
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowercase  :					Dict						    =							sys.version_info >= (3, 10)







def 						_snake_case(  SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None				) ->    List[Any]:
       return field(default_factory=lambda: default					,	metadata=_lowerCamelCase				)

@dataclass
class __snake_case		:
    _a		:						Optional[int]=				42
    _a		:						Dict=				42
    _a		:						Tuple=				42
    _a		:						int=				42






@dataclass
class __snake_case		:
    _a		:						Dict=				42
    _a		:						Any=				field(default="toto"					,	metadata={"help": "help message"}     )






@dataclass
class __snake_case		:
    _a		:						Dict=				False
    _a		:						List[Any]=				True
    _a		:						Dict=				None






class __snake_case		(      lowerCAmelCase     ):
    _a		:						Tuple=				"titi"
    _a		:						Optional[int]=				"toto"






class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"titi"
    _a		:						Union[str, Any]=				"toto"
    _a		:						List[str]=				42






@dataclass
class __snake_case		:
    _a		:						Optional[int]=				"toto"
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      BasicEnum(self.foo      )






@dataclass
class __snake_case		:
    _a		:						List[Any]=				"toto"
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      MixedTypeEnum(self.foo      )






@dataclass
class __snake_case		:
    _a		:						Dict=				None
    _a		:						int=				field(default=lowerCAmelCase					,	metadata={"help": "help message"}     )
    _a		:						List[str]=				None
    _a		:						Tuple=				list_field(default=[]     )
    _a		:						Tuple=				list_field(default=[]     )






@dataclass
class __snake_case		:
    _a		:						List[Any]=				list_field(default=[]     )
    _a		:						List[Any]=				list_field(default=[1, 2, 3]     )
    _a		:						Dict=				list_field(default=["Hallo", "Bonjour", "Hello"]     )
    _a		:						Dict=				list_field(default=[0.1, 0.2, 0.3]     )






@dataclass
class __snake_case		:
    _a		:						List[str]=				field()
    _a		:						int=				field()
    _a		:						int=				field()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      BasicEnum(self.required_enum      )






@dataclass
class __snake_case		:
    _a		:						Dict=				42
    _a		:						Tuple=				field()
    _a		:						List[Any]=				None
    _a		:						Dict=				field(default="toto"					,	metadata={"help": "help message"}     )
    _a		:						Optional[Any]=				list_field(default=["Hallo", "Bonjour", "Hello"]     )


if is_python_no_less_than_3_10:

  @dataclass
  class __snake_case		:
      _a		:						Optional[int]=				False
      _a		:						int=				True
      _a		:						Optional[Any]=				None






  @dataclass
  class __snake_case		:
      _a		:						Optional[Any]=				None
      _a		:						int=				field(default=lowerCAmelCase					,	metadata={"help": "help message"}     )
      _a		:						Dict=				None
      _a		:						Union[str, Any]=				list_field(default=[]     )
      _a		:						Optional[int]=				list_field(default=[]     )




class __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           self.assertEqual(len(a._actions      )		,len(b._actions      )      )
           for x, y in zip(a._actions		,b._actions      ):
                  lowercase							:     int       	=      {k: v for k, v in vars(__a      ).items() if k != "container"}
                  lowercase							:     Dict       	=      {k: v for k, v in vars(__a      ).items() if k != "container"}

                  # Choices with mixed type have custom function as "type"
                  # So we need to compare results directly for equality
                  if xx.get("""choices"""		,__a      ) and yy.get("""choices"""		,__a      ):
                         for expected_choice in yy["choices"] + xx["choices"]:
                                self.assertEqual(xx["""type"""](__a      )		,yy["""type"""](__a      )      )
                         del xx["type"], yy["type"]

                  self.assertEqual(__a		,__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      HfArgumentParser(__a      )

           lowercase							:     List[Any]       	=      argparse.ArgumentParser()
           expected.add_argument("""--foo"""		,type=__a		,required=__a      )
           expected.add_argument("""--bar"""		,type=__a		,required=__a      )
           expected.add_argument("""--baz"""		,type=__a		,required=__a      )
           expected.add_argument("""--flag"""		,type=__a		,default=__a		,const=__a		,nargs="""?"""      )
           self.argparsersEqual(__a		,__a      )

           lowercase							:     int       	=      ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
           (lowercase    )							:     str       	=      parser.parse_args_into_dataclasses(__a		,look_for_args_file=__a      )
           self.assertFalse(example.flag      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      HfArgumentParser(__a      )

