



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    UpperCAmelCase__:

   '''simple docstring'''




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




         """simple docstring"""

         lowercase__  =	device
         lowercase__  =	CLIPTokenizerFast.from_pretrained(lowerCAmelCase)
         lowercase__  =	[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
         lowercase__  =	[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
         lowercase__  =	torchvision.transforms.Normalize(self.image_mean      ,					self.image_std)
         lowercase__  =	torchvision.transforms.Resize(2_24)
         lowercase__  =	torchvision.transforms.CenterCrop(2_24)


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Dict)						->					Dict:




         """simple docstring"""

         lowercase__  =	self.resize(lowerCAmelCase)
         lowercase__  =	self.center_crop(lowerCAmelCase)
         lowercase__  =	self.normalize(lowerCAmelCase)
         return images



   def __call__(    self	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]=None      ,					lowerCAmelCase	:      Tuple=None      ,					**lowerCAmelCase	:      List[str])						->					str:




         """simple docstring"""

         lowercase__  =	self.tokenizer(text=lowerCAmelCase      ,					**lowerCAmelCase)
         lowercase__  =	self.preprocess_img(lowerCAmelCase)
         lowercase__  =	{key: value.to(self.device) for (key, value) in encoding.items()}
         return encoding



class    UpperCAmelCase__(				nn.Module						):

   '''simple docstring'''




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




         """simple docstring"""

         super().__init__()
         lowercase__  =	None
         lowercase__  =	device if device else get_device()
         if vqgan:
               lowercase__  =	vqgan
         else:
               lowercase__  =	load_vqgan(self.device      ,					conf_path=lowerCAmelCase      ,					ckpt_path=lowerCAmelCase)
         self.vqgan.eval()
         if clip:
               lowercase__  =	clip
         else:
               lowercase__  =	CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
         self.clip.to(self.device)
         lowercase__  =	ProcessorGradientFlow(device=self.device)

         lowercase__  =	iterations
         lowercase__  =	lr
         lowercase__  =	log
         lowercase__  =	make_grid
         lowercase__  =	return_val
         lowercase__  =	quantize
         lowercase__  =	self.vqgan.decoder.z_shape


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      int=None      ,					lowerCAmelCase	:      Any=None      ,					lowerCAmelCase	:      Dict=5      ,					lowerCAmelCase	:      str=True)						->					Dict:




         """simple docstring"""

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


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




         """simple docstring"""

         if not (path or img):
               raise ValueError('Input either path or tensor')
         if img is not None:
               raise NotImplementedError
         lowercase__  =	preprocess(Image.open(lowerCAmelCase)      ,					target_image_size=2_56).to(self.device)
         lowercase__  =	preprocess_vqgan(lowerCAmelCase)
         lowercase__,						*lowercase__  =	self.vqgan.encode(lowerCAmelCase)
         return z


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Tuple)						->					Dict:




         """simple docstring"""

         lowercase__  =	self.latent.detach().requires_grad_()
         lowercase__  =	base_latent + transform_vector
         if self.quantize:
               lowercase__,						*lowercase__  =	self.vqgan.quantize(lowerCAmelCase)
         else:
               lowercase__  =	trans_latent
         return self.vqgan.decode(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]=None)						->					List[str]:




         """simple docstring"""

         lowercase__  =	self.clip_preprocessor(text=lowerCAmelCase      ,					images=lowerCAmelCase      ,					return_tensors='pt'      ,					padding=lowerCAmelCase)
         lowercase__  =	self.clip(**lowerCAmelCase)
         lowercase__  =	clip_outputs.logits_per_image
         if weights is not None:
               lowercase__  =	similarity_logits * weights
         return similarity_logits.sum()


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[Any])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self._get_clip_similarity(pos_prompts['prompts']      ,					lowerCAmelCase      ,					weights=(1 / pos_prompts['weights']))
         if neg_prompts:
               lowercase__  =	self._get_clip_similarity(neg_prompts['prompts']      ,					lowerCAmelCase      ,					weights=neg_prompts['weights'])
         else:
               lowercase__  =	torch.tensor([1]      ,					device=self.device)
         lowercase__  =	-torch.log(lowerCAmelCase) + torch.log(lowerCAmelCase)
         return loss


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      List[Any])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	torch.randn_like(self.latent      ,					requires_grad=lowerCAmelCase      ,					device=self.device)
         lowercase__  =	torch.optim.Adam([vector]      ,					lr=self.lr)

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


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[str])						->					Optional[int]:




         """simple docstring"""

         wandb.init(reinit=lowerCAmelCase      ,					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:
               lowercase__  =	Image.open(lowerCAmelCase)
               lowercase__  =	image.resize((2_56, 2_56))
               wandb.log('Original Image'      ,					wandb.Image(lowerCAmelCase))


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      Optional[int])						->					List[str]:




         """simple docstring"""

         if not prompts:
               return []
         lowercase__  =	[]
         lowercase__  =	[]
         if isinstance(lowerCAmelCase      ,					lowerCAmelCase):
               lowercase__  =	[prompt.strip() for prompt in prompts.split('|')]
         for prompt in prompts:
               if isinstance(lowerCAmelCase      ,					(tuple, list)):
                     lowercase__  =	prompt[0]
                     lowercase__  =	float(prompt[1])
               elif ":" in prompt:
                     lowercase__,						lowercase__  =	prompt.split(':')
                     lowercase__  =	float(lowerCAmelCase)
               else:
                     lowercase__  =	prompt
                     lowercase__  =	1.0
               processed_prompts.append(lowerCAmelCase)
               weights.append(lowerCAmelCase)
         return {
             "prompts": processed_prompts,
             "weights": torch.tensor(lowerCAmelCase      ,					device=self.device),
         }



