



import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase     :							Optional[Any]  							=				logging.get_logger(__name__)

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												UpperCAmelCase         =     [144, 192, 240]
												UpperCAmelCase         =     [16, 32, 64, 96, 128, 160, 640]
						elif "mobilevit_xs" in mobilevit_name:
												UpperCAmelCase         =     [96, 120, 144]
												UpperCAmelCase         =     [16, 32, 48, 64, 80, 96, 384]
						elif "mobilevit_xxs" in mobilevit_name:
												UpperCAmelCase         =     [64, 80, 96]
												UpperCAmelCase         =     [16, 16, 24, 48, 64, 80, 320]
												UpperCAmelCase         =     0.05
												UpperCAmelCase         =     2.0

						if mobilevit_name.startswith("deeplabv3_"    ):
												UpperCAmelCase         =     512
												UpperCAmelCase         =     16
												UpperCAmelCase         =     21
												UpperCAmelCase         =     "pascal-voc-id2label.json"
						else:
												UpperCAmelCase         =     1_000
												UpperCAmelCase         =     "imagenet-1k-id2label.json"

						UpperCAmelCase         =     "huggingface/label-files"
						UpperCAmelCase         =     json.load(open(hf_hub_download(lowerCamelCase_    ,   lowerCamelCase_    ,   repo_type="dataset"    )    ,   "r"    )    )
						UpperCAmelCase         =     {int(lowerCamelCase_    ): v for k, v in idalabel.items()}
						UpperCAmelCase         =     idalabel
						UpperCAmelCase         =     {v: k for k, v in idalabel.items()}

						return config

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_=False    ) ->       str:
						for i in range(1    ,   6    ):
												if F'layer_{i}.' in name:
																		UpperCAmelCase         =     name.replace(F'layer_{i}.'    ,   F'encoder.layer.{i - 1}.'    )

						if "conv_1." in name:
												UpperCAmelCase         =     name.replace("conv_1."    ,   "conv_stem."    )
						if ".block." in name:
												UpperCAmelCase         =     name.replace(".block."    ,   "."    )

						if "exp_1x1" in name:
												UpperCAmelCase         =     name.replace("exp_1x1"    ,   "expand_1x1"    )
						if "red_1x1" in name:
												UpperCAmelCase         =     name.replace("red_1x1"    ,   "reduce_1x1"    )
						if ".local_rep.conv_3x3." in name:
												UpperCAmelCase         =     name.replace(".local_rep.conv_3x3."    ,   ".conv_kxk."    )
						if ".local_rep.conv_1x1." in name:
												UpperCAmelCase         =     name.replace(".local_rep.conv_1x1."    ,   ".conv_1x1."    )
						if ".norm." in name:
												UpperCAmelCase         =     name.replace(".norm."    ,   ".normalization."    )
						if ".conv." in name:
												UpperCAmelCase         =     name.replace(".conv."    ,   ".convolution."    )
						if ".conv_proj." in name:
												UpperCAmelCase         =     name.replace(".conv_proj."    ,   ".conv_projection."    )

						for i in range(0    ,   2    ):
												for j in range(0    ,   4    ):
																		if F'.{i}.{j}.' in name:
																								UpperCAmelCase         =     name.replace(F'.{i}.{j}.'    ,   F'.{i}.layer.{j}.'    )

						for i in range(2    ,   6    ):
												for j in range(0    ,   4    ):
																		if F'.{i}.{j}.' in name:
																								UpperCAmelCase         =     name.replace(F'.{i}.{j}.'    ,   F'.{i}.'    )
																								if "expand_1x1" in name:
																														UpperCAmelCase         =     name.replace("expand_1x1"    ,   "downsampling_layer.expand_1x1"    )
																								if "conv_3x3" in name:
																														UpperCAmelCase         =     name.replace("conv_3x3"    ,   "downsampling_layer.conv_3x3"    )
																								if "reduce_1x1" in name:
																														UpperCAmelCase         =     name.replace("reduce_1x1"    ,   "downsampling_layer.reduce_1x1"    )