           lowercase							:     Any       	=      argparse.ArgumentParser()
           expected.add_argument("""--foo"""		,default=42		,type=__a      )
           expected.add_argument("""--baz"""		,default="""toto"""		,type=__a		,help="""help message"""      )
           self.argparsersEqual(__a		,__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      argparse.ArgumentParser()
           expected.add_argument("""--foo"""		,type=__a		,default=__a		,const=__a		,nargs="""?"""      )
           expected.add_argument("""--baz"""		,type=__a		,default=__a		,const=__a		,nargs="""?"""      )
           # A boolean no_* argument always has to come after its "default: True" regular counter-part
           # and its default must be set to False
           expected.add_argument("""--no_baz"""		,action="""store_false"""		,default=__a		,dest="""baz"""      )
           expected.add_argument("""--opt"""		,type=__a		,default=__a      )

           lowercase							:     List[str]       	=      [WithDefaultBoolExample]
           if is_python_no_less_than_3_10:
                  dataclass_types.append(__a      )

           for dataclass_type in dataclass_types:
                  lowercase							:     Any       	=      HfArgumentParser(__a      )
                  self.argparsersEqual(__a		,__a      )

                  lowercase							:     int       	=      parser.parse_args([]      )
                  self.assertEqual(__a		,Namespace(foo=__a		,baz=__a		,opt=__a      )      )

                  lowercase							:     int       	=      parser.parse_args(["""--foo""", """--no_baz"""]      )
                  self.assertEqual(__a		,Namespace(foo=__a		,baz=__a		,opt=__a      )      )

                  lowercase							:     Optional[int]       	=      parser.parse_args(["""--foo""", """--baz"""]      )
                  self.assertEqual(__a		,Namespace(foo=__a		,baz=__a		,opt=__a      )      )

                  lowercase							:     Dict       	=      parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""]      )
                  self.assertEqual(__a		,Namespace(foo=__a		,baz=__a		,opt=__a      )      )

                  lowercase							:     str       	=      parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""]      )
                  self.assertEqual(__a		,Namespace(foo=__a		,baz=__a		,opt=__a      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      HfArgumentParser(__a      )

           lowercase							:     str       	=      argparse.ArgumentParser()
           expected.add_argument(
               """--foo"""		,default="""toto"""		,choices=["""titi""", """toto""", 42]		,type=make_choice_type_function(["""titi""", """toto""", 42]      )		,)
           self.argparsersEqual(__a		,__a      )

           lowercase							:     str       	=      parser.parse_args([]      )
           self.assertEqual(args.foo		,"""toto"""      )
           lowercase							:     Any       	=      parser.parse_args_into_dataclasses([]      )[0]
           self.assertEqual(enum_ex.foo		,MixedTypeEnum.toto      )

           lowercase							:     Optional[int]       	=      parser.parse_args(["""--foo""", """titi"""]      )
           self.assertEqual(args.foo		,"""titi"""      )
           lowercase							:     List[Any]       	=      parser.parse_args_into_dataclasses(["""--foo""", """titi"""]      )[0]
           self.assertEqual(enum_ex.foo		,MixedTypeEnum.titi      )

           lowercase							:     Union[str, Any]       	=      parser.parse_args(["""--foo""", """42"""]      )
           self.assertEqual(args.foo		,42      )
           lowercase							:     Optional[Any]       	=      parser.parse_args_into_dataclasses(["""--foo""", """42"""]      )[0]
           self.assertEqual(enum_ex.foo		,MixedTypeEnum.fourtytwo      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           @dataclass
           class __snake_case		:
               _a		:						Optional[Any]=				"toto"






           lowercase							:     Tuple       	=      HfArgumentParser(__a      )

           lowercase							:     Union[str, Any]       	=      argparse.ArgumentParser()
           expected.add_argument(
               """--foo"""		,default="""toto"""		,choices=("""titi""", """toto""", 42)		,type=make_choice_type_function(["""titi""", """toto""", 42]      )		,)
           self.argparsersEqual(__a		,__a      )

           lowercase							:     Any       	=      parser.parse_args([]      )
           self.assertEqual(args.foo		,"""toto"""      )

           lowercase							:     Optional[Any]       	=      parser.parse_args(["""--foo""", """titi"""]      )
           self.assertEqual(args.foo		,"""titi"""      )

           lowercase							:     Optional[int]       	=      parser.parse_args(["""--foo""", """42"""]      )
           self.assertEqual(args.foo		,42      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      HfArgumentParser(__a      )

