


"""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 			A__	:


     """simple docstring"""

     def __init__(	self:						Optional[int]     ,		__a:						str = "cpu"     ,		__a:						str = "openai/clip-vit-large-patch14"	)->    None:
           lowerCamelCase					:					List[str]        =	device
           lowerCamelCase					:					Tuple        =	CLIPTokenizerFast.from_pretrained(__a	)
           lowerCamelCase					:					Dict        =	[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
           lowerCamelCase					:					Dict        =	[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
           lowerCamelCase					:					Tuple        =	torchvision.transforms.Normalize(self.image_mean     ,		self.image_std	)
           lowerCamelCase					:					List[Any]        =	torchvision.transforms.Resize(224	)
           lowerCamelCase					:					Dict        =	torchvision.transforms.CenterCrop(224	)

     def 		a__						(	self:						Tuple     ,		__a:						str	)->    List[str]:
           lowerCamelCase					:					List[str]        =	self.resize(__a	)
           lowerCamelCase					:					str        =	self.center_crop(__a	)
           lowerCamelCase					:					int        =	self.normalize(__a	)
           return images







     def __call__(	self:						int     ,		__a:						Optional[Any]=None     ,		__a:						Dict=None     ,		**__a:						int	)->    Tuple:
           lowerCamelCase					:					str        =	self.tokenizer(text=__a     ,		**__a	)
           lowerCamelCase					:					Optional[int]        =	self.preprocess_img(__a	)
           lowerCamelCase					:					str        =	{key: value.to(self.device	) for (key, value) in encoding.items()}
           return encoding


class 			A__	(						nn.Module):


     """simple docstring"""

     def __init__(	self:						List[str]     ,		__a:						Optional[Any]=10     ,		__a:						Dict=0.01     ,		__a:						Optional[Any]=None     ,		__a:						List[Any]=None     ,		__a:						Any=None     ,		__a:						int=None     ,		__a:						Tuple=None     ,		__a:						int=None     ,		__a:						Union[str, Any]=False     ,		__a:						str=True     ,		__a:						List[str]="image"     ,		__a:						Any=True     ,		__a:						Dict=False     ,		__a:						str=False     ,		__a:						Any=False     ,		)->    None:
           super().__init__()
           lowerCamelCase					:					Any        =	None
           lowerCamelCase					:					Optional[int]        =	device if device else get_device()
           if vqgan:
                 lowerCamelCase					:					List[Any]        =	vqgan
           else:
                 lowerCamelCase					:					Union[str, Any]        =	load_vqgan(self.device     ,		conf_path=__a     ,		ckpt_path=__a	)
           self.vqgan.eval()
           if clip:
                 lowerCamelCase					:					str        =	clip
           else:
                 lowerCamelCase					:					Tuple        =	CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""	)
           self.clip.to(self.device	)
           lowerCamelCase					:					Any        =	ProcessorGradientFlow(device=self.device	)

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

     def 		a__						(	self:						int     ,		__a:						Optional[Any]=None     ,		__a:						List[Any]=None     ,		__a:						List[Any]=5     ,		__a:						List[str]=True	)->    List[Any]:
           lowerCamelCase					:					Optional[int]        =	[]
           if output_path is None:
                 lowerCamelCase					:					Optional[int]        =	"""./animation.gif"""
           if input_path is None:
                 lowerCamelCase					:					List[str]        =	self.save_path
           lowerCamelCase					:					str        =	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?)"""	)
           lowerCamelCase					:					List[Any]        =	total_duration / len(__a	)
           lowerCamelCase					:					int        =	[frame_duration] * len(__a	)
           if extend_frames:
                 lowerCamelCase					:					Tuple        =	1.5
                 lowerCamelCase					:					str        =	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 		a__						(	self:						Optional[Any]     ,		__a:						Tuple=None     ,		__a:						Any=None	)->    Optional[int]:
           if not (path or img):
                 raise ValueError("""Input either path or tensor"""	)
           if img is not None:
                 raise NotImplementedError
           lowerCamelCase					:					Tuple        =	preprocess(Image.open(__a	)     ,		target_image_size=256	).to(self.device	)
           lowerCamelCase					:					Dict        =	preprocess_vqgan(__a	)
           lowerCamelCase     ,			*lowerCamelCase					:					List[str]        =	self.vqgan.encode(__a	)
           return z

