



'''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__     :


	def __init__(self : List[Any],					__UpperCAmelCase : str = "cpu",					__UpperCAmelCase : str = "openai/clip-vit-large-patch14"      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= device
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= CLIPTokenizerFast.from_pretrained(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [0.4814_5466, 0.457_8275, 0.4082_1073]
					SCREAMING_SNAKE_CASE  :						Any      					= [0.2686_2954, 0.2613_0258, 0.2757_7711]
					SCREAMING_SNAKE_CASE  :						str      					= torchvision.transforms.Normalize(self.image_mean,					self.image_std      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torchvision.transforms.Resize(224      )
					SCREAMING_SNAKE_CASE  :						Any      					= torchvision.transforms.CenterCrop(224      )


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Dict      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.resize(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.center_crop(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= self.normalize(__UpperCAmelCase      )
					return images






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

					"""simple docstring"""

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



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

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

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


	def 				lowercase__  (self : int,					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=5,					__UpperCAmelCase : Tuple=True      )     ->			Any:

					"""simple docstring"""

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


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : Tuple=None      )     ->			Any:

					"""simple docstring"""

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


	def 				lowercase__  (self : int,					__UpperCAmelCase : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

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


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

					"""simple docstring"""

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


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= torch.randn_like(self.latent,					requires_grad=__UpperCAmelCase,					device=self.device      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.optim.Adam([vector],					lr=self.lr      )

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


	def 				lowercase__  (self : str,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Dict      )     ->			Optional[int]:

					"""simple docstring"""

					wandb.init(reinit=__UpperCAmelCase,					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  :						List[str]      					= Image.open(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= image.resize((256, 256)      )
									wandb.log('''Original Image''',					wandb.Image(__UpperCAmelCase      )      )


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : int      )     ->			Union[str, Any]:

					"""simple docstring"""

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






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

					"""simple docstring"""

					if image_path:
									SCREAMING_SNAKE_CASE  :						str      					= self._get_latent(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						Any      					= torch.randn(self.latent_dim,					device=self.device      )
					if self.log:
									self._init_logging(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )

					assert pos_prompts, "You must provide at least one positive prompt."
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.process_prompts(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= self.process_prompts(__UpperCAmelCase      )
					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(__UpperCAmelCase      ):
									os.makedirs(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= save_path + '''_''' + get_timestamp()
									os.makedirs(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= save_path

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= loop_post_process(__UpperCAmelCase      )
					for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )      ):
									if show_intermediate:
													show_pil(__UpperCAmelCase      )
									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(__UpperCAmelCase      )}      )
					if show_final:
									show_pil(__UpperCAmelCase      )
					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_torch_available


snake_case_			=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimesformerModel""",
	    """TimesformerForVideoClassification""",
	    """TimesformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	snake_case_			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