           lowercase							:     Any       	=      argparse.ArgumentParser()
           expected.add_argument("""--foo_int"""		,nargs="""+"""		,default=[]		,type=__a      )
           expected.add_argument("""--bar_int"""		,nargs="""+"""		,default=[1, 2, 3]		,type=__a      )
           expected.add_argument("""--foo_str"""		,nargs="""+"""		,default=["""Hallo""", """Bonjour""", """Hello"""]		,type=__a      )
           expected.add_argument("""--foo_float"""		,nargs="""+"""		,default=[0.1, 0.2, 0.3]		,type=__a      )

           self.argparsersEqual(__a		,__a      )

           lowercase							:     Optional[Any]       	=      parser.parse_args([]      )
           self.assertEqual(
               __a		,Namespace(foo_int=[]		,bar_int=[1, 2, 3]		,foo_str=["""Hallo""", """Bonjour""", """Hello"""]		,foo_float=[0.1, 0.2, 0.3]      )		,)

           lowercase							:     Optional[Any]       	=      parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split()      )
           self.assertEqual(__a		,Namespace(foo_int=[1]		,bar_int=[2, 3]		,foo_str=["""a""", """b""", """c"""]		,foo_float=[0.1, 0.7]      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      argparse.ArgumentParser()
           expected.add_argument("""--foo"""		,default=__a		,type=__a      )
           expected.add_argument("""--bar"""		,default=__a		,type=__a		,help="""help message"""      )
           expected.add_argument("""--baz"""		,default=__a		,type=__a      )
           expected.add_argument("""--ces"""		,nargs="""+"""		,default=[]		,type=__a      )
           expected.add_argument("""--des"""		,nargs="""+"""		,default=[]		,type=__a      )

           lowercase							:     Optional[Any]       	=      [OptionalExample]
           if is_python_no_less_than_3_10:
                  dataclass_types.append(__a      )

           for dataclass_type in dataclass_types:
                  lowercase							:     Tuple       	=      HfArgumentParser(__a      )

                  self.argparsersEqual(__a		,__a      )

                  lowercase							:     int       	=      parser.parse_args([]      )
                  self.assertEqual(__a		,Namespace(foo=__a		,bar=__a		,baz=__a		,ces=[]		,des=[]      )      )

                  lowercase							:     List[Any]       	=      parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split()      )
                  self.assertEqual(__a		,Namespace(foo=12		,bar=3.14		,baz="""42"""		,ces=["""a""", """b""", """c"""]		,des=[1, 2, 3]      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      HfArgumentParser(__a      )

           lowercase							:     Any       	=      argparse.ArgumentParser()
           expected.add_argument("""--required_list"""		,nargs="""+"""		,type=__a		,required=__a      )
           expected.add_argument("""--required_str"""		,type=__a		,required=__a      )
           expected.add_argument(
               """--required_enum"""		,type=make_choice_type_function(["""titi""", """toto"""]      )		,choices=["""titi""", """toto"""]		,required=__a		,)
           self.argparsersEqual(__a		,__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      HfArgumentParser(__a      )

           lowercase							:     Optional[int]       	=      argparse.ArgumentParser()
           expected.add_argument("""--foo"""		,type=__a		,required=__a      )
           expected.add_argument(
               """--required_enum"""		,type=make_choice_type_function(["""titi""", """toto"""]      )		,choices=["""titi""", """toto"""]		,required=__a		,)
           expected.add_argument("""--opt"""		,type=__a		,default=__a      )
           expected.add_argument("""--baz"""		,default="""toto"""		,type=__a		,help="""help message"""      )
           expected.add_argument("""--foo_str"""		,nargs="""+"""		,default=["""Hallo""", """Bonjour""", """Hello"""]		,type=__a      )
           self.argparsersEqual(__a		,__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      HfArgumentParser(__a      )

           lowercase							:     Tuple       	=      {
               "foo": 12,
               "bar": 3.14,
               "baz": "42",
               "flag": True,
           }

           lowercase							:     List[str]       	=      parser.parse_dict(__a      )[0]
           lowercase							:     Optional[int]       	=      BasicExample(**__a      )
           self.assertEqual(__a		,__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      HfArgumentParser(__a      )

           lowercase							:     Optional[int]       	=      {
               "foo": 12,
               "bar": 3.14,
               "baz": "42",
               "flag": True,
               "extra": 42,
           }

           self.assertRaises(__a		,parser.parse_dict		,__a		,allow_extra_keys=__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      HfArgumentParser(__a      )

