





'''simple docstring'''






import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
     import jax.numpy as jnp


class   _lowercase						(							_UpperCAmelCase				):


   def __get__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):
       # See docs.python.org/3/howto/descriptor.html#properties
       if obj is None:
           return self
       if self.fget is None:
           raise AttributeError('''unreadable attribute'''					)
       A       :       int							=						"""__cached_""" + self.fget.__name__
       A       :       str							=						getattr(lowercase__    ,   lowercase__    ,   lowercase__					)
       if cached is None:
           A       :       Any							=						self.fget(lowercase__					)
           setattr(lowercase__    ,   lowercase__    ,   lowercase__					)
       return cached







def 							_lowerCamelCase(  UpperCamelCase__    : Dict		)		->  int:
    A       :       List[Any]							=						val.lower()
    if val in {"y", "yes", "t", "true", "on", "1"}:
        return 1
    if val in {"n", "no", "f", "false", "off", "0"}:
        return 0
    raise ValueError(F'''invalid truth value {val!r}'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]		)		->  str:





    if is_torch_fx_proxy(SCREAMING_SNAKE_CASE__		):
        return True
    if is_torch_available():
        import torch

        if isinstance(SCREAMING_SNAKE_CASE__    ,       torch.Tensor		):
            return True
    if is_tf_available():
        import tensorflow as tf

        if isinstance(SCREAMING_SNAKE_CASE__    ,       tf.Tensor		):
            return True

    if is_flax_available():
        import jax.numpy as jnp
        from jax.core import Tracer

        if isinstance(SCREAMING_SNAKE_CASE__    ,       (jnp.ndarray, Tracer)		):
            return True

    return isinstance(SCREAMING_SNAKE_CASE__    ,       np.ndarray		)
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]		)		->  Optional[int]:
    return isinstance(SCREAMING_SNAKE_CASE__    ,       np.ndarray		)
def 							_lowerCamelCase(  UpperCamelCase__    : Any		)		->  str:
    return _is_numpy(SCREAMING_SNAKE_CASE__		)
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  Union[str, Any]:
    import torch

    return isinstance(SCREAMING_SNAKE_CASE__    ,       torch.Tensor		)
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]		)		->  str:
    return False if not is_torch_available() else _is_torch(SCREAMING_SNAKE_CASE__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  Optional[int]:
    import torch

    return isinstance(SCREAMING_SNAKE_CASE__    ,       torch.device		)
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]		)		->  Union[str, Any]:
    return False if not is_torch_available() else _is_torch_device(SCREAMING_SNAKE_CASE__		)
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]		)		->  List[str]:
    import torch

    if isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		):
        if hasattr(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		):
            A       :       List[Any]							=						getattr(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		)
        else:
            return False
    return isinstance(SCREAMING_SNAKE_CASE__    ,       torch.dtype		)
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  Any:
    return False if not is_torch_available() else _is_torch_dtype(SCREAMING_SNAKE_CASE__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]		)		->  Tuple:
    import tensorflow as tf

    return isinstance(SCREAMING_SNAKE_CASE__    ,       tf.Tensor		)
def 							_lowerCamelCase(  UpperCamelCase__    : Any		)		->  str:
    return False if not is_tf_available() else _is_tensorflow(SCREAMING_SNAKE_CASE__		)
def 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  Optional[int]:
    import tensorflow as tf

    # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
    if hasattr(SCREAMING_SNAKE_CASE__    ,       '''is_symbolic_tensor'''		):
        return tf.is_symbolic_tensor(SCREAMING_SNAKE_CASE__		)
    return type(SCREAMING_SNAKE_CASE__		) == tf.Tensor
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]		)		->  Optional[int]:
    return False if not is_tf_available() else _is_tf_symbolic_tensor(SCREAMING_SNAKE_CASE__		)
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  Dict:
    import jax.numpy as jnp  # noqa: F811

    return isinstance(SCREAMING_SNAKE_CASE__    ,       jnp.ndarray		)
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]		)		->  Dict:
    return False if not is_flax_available() else _is_jax(SCREAMING_SNAKE_CASE__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple		)		->  Any:





    if isinstance(SCREAMING_SNAKE_CASE__    ,       (dict, UserDict)		):
        return {k: to_py_obj(SCREAMING_SNAKE_CASE__		) for k, v in obj.items()}
    elif isinstance(SCREAMING_SNAKE_CASE__    ,       (list, tuple)		):
        return [to_py_obj(SCREAMING_SNAKE_CASE__		) for o in obj]
    elif is_tf_tensor(SCREAMING_SNAKE_CASE__		):
        return obj.numpy().tolist()
    elif is_torch_tensor(SCREAMING_SNAKE_CASE__		):
        return obj.detach().cpu().tolist()
    elif is_jax_tensor(SCREAMING_SNAKE_CASE__		):
        return np.asarray(SCREAMING_SNAKE_CASE__		).tolist()
    elif isinstance(SCREAMING_SNAKE_CASE__    ,       (np.ndarray, np.number)		):  # tolist also works on 0d np arrays
        return obj.tolist()
    else:
        return obj
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple		)		->  List[Any]:





    if isinstance(SCREAMING_SNAKE_CASE__    ,       (dict, UserDict)		):
        return {k: to_numpy(SCREAMING_SNAKE_CASE__		) for k, v in obj.items()}
    elif isinstance(SCREAMING_SNAKE_CASE__    ,       (list, tuple)		):
        return np.array(SCREAMING_SNAKE_CASE__		)
    elif is_tf_tensor(SCREAMING_SNAKE_CASE__		):
        return obj.numpy()
    elif is_torch_tensor(SCREAMING_SNAKE_CASE__		):
        return obj.detach().cpu().numpy()
    elif is_jax_tensor(SCREAMING_SNAKE_CASE__		):
        return np.asarray(SCREAMING_SNAKE_CASE__		)
    else:
        return obj


class   _lowercase						(							_UpperCAmelCase				):


   def 					snake_case				( self					):
       A       :       str							=						fields(self					)

       # Safety and consistency checks
       if not len(lowercase__					):
           raise ValueError(f'''{self.__class__.__name__} has no fields.'''					)
       if not all(field.default is None for field in class_fields[1:]					):
           raise ValueError(f'''{self.__class__.__name__} should not have more than one required field.'''					)

       A       :       Union[str, Any]							=						getattr(self    ,   class_fields[0].name					)
       A       :       Any							=						all(getattr(self    ,   field.name					) is None for field in class_fields[1:]					)

       if other_fields_are_none and not is_tensor(lowercase__					):
           if isinstance(lowercase__    ,   lowercase__					):
               A       :       Optional[Any]							=						first_field.items()
               A       :       int							=						True
           else:
               try:
                   A       :       Any							=						iter(lowercase__					)
                   A       :       str							=						True
               except TypeError:
                   A       :       List[Any]							=						False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
           if first_field_iterator:
               for idx, element in enumerate(lowercase__					):
                   if (
                       not isinstance(lowercase__    ,   (list, tuple)					)
                       or not len(lowercase__					) == 2
                       or not isinstance(element[0]    ,   lowercase__					)
                   ):
                       if idx == 0:
                           # If we do not have an iterator of key/values, set it as attribute
                           A       :       Dict							=						first_field
                       else:
                           # If we have a mixed iterator, raise an error
                           raise ValueError(
                               f'''Cannot set key/value for {element}. It needs to be a tuple (key, value).'''					)
                       break
                   setattr(self    ,   element[0]    ,   element[1]					)
                   if element[1] is not None:
                       A       :       Union[str, Any]							=						element[1]
           elif first_field is not None:
               A       :       Tuple							=						first_field
       else:
           for field in class_fields:
               A       :       Optional[int]							=						getattr(self    ,   field.name					)
               if v is not None:
                   A       :       Optional[int]							=						v


   def __delitem__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''					)


   def __getitem__( self    ,   _UpperCAmelCase					):
       if isinstance(lowercase__    ,   lowercase__					):
           A       :       Union[str, Any]							=						dict(self.items()					)
           return inner_dict[k]
       else:
           return self.to_tuple()[k]


   def __setattr__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       if name in self.keys() and value is not None:
           # Don't call self.__setitem__ to avoid recursion errors
           super().__setitem__(lowercase__    ,   lowercase__					)
       super().__setattr__(lowercase__    ,   lowercase__					)


   def __setitem__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       # Will raise a KeyException if needed
       super().__setitem__(lowercase__    ,   lowercase__					)
       # Don't call self.__setattr__ to avoid recursion errors
       super().__setattr__(lowercase__    ,   lowercase__					)


   def 					snake_case				( self					):
       return tuple(self[k] for k in self.keys()					)







class   _lowercase						(							_UpperCAmelCase		,					_UpperCAmelCase				):


