




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()
snake_case__			 =						logging.get_logger(__name__)


def 				lowerCamelCase__	(      a	:   str				)    ->	Optional[Any]:





			"""simple docstring"""
			a__ :List[str]						  =    SwinConfig.from_pretrained(
			    "microsoft/swin-tiny-patch4-window7-224"	,  out_features=["stage1", "stage2", "stage3", "stage4"]				)
			a__ :Optional[Any]						  =    MaskFormerConfig(backbone_config=_A				)

			a__ :List[Any]						  =    "huggingface/label-files"
			if "ade20k-full" in model_name:
						# this should be ok
						a__ :str						  =    847
						a__ :Tuple						  =    "maskformer-ade20k-full-id2label.json"
			elif "ade" in model_name:
						# this should be ok
						a__ :Optional[Any]						  =    150
						a__ :Tuple						  =    "ade20k-id2label.json"
			elif "coco-stuff" in model_name:
						# this should be ok
						a__ :Optional[int]						  =    171
						a__ :Union[str, Any]						  =    "maskformer-coco-stuff-id2label.json"
			elif "coco" in model_name:
						# TODO
						a__ :int						  =    133
						a__ :Dict						  =    "coco-panoptic-id2label.json"
			elif "cityscapes" in model_name:
						# this should be ok
						a__ :List[str]						  =    19
						a__ :Union[str, Any]						  =    "cityscapes-id2label.json"
			elif "vistas" in model_name:
						# this should be ok
						a__ :str						  =    65
						a__ :Optional[Any]						  =    "mapillary-vistas-id2label.json"

			a__ :List[str]						  =    json.load(open(hf_hub_download(_A	,  _A	,  repo_type="dataset"				)	,  "r"				)				)
			a__ :Optional[int]						  =    {int(_A				): v for k, v in idalabel.items()}

			return config


def 				lowerCamelCase__	(      a	:   int				)    ->	str:





			"""simple docstring"""
			a__ :List[str]						  =    []
			# 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__	(      a	:   List[Any]	,  a	:   Dict	,  a	:   Tuple				)    ->	Any:





			"""simple docstring"""
			a__ :Tuple						  =    dct.pop(_A				)
			a__ :Any						  =    val


def 				lowerCamelCase__	(      a	:   Dict	,  a	:   Any				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Dict						  =    [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
			for i in range(len(backbone_config.depths				)				):
						a__ :Tuple						  =    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)
									a__ :Optional[int]						  =    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''				)
									a__ :Optional[Any]						  =    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
									a__ :List[Any]						  =    in_proj_weight[:dim, :]
									a__ :str						  =    in_proj_bias[: dim]
									a__ :Optional[Any]						  =    in_proj_weight[
									    dim : dim * 2, :
									]
									a__ :str						  =    in_proj_bias[
									    dim : dim * 2
									]
									a__ :Optional[int]						  =    in_proj_weight[
									    -dim :, :
									]
									a__ :Union[str, Any]						  =    in_proj_bias[-dim :]
									# fmt: on


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Any				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :int						  =    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)
						a__ :Optional[int]						  =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''				)
						a__ :List[str]						  =    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
						a__ :Dict						  =    in_proj_weight[: hidden_size, :]
						a__ :Union[str, Any]						  =    in_proj_bias[:config.hidden_size]
						a__ :List[str]						  =    in_proj_weight[hidden_size : hidden_size * 2, :]
						a__ :List[Any]						  =    in_proj_bias[hidden_size : hidden_size * 2]
						a__ :Union[str, Any]						  =    in_proj_weight[-hidden_size :, :]
						a__ :Dict						  =    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)
						a__ :List[str]						  =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''				)
						a__ :List[Any]						  =    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
						a__ :Any						  =    in_proj_weight[: hidden_size, :]
						a__ :Optional[int]						  =    in_proj_bias[:config.hidden_size]
						a__ :Any						  =    in_proj_weight[hidden_size : hidden_size * 2, :]
						a__ :Tuple						  =    in_proj_bias[hidden_size : hidden_size * 2]
						a__ :List[str]						  =    in_proj_weight[-hidden_size :, :]
						a__ :Union[str, Any]						  =    in_proj_bias[-hidden_size :]
			# fmt: on


def 				lowerCamelCase__	(      )    ->	torch.Tensor:





			"""simple docstring"""
			a__ :str						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__ :int						  =    Image.open(requests.get(_A	,  stream=_A				).raw				)
			return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   str	,  a	:   str	,  a	:   str	,  a	:   bool = False				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Any						  =    get_maskformer_config(_A				)

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

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

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

			# update to torch tensors
			for key, value in state_dict.items():
						a__ :Union[str, Any]						  =    torch.from_numpy(_A				)

			# load 🤗 model
			a__ :List[str]						  =    MaskFormerForInstanceSegmentation(_A				)
			model.eval()

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

			a__  ,  a__ :List[Any]						  =    model.load_state_dict(_A	,  strict=_A				)
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(_A				) == 0, F'''Unexpected keys: {unexpected_keys}'''

			# verify results
			a__ :Dict						  =    prepare_img()
			if "vistas" in model_name:
						a__ :Tuple						  =    65
			elif "cityscapes" in model_name:
						a__ :Optional[Any]						  =    65_535
			else:
						a__ :str						  =    255
			a__ :Any						  =    True if "ade" in model_name else False
			a__ :Optional[int]						  =    MaskFormerImageProcessor(ignore_index=_A	,  reduce_labels=_A				)

			a__ :int						  =    image_processor(_A	,  return_tensors="pt"				)

			a__ :Optional[int]						  =    model(**_A				)

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

			if model_name == "maskformer-swin-tiny-ade":
						a__ :List[str]						  =    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]	,  _A	,  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(_A				).mkdir(exist_ok=_A				)
						model.save_pretrained(_A				)
						image_processor.save_pretrained(_A				)

			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__":
		snake_case__			 =						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.'''
		)

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






import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class 					lowerCAmelCase_ :




						def __init__(	self						:   str   ,     __A						:   str = "cpu"   ,     __A						:   str = "openai/clip-vit-large-patch14"						)      ->None:






									"""simple docstring"""






									a__ :List[str]						  =    device
									a__ :Optional[Any]						  =    CLIPTokenizerFast.from_pretrained(__A						)
									a__ :str						  =    [0.48_145_466, 0.4_578_275, 0.40_821_073]
									a__ :Any						  =    [0.26_862_954, 0.26_130_258, 0.27_577_711]
									a__ :Union[str, Any]						  =    torchvision.transforms.Normalize(self.image_mean   ,     self.image_std						)
									a__ :List[Any]						  =    torchvision.transforms.Resize(224						)
									a__ :Optional[Any]						  =    torchvision.transforms.CenterCrop(224						)




						def  _snake_case					(	self						:   int   ,     __A						:   int						)      ->List[Any]:






									"""simple docstring"""






									a__ :List[str]						  =    self.resize(__A						)
									a__ :Union[str, Any]						  =    self.center_crop(__A						)
									a__ :List[str]						  =    self.normalize(__A						)
									return images






						def __call__(	self						:   Union[str, Any]   ,     __A						:   Dict=None   ,     __A						:   List[Any]=None   ,     **__A						:   Union[str, Any]						)      ->Tuple:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.tokenizer(text=__A   ,     **__A						)
									a__ :str						  =    self.preprocess_img(__A						)
									a__ :Any						  =    {key: value.to(self.device						) for (key, value) in encoding.items()}
									return encoding






class 					lowerCAmelCase_ (       nn.Module):




						def __init__(	self						:   List[str]   ,     __A						:   List[str]=10   ,     __A						:   List[Any]=0.01   ,     __A						:   Optional[int]=None   ,     __A						:   List[str]=None   ,     __A						:   Dict=None   ,     __A						:   List[str]=None   ,     __A						:   Tuple=None   ,     __A						:   str=None   ,     __A						:   Any=False   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple="image"   ,     __A						:   Tuple=True   ,     __A						:   int=False   ,     __A						:   Tuple=False   ,     __A						:   Union[str, Any]=False   ,     )      ->None:






									"""simple docstring"""






									super().__init__()
									a__ :List[str]						  =    None
									a__ :int						  =    device if device else get_device()
									if vqgan:
												a__ :Any						  =    vqgan
									else:
												a__ :Optional[int]						  =    load_vqgan(self.device   ,     conf_path=__A   ,     ckpt_path=__A						)
									self.vqgan.eval()
									if clip:
												a__ :Dict						  =    clip
									else:
												a__ :Any						  =    CLIPModel.from_pretrained("openai/clip-vit-base-patch32"						)
									self.clip.to(self.device						)
									a__ :Optional[int]						  =    ProcessorGradientFlow(device=self.device						)

									a__ :Any						  =    iterations
									a__ :Any						  =    lr
									a__ :List[str]						  =    log
									a__ :List[Any]						  =    make_grid
									a__ :Optional[int]						  =    return_val
									a__ :Optional[Any]						  =    quantize
									a__ :Any						  =    self.vqgan.decoder.z_shape




						def  _snake_case					(	self						:   str   ,     __A						:   str=None   ,     __A						:   Optional[int]=None   ,     __A						:   int=5   ,     __A						:   Any=True						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Optional[int]						  =    []
									if output_path is None:
												a__ :Any						  =    "./animation.gif"
									if input_path is None:
												a__ :Dict						  =    self.save_path
									a__ :Dict						  =    sorted(glob(input_path + "/*"						)						)
									if not len(__A						):
												raise ValueError(
												    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
												    " function?)"						)
									if len(__A						) == 1:
												print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"						)
									a__ :str						  =    total_duration / len(__A						)
									a__ :str						  =    [frame_duration] * len(__A						)
									if extend_frames:
												a__ :Optional[Any]						  =    1.5
												a__ :Optional[Any]						  =    3
									for file_name in paths:
												if file_name.endswith(".png"						):
															images.append(imageio.imread(__A						)						)
									imageio.mimsave(__A   ,     __A   ,     duration=__A						)
									print(F'''gif saved to {output_path}'''						)




						def  _snake_case					(	self						:   str   ,     __A						:   Tuple=None   ,     __A						:   Optional[int]=None						)      ->Optional[int]:






									"""simple docstring"""






									if not (path or img):
												raise ValueError("Input either path or tensor"						)
									if img is not None:
												raise NotImplementedError
									a__ :List[Any]						  =    preprocess(Image.open(__A						)   ,     target_image_size=256						).to(self.device						)
									a__ :List[str]						  =    preprocess_vqgan(__A						)
									a__  ,  *a__ :Union[str, Any]						  =    self.vqgan.encode(__A						)
									return z




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Any						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Dict						  =    self.latent.detach().requires_grad_()
									a__ :int						  =    base_latent + transform_vector
									if self.quantize:
												a__  ,  *a__ :str						  =    self.vqgan.quantize(__A						)
									else:
												a__ :List[str]						  =    trans_latent
									return self.vqgan.decode(__A						)




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[Any]   ,     __A						:   List[Any]=None						)      ->str:






									"""simple docstring"""






									a__ :Dict						  =    self.clip_preprocessor(text=__A   ,     images=__A   ,     return_tensors="pt"   ,     padding=__A						)
									a__ :List[str]						  =    self.clip(**__A						)
									a__ :Optional[int]						  =    clip_outputs.logits_per_image
									if weights is not None:
												a__ :Tuple						  =    similarity_logits * weights
									return similarity_logits.sum()




						def  _snake_case					(	self						:   List[str]   ,     __A						:   List[Any]   ,     __A						:   Optional[int]   ,     __A						:   Optional[int]						)      ->int:






									"""simple docstring"""






									a__ :str						  =    self._get_clip_similarity(pos_prompts["prompts"]   ,     __A   ,     weights=(1 / pos_prompts["weights"])						)
									if neg_prompts:
												a__ :Optional[int]						  =    self._get_clip_similarity(neg_prompts["prompts"]   ,     __A   ,     weights=neg_prompts["weights"]						)
									else:
												a__ :Tuple						  =    torch.tensor([1]   ,     device=self.device						)
									a__ :Dict						  =    -torch.log(__A						) + torch.log(__A						)
									return loss




						def  _snake_case					(	self						:   int   ,     __A						:   Dict   ,     __A						:   Union[str, Any]   ,     __A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									a__ :List[Any]						  =    torch.randn_like(self.latent   ,     requires_grad=__A   ,     device=self.device						)
									a__ :Optional[Any]						  =    torch.optim.Adam([vector]   ,     lr=self.lr						)