						for i in range(2    ,   5    ):
												if F'.global_rep.{i}.weight' in name:
																		UpperCAmelCase         =     name.replace(F'.global_rep.{i}.weight'    ,   ".layernorm.weight"    )
												if F'.global_rep.{i}.bias' in name:
																		UpperCAmelCase         =     name.replace(F'.global_rep.{i}.bias'    ,   ".layernorm.bias"    )

						if ".global_rep." in name:
												UpperCAmelCase         =     name.replace(".global_rep."    ,   ".transformer."    )
						if ".pre_norm_mha.0." in name:
												UpperCAmelCase         =     name.replace(".pre_norm_mha.0."    ,   ".layernorm_before."    )
						if ".pre_norm_mha.1.out_proj." in name:
												UpperCAmelCase         =     name.replace(".pre_norm_mha.1.out_proj."    ,   ".attention.output.dense."    )
						if ".pre_norm_ffn.0." in name:
												UpperCAmelCase         =     name.replace(".pre_norm_ffn.0."    ,   ".layernorm_after."    )
						if ".pre_norm_ffn.1." in name:
												UpperCAmelCase         =     name.replace(".pre_norm_ffn.1."    ,   ".intermediate.dense."    )
						if ".pre_norm_ffn.4." in name:
												UpperCAmelCase         =     name.replace(".pre_norm_ffn.4."    ,   ".output.dense."    )
						if ".transformer." in name:
												UpperCAmelCase         =     name.replace(".transformer."    ,   ".transformer.layer."    )

						if ".aspp_layer." in name:
												UpperCAmelCase         =     name.replace(".aspp_layer."    ,   "."    )
						if ".aspp_pool." in name:
												UpperCAmelCase         =     name.replace(".aspp_pool."    ,   "."    )
						if "seg_head." in name:
												UpperCAmelCase         =     name.replace("seg_head."    ,   "segmentation_head."    )
						if "segmentation_head.classifier.classifier." in name:
												UpperCAmelCase         =     name.replace("segmentation_head.classifier.classifier."    ,   "segmentation_head.classifier."    )

						if "classifier.fc." in name:
												UpperCAmelCase         =     name.replace("classifier.fc."    ,   "classifier."    )
						elif (not base_model) and ("segmentation_head." not in name):
												UpperCAmelCase         =     "mobilevit." + name

						return name

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=False    ) ->       Any:
						if base_model:
												UpperCAmelCase         =     ""
						else:
												UpperCAmelCase         =     "mobilevit."

						for key in orig_state_dict.copy().keys():
												UpperCAmelCase         =     orig_state_dict.pop(lowerCamelCase_    )

												if key[:8] == "encoder.":
																		UpperCAmelCase         =     key[8:]

												if "qkv" in key:
																		UpperCAmelCase         =     key.split("."    )
																		UpperCAmelCase         =     int(key_split[0][6:]    ) - 1
																		UpperCAmelCase         =     int(key_split[3]    )
																		UpperCAmelCase         =     model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}'    )
																		UpperCAmelCase         =     layer.transformer.layer[transformer_num].attention.attention.all_head_size
																		UpperCAmelCase         =     (
																		    F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
																		)
																		if "weight" in key:
																								UpperCAmelCase         =     val[:dim, :]
																								UpperCAmelCase         =     val[dim : dim * 2, :]
																								UpperCAmelCase         =     val[-dim:, :]
																		else:
																								UpperCAmelCase         =     val[:dim]
																								UpperCAmelCase         =     val[dim : dim * 2]
																								UpperCAmelCase         =     val[-dim:]
												else:
																		UpperCAmelCase         =     val

						return orig_state_dict

def    lowerCamelCase_() ->       Optional[Any]:
						UpperCAmelCase         =     "http://images.cocodataset.org/val2017/000000039769.jpg"
						UpperCAmelCase         =     Image.open(requests.get(lowerCamelCase_    ,   stream=lowerCamelCase_    ).raw    )
						return im

