




"""simple docstring"""


import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)






class       lowercase(  nn.Module				):







 '''simple docstring'''





 def __init__(       self:	str	):




   '''simple docstring'''



   super().__init__()
   _snake_case							:	Dict			=						nn.Linear(3,   4	)
   _snake_case							:	int			=						nn.BatchNormad(4	)
   _snake_case							:	Tuple			=						nn.Linear(4,   5	)


 def 				UpperCamelCase_     (       self:	Dict,   a_:	int	):




   '''simple docstring'''



   return self.lineara(self.batchnorm(self.lineara(a_	)	)	)







class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						ModelForTest()
   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,   model.state_dict()	)
     _snake_case							:	str			=						os.path.join(a_,   """index.json"""	)
     self.assertTrue(os.path.isfile(a_	)	)
     # TODO: add tests on what is inside the index

     for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
       _snake_case							:	int			=						os.path.join(a_,   f"{key}.dat"	)
       self.assertTrue(os.path.isfile(a_	)	)
       # TODO: add tests on the fact weights are properly loaded


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						[torch.floataa, torch.floataa, torch.bfloataa]

   for dtype in dtypes:
     _snake_case							:	Any			=						torch.randn(2,   3,   dtype=a_	)
     with TemporaryDirectory() as tmp_dir:
       _snake_case							:	List[Any]			=						offload_weight(a_,   """weight""",   a_,   {}	)
       _snake_case							:	str			=						os.path.join(a_,   """weight.dat"""	)
       self.assertTrue(os.path.isfile(a_	)	)
       self.assertDictEqual(a_,   {"""weight""": {"""shape""": [2, 3], """dtype""": str(a_	).split("""."""	)[1]}}	)

       _snake_case							:	List[str]			=						load_offloaded_weight(a_,   index["""weight"""]	)
       self.assertTrue(torch.equal(a_,   a_	)	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						ModelForTest()
   _snake_case							:	int			=						model.state_dict()
   _snake_case							:	str			=						{k: v for k, v in state_dict.items() if """linear2""" not in k}
   _snake_case							:	Optional[int]			=						{k: v for k, v in state_dict.items() if """linear2""" in k}

   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,   a_	)
     _snake_case							:	Tuple			=						OffloadedWeightsLoader(state_dict=a_,   save_folder=a_	)

     # Every key is there with the right value
     self.assertEqual(sorted(a_	),   sorted(state_dict.keys()	)	)
     for key, param in state_dict.items():
       self.assertTrue(torch.allclose(a_,   weight_map[key]	)	)

   _snake_case							:	Dict			=						{k: v for k, v in state_dict.items() if """weight""" in k}
   _snake_case							:	str			=						{k: v for k, v in state_dict.items() if """weight""" not in k}

   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,   a_	)
     _snake_case							:	Optional[int]			=						OffloadedWeightsLoader(state_dict=a_,   save_folder=a_	)

     # Every key is there with the right value
     self.assertEqual(sorted(a_	),   sorted(state_dict.keys()	)	)
     for key, param in state_dict.items():
       self.assertTrue(torch.allclose(a_,   weight_map[key]	)	)

   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,   a_	)
     # Duplicates are removed
     _snake_case							:	Union[str, Any]			=						OffloadedWeightsLoader(state_dict=a_,   save_folder=a_	)

     # Every key is there with the right value
     self.assertEqual(sorted(a_	),   sorted(state_dict.keys()	)	)
     for key, param in state_dict.items():
       self.assertTrue(torch.allclose(a_,   weight_map[key]	)	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						{"""a.1""": 0, """a.10""": 1, """a.2""": 2}
   _snake_case							:	Optional[Any]			=						extract_submodules_state_dict(a_,   ["""a.1""", """a.2"""]	)
   self.assertDictEqual(a_,   {"""a.1""": 0, """a.2""": 2}	)

   _snake_case							:	List[Any]			=						{"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
   _snake_case							:	Tuple			=						extract_submodules_state_dict(a_,   ["""a.1""", """a.2"""]	)
   self.assertDictEqual(a_,   {"""a.1.a""": 0, """a.2.a""": 2}	)








"""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       lowercase:







 '''simple docstring'''





 def __init__(       self:	List[Any],   a_:	str = "cpu",   a_:	str = "openai/clip-vit-large-patch14"	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						device
   _snake_case							:	str			=						CLIPTokenizerFast.from_pretrained(a_	)
   _snake_case							:	Union[str, Any]			=						[0.48_145_466, 0.4_578_275, 0.40_821_073]
   _snake_case							:	Optional[int]			=						[0.26_862_954, 0.26_130_258, 0.27_577_711]
   _snake_case							:	str			=						torchvision.transforms.Normalize(self.image_mean,   self.image_std	)
   _snake_case							:	Optional[int]			=						torchvision.transforms.Resize(224	)
   _snake_case							:	str			=						torchvision.transforms.CenterCrop(224	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	str	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.resize(a_	)
   _snake_case							:	List[Any]			=						self.center_crop(a_	)
   _snake_case							:	Optional[Any]			=						self.normalize(a_	)
   return images


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




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.tokenizer(text=a_,   **a_	)
   _snake_case							:	Any			=						self.preprocess_img(a_	)
   _snake_case							:	Union[str, Any]			=						{key: value.to(self.device	) for (key, value) in encoding.items()}
   return encoding







class       lowercase(  nn.Module				):







 '''simple docstring'''





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




   '''simple docstring'''



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

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


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	str=None,   a_:	Dict=None,   a_:	Dict=5,   a_:	Dict=True	):




   '''simple docstring'''



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




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.latent.detach().requires_grad_()
   _snake_case							:	Tuple			=						base_latent + transform_vector
   if self.quantize:
     _snake_case	,      *_snake_case							:	Any			=						self.vqgan.quantize(a_	)
   else:
     _snake_case							:	List[Any]			=						trans_latent
   return self.vqgan.decode(a_	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Any,   a_:	Union[str, Any],   a_:	Dict=None	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.clip_preprocessor(text=a_,   images=a_,   return_tensors="""pt""",   padding=a_	)
   _snake_case							:	Any			=						self.clip(**a_	)
   _snake_case							:	str			=						clip_outputs.logits_per_image
   if weights is not None:
     _snake_case							:	Any			=						similarity_logits * weights
   return similarity_logits.sum()


 def 				UpperCamelCase_     (       self:	Any,   a_:	Any,   a_:	List[str],   a_:	Dict	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Tuple,   a_:	Union[str, Any],   a_:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						torch.randn_like(self.latent,   requires_grad=a_,   device=self.device	)
   _snake_case							:	Dict			=						torch.optim.Adam([vector],   lr=self.lr	)

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




   '''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:
     _snake_case							:	Any			=						Image.open(a_	)
     _snake_case							:	str			=						image.resize((256, 256)	)
     wandb.log("""Original Image""",   wandb.Image(a_	)	)


 def 				UpperCamelCase_     (       self:	str,   a_:	List[Any]	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Dict,   a_:	List[Any],   a_:	List[Any]=None,   a_:	Optional[Any]=None,   a_:	Optional[Any]=True,   a_:	Dict=False,   a_:	Optional[Any]=True,   a_:	Optional[Any]=True,   a_:	Any=None,   ):




   '''simple docstring'''



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

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

   _snake_case							:	List[Any]			=						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"	)	)


