

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       snake_case__  :







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





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				device
								UpperCAmelCase			:   Dict      =				CLIPTokenizerFast.from_pretrained(lowercase  )
								UpperCAmelCase			:   Any      =				[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
								UpperCAmelCase			:   List[str]      =				[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
								UpperCAmelCase			:   Dict      =				torchvision.transforms.Normalize(self.image_mean			,  self.image_std  )
								UpperCAmelCase			:   Optional[Any]      =				torchvision.transforms.Resize(2_24  )
								UpperCAmelCase			:   str      =				torchvision.transforms.CenterCrop(2_24  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.resize(lowercase  )
								UpperCAmelCase			:   Dict      =				self.center_crop(lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				self.normalize(lowercase  )
								return images







			def __call__(			self			: int			,  lowercase			: Any=None			,  lowercase			: Optional[int]=None			,  **lowercase			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.tokenizer(text=lowercase			,  **lowercase  )
								UpperCAmelCase			:   Dict      =				self.preprocess_img(lowercase  )
								UpperCAmelCase			:   int      =				{key: value.to(self.device  ) for (key, value) in encoding.items()}
								return encoding







class       snake_case__  ( nn.Module     ):







			def __init__(			self			: Dict			,  lowercase			: Optional[int]=10			,  lowercase			: Union[str, Any]=0.0_1			,  lowercase			: str=None			,  lowercase			: Union[str, Any]=None			,  lowercase			: Any=None			,  lowercase			: int=None			,  lowercase			: List[Any]=None			,  lowercase			: int=None			,  lowercase			: Optional[int]=False			,  lowercase			: int=True			,  lowercase			: str="image"			,  lowercase			: List[str]=True			,  lowercase			: List[Any]=False			,  lowercase			: List[Any]=False			,  lowercase			: Optional[int]=False			,  ):





								'''simple docstring'''



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

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







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Dict=None			,  lowercase			: str=None			,  lowercase			: Union[str, Any]=5			,  lowercase			: Union[str, Any]=True  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Optional[int]=None			,  lowercase			: Union[str, Any]=None  ):





								'''simple docstring'''



								if not (path or img):
													raise ValueError("Input either path or tensor"  )
								if img is not None:
													raise NotImplementedError
								UpperCAmelCase			:   str      =				preprocess(Image.open(lowercase  )			,  target_image_size=2_56  ).to(self.device  )
								UpperCAmelCase			:   Any      =				preprocess_vqgan(lowercase  )
								UpperCAmelCase			:   Dict      =				self.vqgan.encode(lowercase  )
								return z







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				self.latent.detach().requires_grad_()
								UpperCAmelCase			:   int      =				base_latent + transform_vector
								if self.quantize:
													UpperCAmelCase			:   Optional[Any]      =				self.vqgan.quantize(lowercase  )
								else:
													UpperCAmelCase			:   Union[str, Any]      =				trans_latent
								return self.vqgan.decode(lowercase  )







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: str			,  lowercase			: Any			,  lowercase			: List[str]=None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.clip_preprocessor(text=lowercase			,  images=lowercase			,  return_tensors="pt"			,  padding=lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				self.clip(**lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				clip_outputs.logits_per_image
								if weights is not None:
													UpperCAmelCase			:   Optional[int]      =				similarity_logits * weights
								return similarity_logits.sum()







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: Dict			,  lowercase			: Union[str, Any]			,  lowercase			: Tuple  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: str			,  lowercase			: Dict			,  lowercase			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				torch.randn_like(self.latent			,  requires_grad=lowercase			,  device=self.device  )
								UpperCAmelCase			:   int      =				torch.optim.Adam([vector]			,  lr=self.lr  )

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







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Tuple			,  lowercase			: List[Any]			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								wandb.init(reinit=lowercase			,  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			:   Tuple      =				Image.open(lowercase  )
													UpperCAmelCase			:   int      =				image.resize((2_56, 2_56)  )
													wandb.log("Original Image"			,  wandb.Image(lowercase  )  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: List[str]			,  lowercase			: Dict=None			,  lowercase			: str=None			,  lowercase			: Tuple=True			,  lowercase			: List[str]=False			,  lowercase			: List[Any]=True			,  lowercase			: List[str]=True			,  lowercase			: List[str]=None			,  ):





								'''simple docstring'''



								if image_path:
													UpperCAmelCase			:   List[str]      =				self._get_latent(lowercase  )
								else:
													UpperCAmelCase			:   Optional[int]      =				torch.randn(self.latent_dim			,  device=self.device  )
								if self.log:
													self._init_logging(lowercase			,  lowercase			,  lowercase  )

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

								UpperCAmelCase			:   List[Any]      =				self.vqgan.decode(self.latent  )[0]
								if show_intermediate:
													print("Original Image"  )
													show_pil(custom_to_pil(lowercase  )  )

								UpperCAmelCase			:   List[str]      =				loop_post_process(lowercase  )
								for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase			,  lowercase			,  lowercase  )  ):
													if show_intermediate:
																		show_pil(lowercase  )
													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(lowercase  )}  )
								if show_final:
													show_pil(lowercase  )
								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_tokenizers_available, is_torch_available


snake_case_   :   Dict        =       {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   List[Any]        =       ["""MvpTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Any        =       [
	    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MvpForCausalLM""",
	    """MvpForConditionalGeneration""",
	    """MvpForQuestionAnswering""",
	    """MvpForSequenceClassification""",
	    """MvpModel""",
	    """MvpPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

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