@torch.no_grad()
def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=False    ) ->       List[Any]:
						UpperCAmelCase         =     get_mobilevit_config(lowerCamelCase_    )

						# load original state_dict
						UpperCAmelCase         =     torch.load(lowerCamelCase_    ,   map_location="cpu"    )

						# load 🤗 model
						if mobilevit_name.startswith("deeplabv3_"    ):
												UpperCAmelCase         =     MobileViTForSemanticSegmentation(lowerCamelCase_    ).eval()
						else:
												UpperCAmelCase         =     MobileViTForImageClassification(lowerCamelCase_    ).eval()

						UpperCAmelCase         =     convert_state_dict(lowerCamelCase_    ,   lowerCamelCase_    )
						model.load_state_dict(lowerCamelCase_    )

						# Check outputs on an image, prepared by MobileViTImageProcessor
						UpperCAmelCase         =     MobileViTImageProcessor(crop_size=config.image_size    ,   size=config.image_size + 32    )
						UpperCAmelCase         =     image_processor(images=prepare_img()    ,   return_tensors="pt"    )
						UpperCAmelCase         =     model(**lowerCamelCase_    )
						UpperCAmelCase         =     outputs.logits

						if mobilevit_name.startswith("deeplabv3_"    ):
												assert logits.shape == (1, 21, 32, 32)

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		UpperCAmelCase         =     torch.tensor(
																		    [
																		        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
																		        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
																		        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
																		    ]    )
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		UpperCAmelCase         =     torch.tensor(
																		    [
																		        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
																		        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
																		        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
																		    ]    )
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		UpperCAmelCase         =     torch.tensor(
																		    [
																		        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
																		        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
																		        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
																		    ]    )
												else:
																		raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'    )

												assert torch.allclose(logits[0, :3, :3, :3]    ,   lowerCamelCase_    ,   atol=1e-4    )
						else:
												assert logits.shape == (1, 1_000)

												if mobilevit_name == "mobilevit_s":
																		UpperCAmelCase         =     torch.tensor([-0.9866, 0.2392, -1.1241]    )
												elif mobilevit_name == "mobilevit_xs":
																		UpperCAmelCase         =     torch.tensor([-2.4761, -0.9399, -1.9587]    )
												elif mobilevit_name == "mobilevit_xxs":
																		UpperCAmelCase         =     torch.tensor([-1.9364, -1.2327, -0.4653]    )
												else:
																		raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'    )

												assert torch.allclose(logits[0, :3]    ,   lowerCamelCase_    ,   atol=1e-4    )

						Path(lowerCamelCase_    ).mkdir(exist_ok=lowerCamelCase_    )
						print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}'    )
						model.save_pretrained(lowerCamelCase_    )
						print(F'Saving image processor to {pytorch_dump_folder_path}'    )
						image_processor.save_pretrained(lowerCamelCase_    )

						if push_to_hub:
												UpperCAmelCase         =     {
												    "mobilevit_s": "mobilevit-small",
												    "mobilevit_xs": "mobilevit-x-small",
												    "mobilevit_xxs": "mobilevit-xx-small",
												    "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
												    "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
												    "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
												}

												print("Pushing to the hub..."    )
												UpperCAmelCase         =     model_mapping[mobilevit_name]
												image_processor.push_to_hub(lowerCamelCase_    ,   organization="apple"    )
												model.push_to_hub(lowerCamelCase_    ,   organization="apple"    )


if __name__ == "__main__":
		__lowerCamelCase     :							Tuple  							=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--mobilevit_name",
		    default="mobilevit_s",
		    type=str,
		    help=(
		        "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
		        " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
		    ),
		)
		parser.add_argument(
		    "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", required=True, 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."
		)

