'''simple docstring'''


import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__					:      Optional[int]				      =  logging.get_logger(__name__)


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							List[str]=False       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				OrderedDict()
     for key, value in state_dict.items():
          if encoder_only and not key.startswith('head'       ):
               UpperCAmelCase_							:       Any       =				'segformer.encoder.' + key
          if key.startswith('backbone'       ):
               UpperCAmelCase_							:       List[Any]       =				key.replace('backbone'     ,					'segformer.encoder'       )
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               UpperCAmelCase_							:       Union[str, Any]       =				key[key.find('patch_embed'       ) + len('patch_embed'       )]
               UpperCAmelCase_							:       Optional[int]       =				key.replace(F'''patch_embed{idx}'''     ,					F'''patch_embeddings.{int(_UpperCAmelCase       )-1}'''       )
          if "norm" in key:
               UpperCAmelCase_							:       List[str]       =				key.replace('norm'     ,					'layer_norm'       )
          if "segformer.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               UpperCAmelCase_							:       Tuple       =				key[key.find('segformer.encoder.layer_norm'       ) + len('segformer.encoder.layer_norm'       )]
               UpperCAmelCase_							:       Union[str, Any]       =				key.replace(F'''layer_norm{idx}'''     ,					F'''layer_norm.{int(_UpperCAmelCase       )-1}'''       )
          if "layer_norm1" in key:
               UpperCAmelCase_							:       str       =				key.replace('layer_norm1'     ,					'layer_norm_1'       )
          if "layer_norm2" in key:
               UpperCAmelCase_							:       int       =				key.replace('layer_norm2'     ,					'layer_norm_2'       )
          if "block" in key:
               # replace for example block1 by block.0
               UpperCAmelCase_							:       Dict       =				key[key.find('block'       ) + len('block'       )]
               UpperCAmelCase_							:       List[str]       =				key.replace(F'''block{idx}'''     ,					F'''block.{int(_UpperCAmelCase       )-1}'''       )
          if "attn.q" in key:
               UpperCAmelCase_							:       Dict       =				key.replace('attn.q'     ,					'attention.self.query'       )
          if "attn.proj" in key:
               UpperCAmelCase_							:       List[str]       =				key.replace('attn.proj'     ,					'attention.output.dense'       )
          if "attn" in key:
               UpperCAmelCase_							:       int       =				key.replace('attn'     ,					'attention.self'       )
          if "fc1" in key:
               UpperCAmelCase_							:       Optional[Any]       =				key.replace('fc1'     ,					'dense1'       )
          if "fc2" in key:
               UpperCAmelCase_							:       int       =				key.replace('fc2'     ,					'dense2'       )
          if "linear_pred" in key:
               UpperCAmelCase_							:       Optional[Any]       =				key.replace('linear_pred'     ,					'classifier'       )
          if "linear_fuse" in key:
               UpperCAmelCase_							:       Any       =				key.replace('linear_fuse.conv'     ,					'linear_fuse'       )
               UpperCAmelCase_							:       List[str]       =				key.replace('linear_fuse.bn'     ,					'batch_norm'       )
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               UpperCAmelCase_							:       int       =				key[key.find('linear_c'       ) + len('linear_c'       )]
               UpperCAmelCase_							:       List[str]       =				key.replace(F'''linear_c{idx}'''     ,					F'''linear_c.{int(_UpperCAmelCase       )-1}'''       )
          if key.startswith('head'       ):
               UpperCAmelCase_							:       Any       =				key.replace('head'     ,					'classifier'       )
          UpperCAmelCase_							:       List[str]       =				value

     return new_state_dict


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							List[str]       ):



     """simple docstring"""





     for i in range(config.num_encoder_blocks       ):
          for j in range(config.depths[i]       ):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               UpperCAmelCase_							:       List[Any]       =				state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''       )
               UpperCAmelCase_							:       int       =				state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias'''       )
               # next, add keys and values (in that order) to the state dict
               UpperCAmelCase_							:       int       =				kv_weight[
                   : config.hidden_sizes[i], :
               ]
               UpperCAmelCase_							:       List[str]       =				kv_bias[: config.hidden_sizes[i]]
               UpperCAmelCase_							:       Union[str, Any]       =				kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               UpperCAmelCase_							:       Optional[Any]       =				kv_bias[
                   config.hidden_sizes[i] :
               ]


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       int       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_							:       Optional[int]       =				Image.open(requests.get(_UpperCAmelCase     ,					stream=_UpperCAmelCase       ).raw       )

     return image


@torch.no_grad()
def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[Any]       =				SegformerConfig()
     UpperCAmelCase_							:       Optional[Any]       =				False

