
"""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 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__ = "cpu"							,  lowerCAmelCase__ = "openai/clip-vit-large-patch14"):
				__SCREAMING_SNAKE_CASE            = device
				__SCREAMING_SNAKE_CASE            = CLIPTokenizerFast.from_pretrained(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
				__SCREAMING_SNAKE_CASE            = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
				__SCREAMING_SNAKE_CASE            = torchvision.transforms.Normalize(self.image_mean							,  self.image_std)
				__SCREAMING_SNAKE_CASE            = torchvision.transforms.Resize(2_2_4)
				__SCREAMING_SNAKE_CASE            = torchvision.transforms.CenterCrop(2_2_4)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.resize(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.center_crop(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.normalize(lowerCAmelCase__)
				return images




		def __call__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.tokenizer(text=lowerCAmelCase__							,  **lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.preprocess_img(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = {key: value.to(self.device) for (key, value) in encoding.items()}
				return encoding



class 						SCREAMING_SNAKE_CASE_ (						nn.Module ):




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__=1_0							,  lowerCAmelCase__=0.01							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=False							,  lowerCAmelCase__=True							,  lowerCAmelCase__="image"							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=False							,  lowerCAmelCase__=False							,  ):
				super().__init__()
				__SCREAMING_SNAKE_CASE            = None
				__SCREAMING_SNAKE_CASE            = device if device else get_device()
				if vqgan:
						__SCREAMING_SNAKE_CASE            = vqgan
				else:
						__SCREAMING_SNAKE_CASE            = load_vqgan(self.device							,  conf_path=lowerCAmelCase__							,  ckpt_path=lowerCAmelCase__)
				self.vqgan.eval()
				if clip:
						__SCREAMING_SNAKE_CASE            = clip
				else:
						__SCREAMING_SNAKE_CASE            = CLIPModel.from_pretrained("""openai/clip-vit-base-patch32""")
				self.clip.to(self.device)
				__SCREAMING_SNAKE_CASE            = ProcessorGradientFlow(device=self.device)

				__SCREAMING_SNAKE_CASE            = iterations
				__SCREAMING_SNAKE_CASE            = lr
				__SCREAMING_SNAKE_CASE            = log
				__SCREAMING_SNAKE_CASE            = make_grid
				__SCREAMING_SNAKE_CASE            = return_val
				__SCREAMING_SNAKE_CASE            = quantize
				__SCREAMING_SNAKE_CASE            = self.vqgan.decoder.z_shape




		def 	snake_case_     (     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=5							,  lowerCAmelCase__=True):
				__SCREAMING_SNAKE_CASE            = []
				if output_path is None:
						__SCREAMING_SNAKE_CASE            = """./animation.gif"""
				if input_path is None:
						__SCREAMING_SNAKE_CASE            = self.save_path
				__SCREAMING_SNAKE_CASE            = sorted(glob(input_path + """/*"""))
				if not len(lowerCAmelCase__):
						raise ValueError(
						    """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
						    """ function?)""")
				if len(lowerCAmelCase__) == 1:
						print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)""")
				__SCREAMING_SNAKE_CASE            = total_duration / len(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = [frame_duration] * len(lowerCAmelCase__)
				if extend_frames:
						__SCREAMING_SNAKE_CASE            = 1.5
						__SCREAMING_SNAKE_CASE            = 3
				for file_name in paths:
						if file_name.endswith(""".png"""):
								images.append(imageio.imread(lowerCAmelCase__))
				imageio.mimsave(lowerCAmelCase__							,  lowerCAmelCase__							,  duration=lowerCAmelCase__)
				print(f"gif saved to {output_path}")




		def 	snake_case_     (     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None):
				if not (path or img):
						raise ValueError("""Input either path or tensor""")
				if img is not None:
						raise NotImplementedError
				__SCREAMING_SNAKE_CASE            = preprocess(Image.open(lowerCAmelCase__)							,  target_image_size=2_5_6).to(self.device)
				__SCREAMING_SNAKE_CASE            = preprocess_vqgan(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE       ,*__SCREAMING_SNAKE_CASE            = self.vqgan.encode(lowerCAmelCase__)
				return z




