



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    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE = "cpu"		,				SCREAMING_SNAKE_CASE = "openai/clip-vit-large-patch14"      ) ->			None:
  a__												= device
  a__												= CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE      )
  a__												= [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
  a__												= [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
  a__												= torchvision.transforms.Normalize(self.image_mean		,				self.image_std      )
  a__												= torchvision.transforms.Resize(2_2_4      )
  a__												= torchvision.transforms.CenterCrop(2_2_4      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= self.resize(SCREAMING_SNAKE_CASE      )
  a__												= self.center_crop(SCREAMING_SNAKE_CASE      )
  a__												= self.normalize(SCREAMING_SNAKE_CASE      )
  return images



 def __call__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= self.tokenizer(text=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  a__												= self.preprocess_img(SCREAMING_SNAKE_CASE      )
  a__												= {key: value.to(self.device      ) for (key, value) in encoding.items()}
  return encoding





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



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

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

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None      ) ->			Dict:
  if not (path or img):
   raise ValueError('''Input either path or tensor'''      )
  if img is not None:
   raise NotImplementedError
  a__												= preprocess(Image.open(SCREAMING_SNAKE_CASE      )		,				target_image_size=2_5_6      ).to(self.device      )
  a__												= preprocess_vqgan(SCREAMING_SNAKE_CASE      )
  a__							,			*a__												= self.vqgan.encode(SCREAMING_SNAKE_CASE      )
  return z

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= self.latent.detach().requires_grad_()
  a__												= base_latent + transform_vector
  if self.quantize:
   a__							,			*a__												= self.vqgan.quantize(SCREAMING_SNAKE_CASE      )
  else:
   a__												= trans_latent
  return self.vqgan.decode(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None      ) ->			Optional[int]:
  a__												= self.clip_preprocessor(text=SCREAMING_SNAKE_CASE		,				images=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''		,				padding=SCREAMING_SNAKE_CASE      )
  a__												= self.clip(**SCREAMING_SNAKE_CASE      )
  a__												= clip_outputs.logits_per_image
  if weights is not None:
   a__												= similarity_logits * weights
  return similarity_logits.sum()

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= torch.randn_like(self.latent		,				requires_grad=SCREAMING_SNAKE_CASE		,				device=self.device      )
  a__												= torch.optim.Adam([vector]		,				lr=self.lr      )

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Tuple:
  wandb.init(reinit=SCREAMING_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:
   a__												= Image.open(SCREAMING_SNAKE_CASE      )
   a__												= image.resize((2_5_6, 2_5_6)      )
   wandb.log('''Original Image'''		,				wandb.Image(SCREAMING_SNAKE_CASE      )      )

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



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=None		,				) ->			str:
  if image_path:
   a__												= self._get_latent(SCREAMING_SNAKE_CASE      )
  else:
   a__												= torch.randn(self.latent_dim		,				device=self.device      )
  if self.log:
   self._init_logging(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

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

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

  a__												= loop_post_process(SCREAMING_SNAKE_CASE      )
  for iter, transformed_img in enumerate(self._optimize_CLIP(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      ):
   if show_intermediate:
    show_pil(SCREAMING_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(SCREAMING_SNAKE_CASE      )}      )
  if show_final:
   show_pil(SCREAMING_SNAKE_CASE      )
  if save_final:
   transformed_img.save(os.path.join(self.save_path		,				f"iter_{iter:03d}_final.png"      )      )










import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


a_      :      int    =     '\\n    Text data.\n    Second line of data.'

a_      :      Optional[Any]    =     'file'






@pytest.fixture(scope='''session'''       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / (FILE_PATH + '''.zstd''')
 a__												= bytes(__UpperCAmelCase							,  '''utf-8'''       )
 with zstd.open(__UpperCAmelCase							,  '''wb'''       ) as f:
  f.write(__UpperCAmelCase       )
 return path






@pytest.fixture
def     __a					(		__UpperCAmelCase       ):
 with open(os.path.join(tmpfs.local_root_dir							,  __UpperCAmelCase       )							,  '''w'''       ) as f:
  f.write(__UpperCAmelCase       )
 return FILE_PATH






@pytest.mark.parametrize('''compression_format'''							,  ['''gzip''', '''xz''', '''zstd''']       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
 a__												= input_paths[compression_format]
 a__												= tmp_path / '''cache'''
 a__												= DownloadConfig(cache_dir=__UpperCAmelCase							,  extract_compressed_file=__UpperCAmelCase       )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''							,  [True, False]       )
@pytest.mark.parametrize('''default_cache_dir'''							,  [True, False]       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= '''custom_cache'''
 a__												= '''custom_extracted_dir'''
 a__												= tmp_path / '''custom_extracted_path'''
 if default_extracted:
  a__												= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
 else:
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''							,  __UpperCAmelCase       )
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''							,  str(__UpperCAmelCase       )       )
  a__												= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

 a__												= xz_file
 a__												= (
     DownloadConfig(extract_compressed_file=__UpperCAmelCase       )
     if default_cache_dir
     else DownloadConfig(cache_dir=tmp_path / custom_cache_dir							,  extract_compressed_file=__UpperCAmelCase       )
 )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 assert Path(__UpperCAmelCase       ).parent.parts[-2:] == expected






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(Path(__UpperCAmelCase       ).resolve()       )
 assert cached_path(__UpperCAmelCase       ) == text_file
 # relative path
 a__												= str(Path(__UpperCAmelCase       ).resolve().relative_to(Path(os.getcwd()       )       )       )
 assert cached_path(__UpperCAmelCase       ) == text_file






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(tmp_path.resolve() / '''__missing_file__.txt'''       )
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )
 # relative path
 a__												= '''./__missing_file__.txt'''
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase       ):
 a__												= get_from_cache(f"tmp://{tmpfs_file}"       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		):
 with pytest.raises(__UpperCAmelCase       ):
  cached_path('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  http_get('''https://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  http_head('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  ftp_get('''ftp://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  ftp_head('''ftp://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_get('''s3://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_head('''s3://huggingface.co'''       )