		__lowerCamelCase     :							Optional[Any]  							=				parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)








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  __magic_name__   (							A__						):
		lowercase      :       "DiagonalGaussianDistribution"





class  __magic_name__   (							A__,  A__						):
		lowercase      :       Union[str, Any]          =True






		@register_to_config
		def __init__(						self      :     List[str]				,		UpperCamelCase__      :     int = 3				,		UpperCamelCase__      :     int = 3				,		UpperCamelCase__      :     Tuple[str] = ("DownEncoderBlock2D",)				,		UpperCamelCase__      :     Tuple[str] = ("UpDecoderBlock2D",)				,		UpperCamelCase__      :     Tuple[int] = (64,)				,		UpperCamelCase__      :     int = 1				,		UpperCamelCase__      :     str = "silu"				,		UpperCamelCase__      :     int = 4				,		UpperCamelCase__      :     int = 32				,		UpperCamelCase__      :     int = 32				,		UpperCamelCase__      :     float = 0.1_82_15				,		)				->					List[str]:
								'''simple docstring'''







								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase         =     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         =     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         =     nn.Convad(2 * latent_channels				,		2 * latent_channels				,		1      )
								UpperCAmelCase         =     nn.Convad(UpperCamelCase__				,		UpperCamelCase__				,		1      )

								UpperCAmelCase         =     False
								UpperCAmelCase         =     False

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     int=False      )				->					Optional[int]:
								'''simple docstring'''







								if isinstance(UpperCamelCase__				,		(Encoder, Decoder)      ):
														UpperCAmelCase         =     value






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     bool = True      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     use_tiling






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Tuple:
								'''simple docstring'''







								self.enable_tiling(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     True






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     False






		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict[str, AttentionProcessor]:
								'''simple docstring'''







								UpperCAmelCase         =     {}

								def fn_recursive_add_processors(UpperCamelCase__      :     str				,		UpperCamelCase__      :     torch.nn.Module				,		UpperCamelCase__      :     Dict[str, AttentionProcessor]      ):
														if hasattr(UpperCamelCase__				,		"set_processor"      ):
																				UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Union[AttentionProcessor, Dict[str, AttentionProcessor]]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     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__      :     str				,		UpperCamelCase__      :     torch.nn.Module				,		UpperCamelCase__      :     List[Any]      ):
														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 SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Dict:
								'''simple docstring'''







								self.set_attn_processor(AttnProcessor()      )






		@apply_forward_hook
		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					AutoencoderKLOutput:
								'''simple docstring'''







								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         =     [self.encoder(UpperCamelCase__      ) for x_slice in x.split(1      )]
														UpperCAmelCase         =     torch.cat(UpperCamelCase__      )
								else:
														UpperCAmelCase         =     self.encoder(UpperCamelCase__      )

								UpperCAmelCase         =     self.quant_conv(UpperCamelCase__      )
								UpperCAmelCase         =     DiagonalGaussianDistribution(UpperCamelCase__      )

								if not return_dict:
														return (posterior,)

								return AutoencoderKLOutput(latent_dist=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					Union[DecoderOutput, torch.FloatTensor]:
								'''simple docstring'''







								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         =     self.post_quant_conv(UpperCamelCase__      )
								UpperCAmelCase         =     self.decoder(UpperCamelCase__      )

								if not return_dict:
														return (dec,)

								return DecoderOutput(sample=UpperCamelCase__      )






		@apply_forward_hook
		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					Union[DecoderOutput, torch.FloatTensor]:
								'''simple docstring'''







								if self.use_slicing and z.shape[0] > 1:
														UpperCAmelCase         =     [self._decode(UpperCamelCase__      ).sample for z_slice in z.split(1      )]
														UpperCAmelCase         =     torch.cat(UpperCamelCase__      )
								else:
														UpperCAmelCase         =     self._decode(UpperCamelCase__      ).sample

								if not return_dict:
														return (decoded,)

								return DecoderOutput(sample=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     min(a.shape[2]				,		b.shape[2]				,		UpperCamelCase__      )
								for y in range(UpperCamelCase__      ):
														UpperCAmelCase         =     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
								return b