   @classmethod
   def 					snake_case				( cls    ,   _UpperCAmelCase					):
       raise ValueError(
           f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()					)}'''					)







class   _lowercase						(							_UpperCAmelCase				):
   _UpperCamelCase       		=   """longest"""
   _UpperCamelCase       		=   """max_length"""
   _UpperCamelCase       		=   """do_not_pad"""







class   _lowercase						(							_UpperCAmelCase				):
   _UpperCamelCase       		=   """pt"""
   _UpperCamelCase       		=   """tf"""
   _UpperCamelCase       		=   """np"""
   _UpperCamelCase       		=   """jax"""







class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase					):
       A       :       Any							=						context_managers
       A       :       int							=						ExitStack()


   def __enter__( self					):
       for context_manager in self.context_managers:
           self.stack.enter_context(lowercase__					)


   def __exit__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       self.stack.__exit__(*lowercase__    ,   **lowercase__					)







def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]		)		->  Tuple:
    A       :       Dict							=						infer_framework(SCREAMING_SNAKE_CASE__		)
    if framework == "tf":
        A       :       Dict							=						inspect.signature(model_class.call		)  # TensorFlow models
    elif framework == "pt":
        A       :       Union[str, Any]							=						inspect.signature(model_class.forward		)  # PyTorch models
    else:
        A       :       List[str]							=						inspect.signature(model_class.__call__		)  # Flax models

    for p in signature.parameters:
        if p == "return_loss" and signature.parameters[p].default is True:
            return True

    return False
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]		)		->  List[Any]:
    A       :       Optional[int]							=						model_class.__name__
    A       :       Tuple							=						infer_framework(SCREAMING_SNAKE_CASE__		)
    if framework == "tf":
        A       :       int							=						inspect.signature(model_class.call		)  # TensorFlow models
    elif framework == "pt":
        A       :       List[str]							=						inspect.signature(model_class.forward		)  # PyTorch models
    else:
        A       :       List[Any]							=						inspect.signature(model_class.__call__		)  # Flax models

    if "QuestionAnswering" in model_name:
        return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
    else:
        return [p for p in signature.parameters if "label" in p]
def 							_lowerCamelCase(  UpperCamelCase__    : MutableMapping    ,       UpperCamelCase__    : str = ""    ,       UpperCamelCase__    : str = "."		)		->  Optional[Any]:





    def _flatten_dict(UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]=""    ,       UpperCamelCase__    : List[Any]="."		):
        for k, v in d.items():
            A       :       Tuple							=						str(SCREAMING_SNAKE_CASE__		) + delimiter + str(SCREAMING_SNAKE_CASE__		) if parent_key else k
            if v and isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		):
                yield from flatten_dict(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       delimiter=SCREAMING_SNAKE_CASE__		).items()
            else:
                yield key, v

    return dict(_flatten_dict(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		)		)
@contextmanager
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : bool = False		)		->  Optional[Any]:





    if use_temp_dir:
        with tempfile.TemporaryDirectory() as tmp_dir:
            yield tmp_dir
    else:
        yield working_dir
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Union[str, Any]=None		)		->  Optional[Any]:





    if is_numpy_array(SCREAMING_SNAKE_CASE__		):
        return np.transpose(SCREAMING_SNAKE_CASE__    ,       axes=SCREAMING_SNAKE_CASE__		)
    elif is_torch_tensor(SCREAMING_SNAKE_CASE__		):
        return array.T if axes is None else array.permute(*SCREAMING_SNAKE_CASE__		)
    elif is_tf_tensor(SCREAMING_SNAKE_CASE__		):
        import tensorflow as tf

        return tf.transpose(SCREAMING_SNAKE_CASE__    ,       perm=SCREAMING_SNAKE_CASE__		)
    elif is_jax_tensor(SCREAMING_SNAKE_CASE__		):
        return jnp.transpose(SCREAMING_SNAKE_CASE__    ,       axes=SCREAMING_SNAKE_CASE__		)
    else:
        raise ValueError(F'''Type not supported for transpose: {type(SCREAMING_SNAKE_CASE__		)}.'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[Any]		)		->  str:





    if is_numpy_array(SCREAMING_SNAKE_CASE__		):
        return np.reshape(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		)
    elif is_torch_tensor(SCREAMING_SNAKE_CASE__		):
        return array.reshape(*SCREAMING_SNAKE_CASE__		)
    elif is_tf_tensor(SCREAMING_SNAKE_CASE__		):
        import tensorflow as tf

        return tf.reshape(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		)
    elif is_jax_tensor(SCREAMING_SNAKE_CASE__		):
        return jnp.reshape(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		)
    else:
        raise ValueError(F'''Type not supported for reshape: {type(SCREAMING_SNAKE_CASE__		)}.'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : List[str]=None		)		->  Optional[int]:





    if is_numpy_array(SCREAMING_SNAKE_CASE__		):
        return np.squeeze(SCREAMING_SNAKE_CASE__    ,       axis=SCREAMING_SNAKE_CASE__		)
    elif is_torch_tensor(SCREAMING_SNAKE_CASE__		):
        return array.squeeze() if axis is None else array.squeeze(dim=SCREAMING_SNAKE_CASE__		)
    elif is_tf_tensor(SCREAMING_SNAKE_CASE__		):
        import tensorflow as tf

        return tf.squeeze(SCREAMING_SNAKE_CASE__    ,       axis=SCREAMING_SNAKE_CASE__		)
    elif is_jax_tensor(SCREAMING_SNAKE_CASE__		):
        return jnp.squeeze(SCREAMING_SNAKE_CASE__    ,       axis=SCREAMING_SNAKE_CASE__		)
    else:
        raise ValueError(F'''Type not supported for squeeze: {type(SCREAMING_SNAKE_CASE__		)}.'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Optional[int]		)		->  int:





    if is_numpy_array(SCREAMING_SNAKE_CASE__		):
        return np.expand_dims(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__		)
    elif is_torch_tensor(SCREAMING_SNAKE_CASE__		):
        return array.unsqueeze(dim=SCREAMING_SNAKE_CASE__		)
    elif is_tf_tensor(SCREAMING_SNAKE_CASE__		):
        import tensorflow as tf

        return tf.expand_dims(SCREAMING_SNAKE_CASE__    ,       axis=SCREAMING_SNAKE_CASE__		)
    elif is_jax_tensor(SCREAMING_SNAKE_CASE__		):
        return jnp.expand_dims(SCREAMING_SNAKE_CASE__    ,       axis=SCREAMING_SNAKE_CASE__		)
    else:
        raise ValueError(F'''Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE__		)}.'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:





    if is_numpy_array(SCREAMING_SNAKE_CASE__		):
        return np.size(SCREAMING_SNAKE_CASE__		)
    elif is_torch_tensor(SCREAMING_SNAKE_CASE__		):
        return array.numel()
    elif is_tf_tensor(SCREAMING_SNAKE_CASE__		):
        import tensorflow as tf

        return tf.size(SCREAMING_SNAKE_CASE__		)
    elif is_jax_tensor(SCREAMING_SNAKE_CASE__		):
        return array.size
    else:
        raise ValueError(F'''Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE__		)}.'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : List[str]		)		->  Tuple:





    for key, value in auto_map.items():
        if isinstance(SCREAMING_SNAKE_CASE__    ,       (tuple, list)		):
            A       :       Dict							=						[F'''{repo_id}--{v}''' if (v is not None and """--""" not in v) else v for v in value]
        elif value is not None and "--" not in value:
            A       :       Union[str, Any]							=						F'''{repo_id}--{value}'''

    return auto_map





def 							_lowerCamelCase(  UpperCamelCase__    : Tuple		)		->  Optional[int]:





    for base_class in inspect.getmro(SCREAMING_SNAKE_CASE__		):
        A       :       Dict							=						base_class.__module__
        A       :       int							=						base_class.__name__
        if module.startswith('''tensorflow'''		) or module.startswith('''keras'''		) or name == "TFPreTrainedModel":
            return "tf"
        elif module.startswith('''torch'''		) or name == "PreTrainedModel":
            return "pt"
        elif module.startswith('''flax'''		) or module.startswith('''jax'''		) or name == "FlaxPreTrainedModel":
            return "flax"
    else:
        raise TypeError(F'''Could not infer framework from class {model_class}.'''		)