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.latent.detach().requires_grad_()
				__SCREAMING_SNAKE_CASE            = base_latent + transform_vector
				if self.quantize:
						__SCREAMING_SNAKE_CASE       ,*__SCREAMING_SNAKE_CASE            = self.vqgan.quantize(lowerCAmelCase__)
				else:
						__SCREAMING_SNAKE_CASE            = trans_latent
				return self.vqgan.decode(lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=None):
				__SCREAMING_SNAKE_CASE            = self.clip_preprocessor(text=lowerCAmelCase__							,  images=lowerCAmelCase__							,  return_tensors="""pt"""							,  padding=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.clip(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = clip_outputs.logits_per_image
				if weights is not None:
						__SCREAMING_SNAKE_CASE            = similarity_logits * weights
				return similarity_logits.sum()




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self._get_clip_similarity(pos_prompts["""prompts"""]							,  lowerCAmelCase__							,  weights=(1 / pos_prompts["""weights"""]))
				if neg_prompts:
						__SCREAMING_SNAKE_CASE            = self._get_clip_similarity(neg_prompts["""prompts"""]							,  lowerCAmelCase__							,  weights=neg_prompts["""weights"""])
				else:
						__SCREAMING_SNAKE_CASE            = torch.tensor([1]							,  device=self.device)
				__SCREAMING_SNAKE_CASE            = -torch.log(lowerCAmelCase__) + torch.log(lowerCAmelCase__)
				return loss




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = torch.randn_like(self.latent							,  requires_grad=lowerCAmelCase__							,  device=self.device)
				__SCREAMING_SNAKE_CASE            = torch.optim.Adam([vector]							,  lr=self.lr)

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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				wandb.init(reinit=lowerCAmelCase__							,  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            = Image.open(lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = image.resize((2_5_6, 2_5_6))
						wandb.log("""Original Image"""							,  wandb.Image(lowerCAmelCase__))




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				if not prompts:
						return []
				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = []
				if isinstance(lowerCAmelCase__							,  lowerCAmelCase__):
						__SCREAMING_SNAKE_CASE            = [prompt.strip() for prompt in prompts.split("""|""")]
				for prompt in prompts:
						if isinstance(lowerCAmelCase__							,  (tuple, list)):
								__SCREAMING_SNAKE_CASE            = prompt[0]
								__SCREAMING_SNAKE_CASE            = float(prompt[1])
						elif ":" in prompt:
								__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = prompt.split(""":""")
								__SCREAMING_SNAKE_CASE            = float(lowerCAmelCase__)
						else:
								__SCREAMING_SNAKE_CASE            = prompt
								__SCREAMING_SNAKE_CASE            = 1.0
						processed_prompts.append(lowerCAmelCase__)
						weights.append(lowerCAmelCase__)
				return {
				    "prompts": processed_prompts,
				    "weights": torch.tensor(lowerCAmelCase__							,  device=self.device),
				}




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=None							,  ):
				if image_path:
						__SCREAMING_SNAKE_CASE            = self._get_latent(lowerCAmelCase__)
				else:
						__SCREAMING_SNAKE_CASE            = torch.randn(self.latent_dim							,  device=self.device)
				if self.log:
						self._init_logging(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)

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

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

				__SCREAMING_SNAKE_CASE            = loop_post_process(lowerCAmelCase__)
				for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)):
						if show_intermediate:
								show_pil(lowerCAmelCase__)
						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(lowerCAmelCase__)})
				if show_final:
						show_pil(lowerCAmelCase__)
				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


lowerCAmelCase					:     int												={
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Any												=[
     '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BigBirdPegasusForCausalLM''',
     '''BigBirdPegasusForConditionalGeneration''',
     '''BigBirdPegasusForQuestionAnswering''',
     '''BigBirdPegasusForSequenceClassification''',
     '''BigBirdPegasusModel''',
     '''BigBirdPegasusPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase					:     Dict												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