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     int      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     min(a.shape[3]				,		b.shape[3]				,		UpperCamelCase__      )
								for x in range(UpperCamelCase__      ):
														UpperCAmelCase         =     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
								return b






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					AutoencoderKLOutput:
								'''simple docstring'''







								UpperCAmelCase         =     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)      )
								UpperCAmelCase         =     int(self.tile_latent_min_size * self.tile_overlap_factor      )
								UpperCAmelCase         =     self.tile_latent_min_size - blend_extent

								# Split the image into 512x512 tiles and encode them separately.
								UpperCAmelCase         =     []
								for i in range(0				,		x.shape[2]				,		UpperCamelCase__      ):
														UpperCAmelCase         =     []
														for j in range(0				,		x.shape[3]				,		UpperCamelCase__      ):
																				UpperCAmelCase         =     x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																				UpperCAmelCase         =     self.encoder(UpperCamelCase__      )
																				UpperCAmelCase         =     self.quant_conv(UpperCamelCase__      )
																				row.append(UpperCamelCase__      )
														rows.append(UpperCamelCase__      )
								UpperCAmelCase         =     []
								for i, row in enumerate(UpperCamelCase__      ):
														UpperCAmelCase         =     []
														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         =     self.blend_v(rows[i - 1][j]				,		UpperCamelCase__				,		UpperCamelCase__      )
																				if j > 0:
																										UpperCAmelCase         =     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         =     torch.cat(UpperCamelCase__				,		dim=2      )
								UpperCAmelCase         =     DiagonalGaussianDistribution(UpperCamelCase__      )

								if not return_dict:
														return (posterior,)

								return AutoencoderKLOutput(latent_dist=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					Union[DecoderOutput, torch.FloatTensor]:
								'''simple docstring'''







								UpperCAmelCase         =     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)      )
								UpperCAmelCase         =     int(self.tile_sample_min_size * self.tile_overlap_factor      )
								UpperCAmelCase         =     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         =     []
								for i in range(0				,		z.shape[2]				,		UpperCamelCase__      ):
														UpperCAmelCase         =     []
														for j in range(0				,		z.shape[3]				,		UpperCamelCase__      ):
																				UpperCAmelCase         =     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																				UpperCAmelCase         =     self.post_quant_conv(UpperCamelCase__      )
																				UpperCAmelCase         =     self.decoder(UpperCamelCase__      )
																				row.append(UpperCamelCase__      )
														rows.append(UpperCamelCase__      )
								UpperCAmelCase         =     []
								for i, row in enumerate(UpperCamelCase__      ):
														UpperCAmelCase         =     []
														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         =     self.blend_v(rows[i - 1][j]				,		UpperCamelCase__				,		UpperCamelCase__      )
																				if j > 0:
																										UpperCAmelCase         =     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         =     torch.cat(UpperCamelCase__				,		dim=2      )
								if not return_dict:
														return (dec,)

								return DecoderOutput(sample=UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = False				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Optional[torch.Generator] = None				,		)				->					Union[DecoderOutput, torch.FloatTensor]:
								'''simple docstring'''







								UpperCAmelCase         =     sample
								UpperCAmelCase         =     self.encode(UpperCamelCase__      ).latent_dist
								if sample_posterior:
														UpperCAmelCase         =     posterior.sample(generator=UpperCamelCase__      )
								else:
														UpperCAmelCase         =     posterior.mode()
								UpperCAmelCase         =     self.decode(UpperCamelCase__      ).sample

								if not return_dict:
														return (dec,)

								return DecoderOutput(sample=UpperCamelCase__      )