'''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						:


   def __init__( self    ,   _UpperCAmelCase = "cpu"    ,   _UpperCAmelCase = "openai/clip-vit-large-patch14"					):
       A       :       List[Any]							=						device
       A       :       Optional[int]							=						CLIPTokenizerFast.from_pretrained(_UpperCAmelCase					)
       A       :       Dict							=						[0.48145466, 0.4578275, 0.40821073]
       A       :       List[str]							=						[0.26862954, 0.26130258, 0.27577711]
       A       :       List[Any]							=						torchvision.transforms.Normalize(self.image_mean    ,   self.image_std					)
       A       :       Dict							=						torchvision.transforms.Resize(224					)
       A       :       List[str]							=						torchvision.transforms.CenterCrop(224					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Any							=						self.resize(_UpperCAmelCase					)
       A       :       Optional[int]							=						self.center_crop(_UpperCAmelCase					)
       A       :       List[Any]							=						self.normalize(_UpperCAmelCase					)
       return images


   def __call__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase					):
       A       :       Any							=						self.tokenizer(text=_UpperCAmelCase    ,   **_UpperCAmelCase					)
       A       :       List[str]							=						self.preprocess_img(_UpperCAmelCase					)
       A       :       Any							=						{key: value.to(self.device					) for (key, value) in encoding.items()}
       return encoding







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase=10    ,   _UpperCAmelCase=0.01    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase="image"    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   ):
       super().__init__()
       A       :       Any							=						None
       A       :       Dict							=						device if device else get_device()
       if vqgan:
           A       :       Union[str, Any]							=						vqgan
       else:
           A       :       List[str]							=						load_vqgan(self.device    ,   conf_path=_UpperCAmelCase    ,   ckpt_path=_UpperCAmelCase					)
       self.vqgan.eval()
       if clip:
           A       :       str							=						clip
       else:
           A       :       Tuple							=						CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''					)
       self.clip.to(self.device					)
       A       :       Dict							=						ProcessorGradientFlow(device=self.device					)

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


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


   def 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       if not (path or img):
           raise ValueError('''Input either path or tensor'''					)
       if img is not None:
           raise NotImplementedError
       A       :       Tuple							=						preprocess(Image.open(_UpperCAmelCase					)    ,   target_image_size=256					).to(self.device					)
       A       :       List[str]							=						preprocess_vqgan(_UpperCAmelCase					)
       A,	*A       :       Dict							=						self.vqgan.encode(_UpperCAmelCase					)
       return z


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						self.latent.detach().requires_grad_()
       A       :       List[str]							=						base_latent + transform_vector
       if self.quantize:
           A,	*A       :       List[str]							=						self.vqgan.quantize(_UpperCAmelCase					)
       else:
           A       :       Any							=						trans_latent
       return self.vqgan.decode(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):
       A       :       List[str]							=						self.clip_preprocessor(text=_UpperCAmelCase    ,   images=_UpperCAmelCase    ,   return_tensors='''pt'''    ,   padding=_UpperCAmelCase					)
       A       :       int							=						self.clip(**_UpperCAmelCase					)
       A       :       Dict							=						clip_outputs.logits_per_image
       if weights is not None:
           A       :       Optional[Any]							=						similarity_logits * weights
       return similarity_logits.sum()


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						torch.randn_like(self.latent    ,   requires_grad=_UpperCAmelCase    ,   device=self.device					)
       A       :       Optional[int]							=						torch.optim.Adam([vector]    ,   lr=self.lr					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       wandb.init(reinit=_UpperCAmelCase    ,   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       :       Optional[Any]							=						Image.open(_UpperCAmelCase					)
           A       :       Dict							=						image.resize((256, 256)					)
           wandb.log('''Original Image'''    ,   wandb.Image(_UpperCAmelCase					)					)


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   ):
       if image_path:
           A       :       List[str]							=						self._get_latent(_UpperCAmelCase					)
       else:
           A       :       List[Any]							=						torch.randn(self.latent_dim    ,   device=self.device					)
       if self.log:
           self._init_logging(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)

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

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

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