           lowercase							:     Tuple       	=      {
               "foo": 12,
               "bar": 3.14,
               "baz": "42",
               "flag": True,
           }
           with tempfile.TemporaryDirectory() as tmp_dir:
                  lowercase							:     Dict       	=      os.path.join(__a		,"""temp_json"""      )
                  os.mkdir(__a      )
                  with open(temp_local_path + """.json"""		,"""w+"""      ) as f:
                         json.dump(__a		,__a      )
                  lowercase							:     Optional[int]       	=      parser.parse_yaml_file(Path(temp_local_path + """.json"""      )      )[0]

           lowercase							:     str       	=      BasicExample(**__a      )
           self.assertEqual(__a		,__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      HfArgumentParser(__a      )

           lowercase							:     int       	=      {
               "foo": 12,
               "bar": 3.14,
               "baz": "42",
               "flag": True,
           }
           with tempfile.TemporaryDirectory() as tmp_dir:
                  lowercase							:     Any       	=      os.path.join(__a		,"""temp_yaml"""      )
                  os.mkdir(__a      )
                  with open(temp_local_path + """.yaml"""		,"""w+"""      ) as f:
                         yaml.dump(__a		,__a      )
                  lowercase							:     str       	=      parser.parse_yaml_file(Path(temp_local_path + """.yaml"""      )      )[0]
           lowercase							:     Optional[int]       	=      BasicExample(**__a      )
           self.assertEqual(__a		,__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      HfArgumentParser(__a      )
           self.assertIsNotNone(__a      )





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_    :







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

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.resize(__a)
														_lowerCAmelCase     :							List[str]    =    self.center_crop(__a)
														_lowerCAmelCase     :							Optional[Any]    =    self.normalize(__a)
														return images







							def __call__(   self,			__a=None,			__a=None,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.tokenizer(text=__a,			**__a)
														_lowerCAmelCase     :							List[str]    =    self.preprocess_img(__a)
														_lowerCAmelCase     :							Tuple    =    {key: value.to(self.device) for (key, value) in encoding.items()}
														return encoding



class   UpperCAmelCase_    (       nn.Module):







							def __init__(   self,			__a=10,			__a=0.01,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=False,			__a=True,			__a="image",			__a=True,			__a=False,			__a=False,			__a=False,			):

														'''simple docstring'''

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

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







							def    snake_case__      (   self,			__a=None,			__a=None,			__a=5,			__a=True):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    []
														if output_path is None:
																					_lowerCAmelCase     :							List[Any]    =    "./animation.gif"
														if input_path is None:
																					_lowerCAmelCase     :							str    =    self.save_path
														_lowerCAmelCase     :							str    =    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?)")
														_lowerCAmelCase     :							Optional[int]    =    total_duration / len(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    [frame_duration] * len(__a)
														if extend_frames:
																					_lowerCAmelCase     :							Any    =    1.5
																					_lowerCAmelCase     :							List[str]    =    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    snake_case__      (   self,			__a=None,			__a=None):

														'''simple docstring'''

														if not (path or img):
																					raise ValueError("Input either path or tensor")
														if img is not None:
																					raise NotImplementedError
														_lowerCAmelCase     :							Dict    =    preprocess(Image.open(__a),			target_image_size=256).to(self.device)
														_lowerCAmelCase     :							Dict    =    preprocess_vqgan(__a)
														_lowerCAmelCase   ,    *_lowerCAmelCase     :							str    =    self.vqgan.encode(__a)
														return z







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.latent.detach().requires_grad_()
														_lowerCAmelCase     :							Dict    =    base_latent + transform_vector
														if self.quantize:
																					_lowerCAmelCase   ,    *_lowerCAmelCase     :							List[Any]    =    self.vqgan.quantize(__a)
														else:
																					_lowerCAmelCase     :							Any    =    trans_latent
														return self.vqgan.decode(__a)







							def    snake_case__      (   self,			__a,			__a,			__a=None):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    torch.randn_like(self.latent,			requires_grad=__a,			device=self.device)
														_lowerCAmelCase     :							Optional[int]    =    torch.optim.Adam([vector],			lr=self.lr)

														for i in range(self.iterations):
																					optim.zero_grad()
																					_lowerCAmelCase     :							Any    =    self._add_vector(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    loop_post_process(__a)
																					_lowerCAmelCase     :							Optional[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    snake_case__      (   self,			__a,			__a,			__a):

														'''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:
																					_lowerCAmelCase     :							str    =    Image.open(__a)
																					_lowerCAmelCase     :							int    =    image.resize((256, 256))
																					wandb.log("Original Image",			wandb.Image(__a))







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a=None,			__a=None,			__a=True,			__a=False,			__a=True,			__a=True,			__a=None,			):

														'''simple docstring'''

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

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

														_lowerCAmelCase     :							int    =    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"))
