
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							:							int     ,						_A							:							str = "cpu"     ,						_A							:							str = "openai/clip-vit-large-patch14" ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		device
       __SCREAMING_SNAKE_CASE      :	int					=		CLIPTokenizerFast.from_pretrained(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		torchvision.transforms.Normalize(self.image_mean     ,						self.image_std )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		torchvision.transforms.Resize(224 )
       __SCREAMING_SNAKE_CASE      :	int					=		torchvision.transforms.CenterCrop(224 )



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							List[Any] ):







       """simple docstring"""




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




     def __call__(				self							:							int     ,						_A							:							Dict=None     ,						_A							:							str=None     ,						**_A							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.tokenizer(text=_A     ,						**_A )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.preprocess_img(_A )
       __SCREAMING_SNAKE_CASE      :	str					=		{key: value.to(self.device ) for (key, value) in encoding.items()}
       return encoding






class     __UpperCamelCase (							nn.Module       ):






     """simple docstring"""







     def __init__(				self							:							Any     ,						_A							:							Any=10     ,						_A							:							Tuple=0.01     ,						_A							:							int=None     ,						_A							:							Any=None     ,						_A							:							str=None     ,						_A							:							str=None     ,						_A							:							Union[str, Any]=None     ,						_A							:							int=None     ,						_A							:							str=False     ,						_A							:							int=True     ,						_A							:							str="image"     ,						_A							:							Union[str, Any]=True     ,						_A							:							Tuple=False     ,						_A							:							str=False     ,						_A							:							Dict=False     ,						):







       """simple docstring"""




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

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



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							int=None     ,						_A							:							str=None     ,						_A							:							str=5     ,						_A							:							str=True ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		[]
       if output_path is None:
         __SCREAMING_SNAKE_CASE      :	Dict					=		'''./animation.gif'''
       if input_path is None:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.save_path
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		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?)''' )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		total_duration / len(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[frame_duration] * len(_A )
       if extend_frames:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		1.5
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		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  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							Dict=None     ,						_A							:							Tuple=None ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		self.latent.detach().requires_grad_()
       __SCREAMING_SNAKE_CASE      :	Any					=		base_latent + transform_vector
       if self.quantize:
         __SCREAMING_SNAKE_CASE,		*__SCREAMING_SNAKE_CASE      :	List[Any]					=		self.vqgan.quantize(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	Tuple					=		trans_latent
       return self.vqgan.decode(_A )



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							Tuple     ,						_A							:							Dict     ,						_A							:							Optional[Any]=None ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		self.clip_preprocessor(text=_A     ,						images=_A     ,						return_tensors='''pt'''     ,						padding=_A )
       __SCREAMING_SNAKE_CASE      :	str					=		self.clip(**_A )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		clip_outputs.logits_per_image
       if weights is not None:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		similarity_logits * weights
       return similarity_logits.sum()



     def  UpperCAmelCase__							(				self							:							Tuple     ,						_A							:							List[Any]     ,						_A							:							List[Any]     ,						_A							:							Union[str, Any] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							str     ,						_A							:							List[Any]     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.randn_like(self.latent     ,						requires_grad=_A     ,						device=self.device )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		torch.optim.Adam([vector]     ,						lr=self.lr )

       for i in range(self.iterations ):
         optim.zero_grad()
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self._add_vector(_A )
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		loop_post_process(_A )
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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  UpperCAmelCase__							(				self							:							str     ,						_A							:							List[str]     ,						_A							:							int     ,						_A							:							List[Any] ):







       """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:
         __SCREAMING_SNAKE_CASE      :	Dict					=		Image.open(_A )
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		image.resize((256, 256) )
         wandb.log('''Original Image'''     ,						wandb.Image(_A ) )



     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							Optional[int] ):







       """simple docstring"""




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




     def  UpperCAmelCase__							(				self							:							Optional[int]     ,						_A							:							List[str]     ,						_A							:							List[Any]=None     ,						_A							:							Tuple=None     ,						_A							:							Dict=True     ,						_A							:							int=False     ,						_A							:							List[Any]=True     ,						_A							:							Tuple=True     ,						_A							:							int=None     ,						):







       """simple docstring"""




       if image_path:
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self._get_latent(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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."
       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.process_prompts(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.process_prompts(_A )
       if save_final and save_path is None:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		os.path.join('''./outputs/'''     ,						'''_'''.join(pos_prompts['''prompts'''] ) )
       if not os.path.exists(_A ):
         os.makedirs(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	str					=		save_path + '''_''' + get_timestamp()
         os.makedirs(_A )
       __SCREAMING_SNAKE_CASE      :	Dict					=		save_path

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

       __SCREAMING_SNAKE_CASE      :	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''' ) )




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			:  Dict										=   {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[Any]										=   ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  int										=   [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[int]										=   [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[str]										=   [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






