_name__            =     module.processor

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

																return processors

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

										return processors







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]	)				->					int:
										__magic_name__            =     len(self.attn_processors.keys()	)

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

										def fn_recursive_attn_processor(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						List[str]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						if not isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																												module.set_processor(__lowerCamelCase	)
																						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}'''						,    __lowerCamelCase						,    __lowerCamelCase	)

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







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										self.set_attn_processor(AttnProcessor()	)







				@apply_forward_hook
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = 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(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										if self.use_slicing and x.shape[0] > 1:
																__magic_name__            =     [self.encoder(__lowerCamelCase	) for x_slice in x.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self.encoder(__lowerCamelCase	)

										__magic_name__            =     self.quant_conv(__lowerCamelCase	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = 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(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
										__magic_name__            =     self.decoder(__lowerCamelCase	)

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				@apply_forward_hook
				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_slicing and z.shape[0] > 1:
																__magic_name__            =     [self._decode(__lowerCamelCase	).sample for z_slice in z.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self._decode(__lowerCamelCase	).sample

										if not return_dict:
																return (decoded,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[2]						,    b.shape[2]						,    __lowerCamelCase	)
										for y in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
										return b







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[3]						,    b.shape[3]						,    __lowerCamelCase	)
										for x in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
										return b







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										__magic_name__            =     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_latent_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_latent_min_size - blend_extent

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

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_sample_min_size * self.tile_overlap_factor	)
										__magic_name__            =     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.
										__magic_name__            =     []
										for i in range(0						,    z.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    z.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
																						__magic_name__            =     self.decoder(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																for j, tile in enumerate(__lowerCamelCase	):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[torch.Generator] = None						,    )				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     sample
										__magic_name__            =     self.encode(__lowerCamelCase	).latent_dist
										if sample_posterior:
																__magic_name__            =     posterior.sample(generator=__lowerCamelCase	)
										else:
																__magic_name__            =     posterior.mode()
										__magic_name__            =     self.decode(__lowerCamelCase	).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)