									for i in range(self.iterations						):
												optim.zero_grad()
												a__ :Tuple						  =    self._add_vector(__A						)
												a__ :Dict						  =    loop_post_process(__A						)
												a__ :str						  =    self._get_CLIP_loss(__A   ,     __A   ,     __A						)
												print("CLIP loss"   ,     __A						)
												if self.log:
															wandb.log({"CLIP Loss": clip_loss}						)
												clip_loss.backward(retain_graph=__A						)
												optim.step()
												if self.return_val == "image":
															yield custom_to_pil(transformed_img[0]						)
												else:
															yield vector




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[Any]						)      ->Optional[int]:






									"""simple docstring"""






									wandb.init(reinit=__A   ,     project="face-editor"						)
									wandb.config.update({"Positive Prompts": positive_prompts}						)
									wandb.config.update({"Negative Prompts": negative_prompts}						)
									wandb.config.update({"lr": self.lr, "iterations": self.iterations}						)
									if image_path:
												a__ :List[str]						  =    Image.open(__A						)
												a__ :Any						  =    image.resize((256, 256)						)
												wandb.log("Original Image"   ,     wandb.Image(__A						)						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									if not prompts:
												return []
									a__ :List[str]						  =    []
									a__ :Tuple						  =    []
									if isinstance(__A   ,     __A						):
												a__ :Tuple						  =    [prompt.strip() for prompt in prompts.split("|"						)]
									for prompt in prompts:
												if isinstance(__A   ,     (tuple, list)						):
															a__ :str						  =    prompt[0]
															a__ :List[Any]						  =    float(prompt[1]						)
												elif ":" in prompt:
															a__  ,  a__ :Tuple						  =    prompt.split(":"						)
															a__ :Dict						  =    float(__A						)
												else:
															a__ :int						  =    prompt
															a__ :Any						  =    1.0
												processed_prompts.append(__A						)
												weights.append(__A						)
									return {
									    "prompts": processed_prompts,
									    "weights": torch.tensor(__A   ,     device=self.device						),
									}






						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Any   ,     __A						:   int=None   ,     __A						:   int=None   ,     __A						:   str=True   ,     __A						:   List[Any]=False   ,     __A						:   Optional[int]=True   ,     __A						:   Any=True   ,     __A						:   Optional[Any]=None   ,     )      ->Optional[Any]:






									"""simple docstring"""






									if image_path:
												a__ :Optional[int]						  =    self._get_latent(__A						)
									else:
												a__ :Dict						  =    torch.randn(self.latent_dim   ,     device=self.device						)
									if self.log:
												self._init_logging(__A   ,     __A   ,     __A						)

									assert pos_prompts, "You must provide at least one positive prompt."
									a__ :int						  =    self.process_prompts(__A						)
									a__ :Any						  =    self.process_prompts(__A						)
									if save_final and save_path is None:
												a__ :Dict						  =    os.path.join("./outputs/"   ,     "_".join(pos_prompts["prompts"]						)						)
									if not os.path.exists(__A						):
												os.makedirs(__A						)
									else:
												a__ :str						  =    save_path + "_" + get_timestamp()
												os.makedirs(__A						)
									a__ :Any						  =    save_path

									a__ :Optional[int]						  =    self.vqgan.decode(self.latent						)[0]
									if show_intermediate:
												print("Original Image"						)
												show_pil(custom_to_pil(__A						)						)

									a__ :List[str]						  =    loop_post_process(__A						)
									for iter, transformed_img in enumerate(self._optimize_CLIP(__A   ,     __A   ,     __A						)						):
												if show_intermediate:
															show_pil(__A						)
												if save_intermediate:
															transformed_img.save(os.path.join(self.save_path   ,     F'''iter_{iter:03d}.png'''						)						)
												if self.log:
															wandb.log({"Image": wandb.Image(__A						)}						)
									if show_final:
												show_pil(__A						)
									if save_final:
												transformed_img.save(os.path.join(self.save_path   ,     F'''iter_{iter:03d}_final.png'''						)						)