     def 		a__						(	self:						Optional[Any]     ,		__a:						Dict	)->    Dict:
           lowerCamelCase					:					List[str]        =	self.latent.detach().requires_grad_()
           lowerCamelCase					:					int        =	base_latent + transform_vector
           if self.quantize:
                 lowerCamelCase     ,			*lowerCamelCase					:					Union[str, Any]        =	self.vqgan.quantize(__a	)
           else:
                 lowerCamelCase					:					Optional[Any]        =	trans_latent
           return self.vqgan.decode(__a	)

     def 		a__						(	self:						str     ,		__a:						Optional[int]     ,		__a:						List[str]     ,		__a:						List[Any]=None	)->    Optional[Any]:
           lowerCamelCase					:					Optional[int]        =	self.clip_preprocessor(text=__a     ,		images=__a     ,		return_tensors="""pt"""     ,		padding=__a	)
           lowerCamelCase					:					Optional[int]        =	self.clip(**__a	)
           lowerCamelCase					:					Optional[int]        =	clip_outputs.logits_per_image
           if weights is not None:
                 lowerCamelCase					:					List[str]        =	similarity_logits * weights
           return similarity_logits.sum()

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

     def 		a__						(	self:						Optional[int]     ,		__a:						List[str]     ,		__a:						Optional[int]     ,		__a:						Dict	)->    int:
           lowerCamelCase					:					Tuple        =	torch.randn_like(self.latent     ,		requires_grad=__a     ,		device=self.device	)
           lowerCamelCase					:					int        =	torch.optim.Adam([vector]     ,		lr=self.lr	)

           for i in range(self.iterations	):
                 optim.zero_grad()
                 lowerCamelCase					:					Optional[Any]        =	self._add_vector(__a	)
                 lowerCamelCase					:					Optional[Any]        =	loop_post_process(__a	)
                 lowerCamelCase					:					Dict        =	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 		a__						(	self:						Optional[Any]     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Tuple	)->    List[Any]:
           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:
                 lowerCamelCase					:					Dict        =	Image.open(__a	)
                 lowerCamelCase					:					Optional[Any]        =	image.resize((256, 256)	)
                 wandb.log("""Original Image"""     ,		wandb.Image(__a	)	)

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







     def 		a__						(	self:						List[Any]     ,		__a:						int     ,		__a:						Any=None     ,		__a:						Tuple=None     ,		__a:						Tuple=True     ,		__a:						Any=False     ,		__a:						Any=True     ,		__a:						List[str]=True     ,		__a:						int=None     ,		)->    Union[str, Any]:
           if image_path:
                 lowerCamelCase					:					str        =	self._get_latent(__a	)
           else:
                 lowerCamelCase					:					List[str]        =	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."
           lowerCamelCase					:					int        =	self.process_prompts(__a	)
           lowerCamelCase					:					int        =	self.process_prompts(__a	)
           if save_final and save_path is None:
                 lowerCamelCase					:					str        =	os.path.join("""./outputs/"""     ,		"""_""".join(pos_prompts["""prompts"""]	)	)
           if not os.path.exists(__a	):
                 os.makedirs(__a	)
           else:
                 lowerCamelCase					:					Dict        =	save_path + """_""" + get_timestamp()
                 os.makedirs(__a	)
           lowerCamelCase					:					Tuple        =	save_path

           lowerCamelCase					:					str        =	self.vqgan.decode(self.latent	)[0]
           if show_intermediate:
                 print("""Original Image"""	)
                 show_pil(custom_to_pil(__a	)	)

           lowerCamelCase					:					Optional[int]        =	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'	)	)








"""simple docstring"""


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

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :Dict	    =		logging.get_logger()




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						LevitConfig			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						bool = True    )   ->      Dict:
      print(F'Converting {name}...'    )