     # set attributes based on model_name
     UpperCAmelCase_							:       int       =				'huggingface/label-files'
     if "segformer" in model_name:
          UpperCAmelCase_							:       Tuple       =				model_name[len('segformer.'       ) : len('segformer.'       ) + 2]
          if "ade" in model_name:
               UpperCAmelCase_							:       Optional[Any]       =				150
               UpperCAmelCase_							:       Optional[int]       =				'ade20k-id2label.json'
               UpperCAmelCase_							:       Optional[Any]       =				(1, 150, 128, 128)
          elif "city" in model_name:
               UpperCAmelCase_							:       Any       =				19
               UpperCAmelCase_							:       List[Any]       =				'cityscapes-id2label.json'
               UpperCAmelCase_							:       Tuple       =				(1, 19, 128, 128)
          else:
               raise ValueError(F'''Model {model_name} not supported'''       )
     elif "mit" in model_name:
          UpperCAmelCase_							:       Union[str, Any]       =				True
          UpperCAmelCase_							:       int       =				model_name[4:6]
          UpperCAmelCase_							:       Union[str, Any]       =				1000
          UpperCAmelCase_							:       str       =				'imagenet-1k-id2label.json'
          UpperCAmelCase_							:       Any       =				(1, 1000)
     else:
          raise ValueError(F'''Model {model_name} not supported'''       )

     # set config attributes
     UpperCAmelCase_							:       List[Any]       =				json.load(open(hf_hub_download(_UpperCAmelCase     ,					_UpperCAmelCase     ,					repo_type='dataset'       )     ,					'r'       )       )
     UpperCAmelCase_							:       Optional[int]       =				{int(_UpperCAmelCase       ): v for k, v in idalabel.items()}
     UpperCAmelCase_							:       Dict       =				idalabel
     UpperCAmelCase_							:       List[Any]       =				{v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          UpperCAmelCase_							:       List[Any]       =				[64, 128, 320, 512]
          UpperCAmelCase_							:       str       =				256
     elif size == "b2":
          UpperCAmelCase_							:       int       =				[64, 128, 320, 512]
          UpperCAmelCase_							:       str       =				768
          UpperCAmelCase_							:       Optional[int]       =				[3, 4, 6, 3]
     elif size == "b3":
          UpperCAmelCase_							:       Any       =				[64, 128, 320, 512]
          UpperCAmelCase_							:       int       =				768
          UpperCAmelCase_							:       Tuple       =				[3, 4, 18, 3]
     elif size == "b4":
          UpperCAmelCase_							:       Any       =				[64, 128, 320, 512]
          UpperCAmelCase_							:       Union[str, Any]       =				768
          UpperCAmelCase_							:       Union[str, Any]       =				[3, 8, 27, 3]
     elif size == "b5":
          UpperCAmelCase_							:       Any       =				[64, 128, 320, 512]
          UpperCAmelCase_							:       int       =				768
          UpperCAmelCase_							:       List[str]       =				[3, 6, 40, 3]
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # load image processor (only resize + normalize)
     UpperCAmelCase_							:       Optional[Any]       =				SegformerImageProcessor(
         image_scale=(512, 512)     ,					keep_ratio=_UpperCAmelCase     ,					align=_UpperCAmelCase     ,					do_random_crop=_UpperCAmelCase       )

     # prepare image
     UpperCAmelCase_							:       Dict       =				prepare_img()
     UpperCAmelCase_							:       List[str]       =				image_processor(images=_UpperCAmelCase     ,					return_tensors='pt'       ).pixel_values

     logger.info(F'''Converting model {model_name}...'''       )

     # load original state dict
     if encoder_only:
          UpperCAmelCase_							:       List[Any]       =				torch.load(_UpperCAmelCase     ,					map_location=torch.device('cpu'       )       )
     else:
          UpperCAmelCase_							:       Optional[Any]       =				torch.load(_UpperCAmelCase     ,					map_location=torch.device('cpu'       )       )['state_dict']

     # rename keys
     UpperCAmelCase_							:       Optional[int]       =				rename_keys(_UpperCAmelCase     ,					encoder_only=_UpperCAmelCase       )
     if not encoder_only:
          del state_dict["decode_head.conv_seg.weight"]
          del state_dict["decode_head.conv_seg.bias"]

     # key and value matrices need special treatment
     read_in_k_v(_UpperCAmelCase     ,					_UpperCAmelCase       )

     # create HuggingFace model and load state dict
     if encoder_only:
          UpperCAmelCase_							:       Dict       =				False
          UpperCAmelCase_							:       Union[str, Any]       =				SegformerForImageClassification(_UpperCAmelCase       )
     else:
          UpperCAmelCase_							:       Dict       =				SegformerForSemanticSegmentation(_UpperCAmelCase       )
     model.load_state_dict(_UpperCAmelCase       )
     model.eval()