   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      List[str]=None      ,					lowerCAmelCase	:      int=None      ,					lowerCAmelCase	:      List[str]=True      ,					lowerCAmelCase	:      Tuple=False      ,					lowerCAmelCase	:      Dict=True      ,					lowerCAmelCase	:      Optional[Any]=True      ,					lowerCAmelCase	:      Optional[int]=None      ,					)						->					int:




         """simple docstring"""

         if image_path:
               lowercase__  =	self._get_latent(lowerCAmelCase)
         else:
               lowercase__  =	torch.randn(self.latent_dim      ,					device=self.device)
         if self.log:
               self._init_logging(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

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

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

         lowercase__  =	loop_post_process(lowerCAmelCase)
         for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)):
               if show_intermediate:
                     show_pil(lowerCAmelCase)
               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(lowerCAmelCase)})
         if show_final:
               show_pil(lowerCAmelCase)
         if save_final:
               transformed_img.save(os.path.join(self.save_path      ,					f'''iter_{iter:03d}_final.png'''))





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	SwinConfig(image_size=192						)

      if "base" in model_name:
            lowercase__  =	6
            lowercase__  =	128
            lowercase__  =	(2, 2, 18, 2)
            lowercase__  =	(4, 8, 16, 32)
      elif "large" in model_name:
            lowercase__  =	12
            lowercase__  =	192
            lowercase__  =	(2, 2, 18, 2)
            lowercase__  =	(6, 12, 24, 48)
      else:
            raise ValueError('Model not supported, only supports base and large variants'						)

      lowercase__  =	window_size
      lowercase__  =	embed_dim
      lowercase__  =	depths
      lowercase__  =	num_heads

      return config

def 	_lowerCAmelCase (    A__						):
      if "encoder.mask_token" in name:
            lowercase__  =	name.replace('encoder.mask_token'    ,	'embeddings.mask_token'						)
      if "encoder.patch_embed.proj" in name:
            lowercase__  =	name.replace('encoder.patch_embed.proj'    ,	'embeddings.patch_embeddings.projection'						)
      if "encoder.patch_embed.norm" in name:
            lowercase__  =	name.replace('encoder.patch_embed.norm'    ,	'embeddings.norm'						)
      if "attn.proj" in name:
            lowercase__  =	name.replace('attn.proj'    ,	'attention.output.dense'						)
      if "attn" in name:
            lowercase__  =	name.replace('attn'    ,	'attention.self'						)
      if "norm1" in name:
            lowercase__  =	name.replace('norm1'    ,	'layernorm_before'						)
      if "norm2" in name:
            lowercase__  =	name.replace('norm2'    ,	'layernorm_after'						)
      if "mlp.fc1" in name:
            lowercase__  =	name.replace('mlp.fc1'    ,	'intermediate.dense'						)
      if "mlp.fc2" in name:
            lowercase__  =	name.replace('mlp.fc2'    ,	'output.dense'						)

      if name == "encoder.norm.weight":
            lowercase__  =	'layernorm.weight'
      if name == "encoder.norm.bias":
            lowercase__  =	'layernorm.bias'

      if "decoder" in name:
            pass
      else:
            lowercase__  =	'swin.' + name

      return name

def 	_lowerCAmelCase (    A__    ,	A__						):
      for key in orig_state_dict.copy().keys():
            lowercase__  =	orig_state_dict.pop(A__						)

            if "attn_mask" in key:
                  pass
            elif "qkv" in key:
                  lowercase__  =	key.split('.'						)
                  lowercase__  =	int(key_split[2]						)
                  lowercase__  =	int(key_split[4]						)
                  lowercase__  =	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        lowercase__  =	val[:dim, :]
                        lowercase__  =	val[
                            dim : dim * 2, :
                        ]
                        lowercase__  =	val[-dim:, :]
                  else:
                        lowercase__  =	val[
                            :dim
                        ]
                        lowercase__  =	val[
                            dim : dim * 2
                        ]
                        lowercase__  =	val[
                            -dim:
                        ]
            else:
                  lowercase__  =	val

      return orig_state_dict

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__						):
      lowercase__  =	torch.load(A__    ,	map_location='cpu'						)['model']

      lowercase__  =	get_swin_config(A__						)
      lowercase__  =	SwinForMaskedImageModeling(A__						)
      model.eval()

      lowercase__  =	convert_state_dict(A__    ,	A__						)
      model.load_state_dict(A__						)

      lowercase__  =	'http://images.cocodataset.org/val2017/000000039769.jpg'

      lowercase__  =	ViTImageProcessor(size={'height': 192, 'width': 192}						)
      lowercase__  =	Image.open(requests.get(A__    ,	stream=A__						).raw						)
      lowercase__  =	image_processor(images=A__    ,	return_tensors='pt'						)

      with torch.no_grad():
            lowercase__  =	model(**A__						).logits

      print(outputs.keys()						)
      print('Looks ok!'						)

      if pytorch_dump_folder_path is not None:
            print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''						)
            model.save_pretrained(A__						)

            print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
            image_processor.save_pretrained(A__						)

      if push_to_hub:
            print(F'''Pushing model and image processor for {model_name} to hub'''						)
            model.push_to_hub(F'''microsoft/{model_name}'''						)
            image_processor.push_to_hub(F'''microsoft/{model_name}'''						)


if __name__ == "__main__":
       a__   :					Optional[Any] 						=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="swin-base-simmim-window6-192",
           type=str,
           choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
           help="Name of the Swin SimMIM model you'd like to convert.",
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
           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 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."
       )

       a__   :					Optional[int] 						=      parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