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        lowerCamelCase					:					Optional[Any]        =	timm.create_model("""levit_128s"""			,				pretrained=UpperCamelCase__    )
                  else:
                        lowerCamelCase					:					Dict        =	timm.create_model("""levit_128"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 192:
                  lowerCamelCase					:					Tuple        =	timm.create_model("""levit_192"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 256:
                  lowerCamelCase					:					Optional[int]        =	timm.create_model("""levit_256"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 384:
                  lowerCamelCase					:					Dict        =	timm.create_model("""levit_384"""			,				pretrained=UpperCamelCase__    )

            from_model.eval()
            lowerCamelCase					:					Optional[Any]        =	LevitForImageClassificationWithTeacher(UpperCamelCase__    ).eval()
            lowerCamelCase					:					Tuple        =	OrderedDict()

            lowerCamelCase					:					Optional[Any]        =	from_model.state_dict()
            lowerCamelCase					:					str        =	list(from_model.state_dict().keys()    )
            lowerCamelCase					:					List[Any]        =	list(our_model.state_dict().keys()    )
            print(len(UpperCamelCase__    )			,				len(UpperCamelCase__    )    )
            for i in range(len(UpperCamelCase__    )    ):
                  lowerCamelCase					:					str        =	weights[og_keys[i]]
            our_model.load_state_dict(UpperCamelCase__    )

            lowerCamelCase					:					int        =	torch.randn((2, 3, 224, 224)    )
            lowerCamelCase					:					Any        =	from_model(UpperCamelCase__    )
            lowerCamelCase					:					List[Any]        =	our_model(UpperCamelCase__    ).logits

      assert torch.allclose(UpperCamelCase__			,				UpperCamelCase__    ), "The model logits don't match the original one."

      lowerCamelCase					:					Dict        =	name
      print(UpperCamelCase__    )

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name    )
            lowerCamelCase					:					Optional[int]        =	LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name    )

            print(F'Pushed {checkpoint_name}'    )




def   snake_case		(     UpperCamelCase__       :						Path			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						bool = True    )   ->      Optional[int]:
      lowerCamelCase					:					Optional[Any]        =	"""imagenet-1k-id2label.json"""
      lowerCamelCase					:					List[Any]        =	1000
      lowerCamelCase					:					Dict        =	(1, num_labels)

      lowerCamelCase					:					List[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Optional[int]        =	num_labels
      lowerCamelCase					:					List[str]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					Any        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}

      lowerCamelCase					:					List[Any]        =	idalabel
      lowerCamelCase					:					str        =	{v: k for k, v in idalabel.items()}

      lowerCamelCase					:					Tuple        =	partial(UpperCamelCase__			,				num_labels=UpperCamelCase__			,				idalabel=UpperCamelCase__			,				labelaid=UpperCamelCase__    )

      lowerCamelCase					:					Optional[int]        =	{
          """levit-128S""": 128,
          """levit-128""": 128,
          """levit-192""": 192,
          """levit-256""": 256,
          """levit-384""": 384,
      }

      lowerCamelCase					:					List[Any]        =	{
          """levit-128S""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 6, 8]			,				depths=[2, 3, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-128""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 8, 12]			,				depths=[4, 4, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-192""": ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384]			,				num_attention_heads=[3, 5, 6]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-256""": ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512]			,				num_attention_heads=[4, 6, 8]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-384""": ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768]			,				num_attention_heads=[6, 9, 12]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0.1			,				),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				names_to_config[model_name]			,				UpperCamelCase__			,				UpperCamelCase__    )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      return config, expected_shape


if __name__ == "__main__":
    __lowerCamelCase      :Union[str, Any]	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default=None,
        type=str,
        help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='levit-dump-folder/',
        type=Path,
        required=False,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
    parser.add_argument(
        '--no-push_to_hub',
        dest='push_to_hub',
        action='store_false',
        help='Do not push model and image processor to the hub',
    )

    __lowerCamelCase      :List[Any]	    =		parser.parse_args()
    __lowerCamelCase      :Path	    =		args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