     # forward pass
     UpperCAmelCase_							:       List[str]       =				model(_UpperCAmelCase       )
     UpperCAmelCase_							:       Tuple       =				outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          UpperCAmelCase_							:       Tuple       =				torch.tensor(
              [
                  [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                  [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                  [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
              ]       )
     elif model_name == "segformer.b1.512x512.ade.160k":
          UpperCAmelCase_							:       str       =				torch.tensor(
              [
                  [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                  [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                  [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
              ]       )
     elif model_name == "segformer.b2.512x512.ade.160k":
          UpperCAmelCase_							:       Optional[Any]       =				torch.tensor(
              [
                  [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                  [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                  [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
              ]       )
     elif model_name == "segformer.b3.512x512.ade.160k":
          UpperCAmelCase_							:       Union[str, Any]       =				torch.tensor(
              [
                  [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                  [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                  [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
              ]       )
     elif model_name == "segformer.b4.512x512.ade.160k":
          UpperCAmelCase_							:       Dict       =				torch.tensor(
              [
                  [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                  [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                  [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
              ]       )
     elif model_name == "segformer.b5.640x640.ade.160k":
          UpperCAmelCase_							:       Union[str, Any]       =				torch.tensor(
              [
                  [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                  [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                  [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
              ]       )
     # Cityscapes checkpoints
     elif model_name == "segformer.b0.1024x1024.city.160k":
          UpperCAmelCase_							:       List[str]       =				torch.tensor(
              [
                  [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                  [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                  [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
              ]       )
     elif model_name == "segformer.b0.512x1024.city.160k":
          UpperCAmelCase_							:       int       =				torch.tensor(
              [
                  [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                  [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                  [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
              ]       )
     elif model_name == "segformer.b0.640x1280.city.160k":
          UpperCAmelCase_							:       int       =				torch.tensor(
              [
                  [
                      [-1.1_372e01, -1.2_787e01, -1.3_477e01],
                      [-1.2_536e01, -1.4_194e01, -1.4_409e01],
                      [-1.3_217e01, -1.4_888e01, -1.5_327e01],
                  ],
                  [
                      [-1.4_791e01, -1.7_122e01, -1.8_277e01],
                      [-1.7_163e01, -1.9_192e01, -1.9_533e01],
                      [-1.7_897e01, -1.9_991e01, -2.0_315e01],
                  ],
                  [
                      [7.6_723e-01, 4.1_921e-01, -7.7_878e-02],
                      [4.7_772e-01, 9.5_557e-03, -2.8_082e-01],
                      [3.6_032e-01, -2.4_826e-01, -5.1_168e-01],
                  ],
              ]       )
     elif model_name == "segformer.b0.768x768.city.160k":
          UpperCAmelCase_							:       Optional[int]       =				torch.tensor(
              [
                  [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                  [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                  [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
              ]       )
     elif model_name == "segformer.b1.1024x1024.city.160k":
          UpperCAmelCase_							:       Tuple       =				torch.tensor(
              [
                  [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                  [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                  [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
              ]       )
     elif model_name == "segformer.b2.1024x1024.city.160k":
          UpperCAmelCase_							:       Optional[Any]       =				torch.tensor(
              [
                  [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                  [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                  [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
              ]       )
     elif model_name == "segformer.b3.1024x1024.city.160k":
          UpperCAmelCase_							:       Union[str, Any]       =				torch.tensor(
              [
                  [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                  [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                  [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
              ]       )
     elif model_name == "segformer.b4.1024x1024.city.160k":
          UpperCAmelCase_							:       Optional[int]       =				torch.tensor(
              [
                  [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                  [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                  [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
              ]       )
     elif model_name == "segformer.b5.1024x1024.city.160k":
          UpperCAmelCase_							:       int       =				torch.tensor(
              [
                  [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                  [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                  [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
              ]       )
     else:
          UpperCAmelCase_							:       Any       =				logits.argmax(-1       ).item()
          print('Predicted class:'     ,					model.config.idalabel[predicted_class_idx]       )

     # verify logits
     if not encoder_only:
          assert logits.shape == expected_shape
          assert torch.allclose(logits[0, :3, :3, :3]     ,					_UpperCAmelCase     ,					atol=1e-2       )

     # finally, save model and image processor
     logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''       )
     Path(_UpperCAmelCase       ).mkdir(exist_ok=_UpperCAmelCase       )
     model.save_pretrained(_UpperCAmelCase       )
     image_processor.save_pretrained(_UpperCAmelCase       )


if __name__ == "__main__":
 snake_case__					:      Union[str, Any]				      =  argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''segformer.b0.512x512.ade.160k''',
     type=str,
     help='''Name of the model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 snake_case__					:      Optional[int]				      =  parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_ = "cpu"				,		snake_case_ = "openai/clip-vit-large-patch14"						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				device
       UpperCAmelCase_							:       Tuple       =				CLIPTokenizerFast.from_pretrained(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
       UpperCAmelCase_							:       Union[str, Any]       =				[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
       UpperCAmelCase_							:       Tuple       =				torchvision.transforms.Normalize(self.image_mean				,		self.image_std						)
       UpperCAmelCase_							:       Optional[Any]       =				torchvision.transforms.Resize(2_2_4						)
       UpperCAmelCase_							:       Any       =				torchvision.transforms.CenterCrop(2_2_4						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.resize(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				self.center_crop(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.normalize(snake_case_						)
       return images






