


"""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      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : List[str] ,       UpperCamelCase    : str = "cpu" ,       UpperCamelCase    : str = "openai/clip-vit-large-patch14"  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					device
												__UpperCAmelCase   :						str      							=					CLIPTokenizerFast.from_pretrained(snake_case__  )
												__UpperCAmelCase   :						List[Any]      							=					[0.48145466, 0.4578275, 0.40821073]
												__UpperCAmelCase   :						Optional[int]      							=					[0.26862954, 0.26130258, 0.27577711]
												__UpperCAmelCase   :						List[Any]      							=					torchvision.transforms.Normalize(self.image_mean ,       self.image_std  )
												__UpperCAmelCase   :						List[str]      							=					torchvision.transforms.Resize(224  )
												__UpperCAmelCase   :						List[Any]      							=					torchvision.transforms.CenterCrop(224  )






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






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






					def __call__(       self    : Optional[int] ,       UpperCamelCase    : Optional[int]=None ,       UpperCamelCase    : List[Any]=None ,       **UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					self.tokenizer(text=snake_case__ ,       **snake_case__  )
												__UpperCAmelCase   :						str      							=					self.preprocess_img(snake_case__  )
												__UpperCAmelCase   :						Any      							=					{key: value.to(self.device  ) for (key, value) in encoding.items()}
												return encoding






class      lowerCamelCase__   (     nn.Module				):







					"""simple docstring"""


					def __init__(       self    : int ,       UpperCamelCase    : Tuple=10 ,       UpperCamelCase    : List[str]=0.01 ,       UpperCamelCase    : Dict=None ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : Dict=None ,       UpperCamelCase    : Optional[Any]=None ,       UpperCamelCase    : Union[str, Any]=None ,       UpperCamelCase    : Dict=None ,       UpperCamelCase    : Any=False ,       UpperCamelCase    : Optional[Any]=True ,       UpperCamelCase    : Any="image" ,       UpperCamelCase    : str=True ,       UpperCamelCase    : Any=False ,       UpperCamelCase    : List[str]=False ,       UpperCamelCase    : Optional[Any]=False ,       ):
												'''simple docstring'''






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

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






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : Dict=None ,       UpperCamelCase    : Tuple=None ,       UpperCamelCase    : Any=5 ,       UpperCamelCase    : Optional[Any]=True  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : str ,       UpperCamelCase    : str=None ,       UpperCamelCase    : List[Any]=None  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : Tuple  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : Optional[int]=None  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : Dict  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Optional[Any] ,       UpperCamelCase    : Union[str, Any] ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[Any]  ):
												'''simple docstring'''






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

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






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str ,       UpperCamelCase    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												wandb.init(reinit=snake_case__ ,       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   :						Optional[Any]      							=					Image.open(snake_case__  )
																			__UpperCAmelCase   :						Dict      							=					image.resize((256, 256)  )
																			wandb.log("""Original Image""" ,       wandb.Image(snake_case__  )  )






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : Dict ,       UpperCamelCase    : Union[str, Any]=None ,       UpperCamelCase    : str=None ,       UpperCamelCase    : Union[str, Any]=True ,       UpperCamelCase    : Any=False ,       UpperCamelCase    : List[Any]=True ,       UpperCamelCase    : List[Any]=True ,       UpperCamelCase    : Dict=None ,       ):
												'''simple docstring'''






												if image_path:
																			__UpperCAmelCase   :						List[Any]      							=					self._get_latent(snake_case__  )
												else:
																			__UpperCAmelCase   :						Optional[int]      							=					torch.randn(self.latent_dim ,       device=self.device  )
												if self.log:
																			self._init_logging(snake_case__ ,       snake_case__ ,       snake_case__  )

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

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

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

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