  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

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







class 					__SCREAMING_SNAKE_CASE						(	nn.Module	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_=1_0				,		snake_case_=0.01				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=False				,		snake_case_=True				,		snake_case_="image"				,		snake_case_=True				,		snake_case_=False				,		snake_case_=False				,		snake_case_=False				,		):




       '''simple docstring'''

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

       UpperCAmelCase_							:       Dict       =				iterations
       UpperCAmelCase_							:       Dict       =				lr
       UpperCAmelCase_							:       str       =				log
       UpperCAmelCase_							:       Tuple       =				make_grid
       UpperCAmelCase_							:       Union[str, Any]       =				return_val
       UpperCAmelCase_							:       List[Any]       =				quantize
       UpperCAmelCase_							:       int       =				self.vqgan.decoder.z_shape


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=5				,		snake_case_=True						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None						):




       '''simple docstring'''

       if not (path or img):
            raise ValueError('Input either path or tensor'						)
       if img is not None:
            raise NotImplementedError
       UpperCAmelCase_							:       Optional[Any]       =				preprocess(Image.open(snake_case_						)				,		target_image_size=2_5_6						).to(self.device						)
       UpperCAmelCase_							:       Dict       =				preprocess_vqgan(snake_case_						)
       UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.encode(snake_case_						)
       return z


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.latent.detach().requires_grad_()
       UpperCAmelCase_							:       List[Any]       =				base_latent + transform_vector
       if self.quantize:
            UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.quantize(snake_case_						)
       else:
            UpperCAmelCase_							:       Optional[int]       =				trans_latent
       return self.vqgan.decode(snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.clip_preprocessor(text=snake_case_				,		images=snake_case_				,		return_tensors='pt'				,		padding=snake_case_						)
       UpperCAmelCase_							:       Any       =				self.clip(**snake_case_						)
       UpperCAmelCase_							:       Dict       =				clip_outputs.logits_per_image
       if weights is not None:
            UpperCAmelCase_							:       Union[str, Any]       =				similarity_logits * weights
       return similarity_logits.sum()


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self._get_clip_similarity(pos_prompts['prompts']				,		snake_case_				,		weights=(1 / pos_prompts['weights'])						)
       if neg_prompts:
            UpperCAmelCase_							:       List[Any]       =				self._get_clip_similarity(neg_prompts['prompts']				,		snake_case_				,		weights=neg_prompts['weights']						)
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				torch.tensor([1]				,		device=self.device						)
       UpperCAmelCase_							:       Dict       =				-torch.log(snake_case_						) + torch.log(snake_case_						)
       return loss


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				torch.randn_like(self.latent				,		requires_grad=snake_case_				,		device=self.device						)
       UpperCAmelCase_							:       int       =				torch.optim.Adam([vector]				,		lr=self.lr						)

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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       wandb.init(reinit=snake_case_				,		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:
            UpperCAmelCase_							:       str       =				Image.open(snake_case_						)
            UpperCAmelCase_							:       str       =				image.resize((2_5_6, 2_5_6)						)
            wandb.log('Original Image'				,		wandb.Image(snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_=None				,		snake_case_=None				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=True				,		snake_case_=None				,		):




       '''simple docstring'''

       if image_path:
            UpperCAmelCase_							:       List[Any]       =				self._get_latent(snake_case_						)
       else:
            UpperCAmelCase_							:       Any       =				torch.randn(self.latent_dim				,		device=self.device						)
       if self.log:
            self._init_logging(snake_case_				,		snake_case_				,		snake_case_						)

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

       UpperCAmelCase_							:       Dict       =				self.vqgan.decode(self.latent						)[0]
       if show_intermediate:
            print('Original Image'						)
            show_pil(custom_to_pil(snake_case_						)						)

       UpperCAmelCase_							:       Optional[int]       =				loop_post_process(snake_case_						)
       for iter, transformed_img in enumerate(self._optimize_CLIP(snake_case_				,		snake_case_				,		snake_case_						)						):
            if show_intermediate:
                 show_pil(snake_case_						)
            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(snake_case_						)}						)
       if show_final:
            show_pil(snake_case_						)
       if save_final:
            transformed_img.save(os.path.join(self.save_path				,		F'''iter_{iter:03d}_final.png'''						)						)






