


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						:		Any  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any] = "cpu"  ,     SCREAMING_SNAKE_CASE						:		Tuple = "openai/clip-vit-large-patch14"    )      ->						None:




											'''simple docstring'''

											__snake_case				=		device
											__snake_case				=		CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE    )
											__snake_case				=		[0.48145466, 0.4578275, 0.40821073]
											__snake_case				=		[0.26862954, 0.26130258, 0.27577711]
											__snake_case				=		torchvision.transforms.Normalize(self.image_mean  ,     self.image_std    )
											__snake_case				=		torchvision.transforms.Resize(2_2_4    )
											__snake_case				=		torchvision.transforms.CenterCrop(2_2_4    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		List[str]    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.resize(SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.center_crop(SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.normalize(SCREAMING_SNAKE_CASE    )
											return images






					def __call__(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		List[Any]=None  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=None  ,     **SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.tokenizer(text=SCREAMING_SNAKE_CASE  ,     **SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.preprocess_img(SCREAMING_SNAKE_CASE    )
											__snake_case				=		{key: value.to(self.device    ) for (key, value) in encoding.items()}
											return encoding






class      UpperCamelCase(	nn.Module ):


					def __init__(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		str=1_0  ,     SCREAMING_SNAKE_CASE						:		List[str]=0.01  ,     SCREAMING_SNAKE_CASE						:		List[str]=None  ,     SCREAMING_SNAKE_CASE						:		List[Any]=None  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=None  ,     SCREAMING_SNAKE_CASE						:		int=None  ,     SCREAMING_SNAKE_CASE						:		List[Any]=None  ,     SCREAMING_SNAKE_CASE						:		List[str]=None  ,     SCREAMING_SNAKE_CASE						:		str=False  ,     SCREAMING_SNAKE_CASE						:		Any=True  ,     SCREAMING_SNAKE_CASE						:		Optional[int]="image"  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=True  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=False  ,     SCREAMING_SNAKE_CASE						:		Dict=False  ,     SCREAMING_SNAKE_CASE						:		List[Any]=False  ,     )      ->						None:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		None
											__snake_case				=		device if device else get_device()
											if vqgan:
																	__snake_case				=		vqgan
											else:
																	__snake_case				=		load_vqgan(self.device  ,     conf_path=SCREAMING_SNAKE_CASE  ,     ckpt_path=SCREAMING_SNAKE_CASE    )
											self.vqgan.eval()
											if clip:
																	__snake_case				=		clip
											else:
																	__snake_case				=		CLIPModel.from_pretrained("openai/clip-vit-base-patch32"    )
											self.clip.to(self.device    )
											__snake_case				=		ProcessorGradientFlow(device=self.device    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=None  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=None  ,     SCREAMING_SNAKE_CASE						:		Dict=5  ,     SCREAMING_SNAKE_CASE						:		Any=True    )      ->						int:




											'''simple docstring'''

											__snake_case				=		[]
											if output_path is None:
																	__snake_case				=		"./animation.gif"
											if input_path is None:
																	__snake_case				=		self.save_path
											__snake_case				=		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?)"    )
											__snake_case				=		total_duration / len(SCREAMING_SNAKE_CASE    )
											__snake_case				=		[frame_duration] * len(SCREAMING_SNAKE_CASE    )
											if extend_frames:
																	__snake_case				=		1.5
																	__snake_case				=		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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		int=None  ,     SCREAMING_SNAKE_CASE						:		Dict=None    )      ->						Optional[int]:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Dict    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.latent.detach().requires_grad_()
											__snake_case				=		base_latent + transform_vector
											if self.quantize:
																	__snake_case				=		self.vqgan.quantize(SCREAMING_SNAKE_CASE    )
											else:
																	__snake_case				=		trans_latent
											return self.vqgan.decode(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		str=None    )      ->						Dict:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]    )      ->						List[Any]:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.randn_like(self.latent  ,     requires_grad=SCREAMING_SNAKE_CASE  ,     device=self.device    )
											__snake_case				=		torch.optim.Adam([vector]  ,     lr=self.lr    )

											for i in range(self.iterations    ):
																	optim.zero_grad()
																	__snake_case				=		self._add_vector(SCREAMING_SNAKE_CASE    )
																	__snake_case				=		loop_post_process(SCREAMING_SNAKE_CASE    )
																	__snake_case				=		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 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						Union[str, Any]:




											'''simple docstring'''

											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:
																	__snake_case				=		Image.open(SCREAMING_SNAKE_CASE    )
																	__snake_case				=		image.resize((2_5_6, 2_5_6)    )
																	wandb.log("Original Image"  ,     wandb.Image(SCREAMING_SNAKE_CASE    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						Tuple:




											'''simple docstring'''

											if not prompts:
																	return []
											__snake_case				=		[]
											__snake_case				=		[]
											if isinstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		[prompt.strip() for prompt in prompts.split("|"    )]
											for prompt in prompts:
																	if isinstance(SCREAMING_SNAKE_CASE  ,     (tuple, list)    ):
																							__snake_case				=		prompt[0]
																							__snake_case				=		float(prompt[1]    )
																	elif ":" in prompt:
																							__snake_case				=		prompt.split(":"    )
																							__snake_case				=		float(SCREAMING_SNAKE_CASE    )
																	else:
																							__snake_case				=		prompt
																							__snake_case				=		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 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=True  ,     SCREAMING_SNAKE_CASE						:		Tuple=False  ,     SCREAMING_SNAKE_CASE						:		Any=True  ,     SCREAMING_SNAKE_CASE						:		str=True  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     )      ->						Tuple:




											'''simple docstring'''

											if image_path:
																	__snake_case				=		self._get_latent(SCREAMING_SNAKE_CASE    )
											else:
																	__snake_case				=		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."
											__snake_case				=		self.process_prompts(SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.process_prompts(SCREAMING_SNAKE_CASE    )
											if save_final and save_path is None:
																	__snake_case				=		os.path.join("./outputs/"  ,     "_".join(pos_prompts["prompts"]    )    )
											if not os.path.exists(SCREAMING_SNAKE_CASE    ):
																	os.makedirs(SCREAMING_SNAKE_CASE    )
											else:
																	__snake_case				=		save_path + "_" + get_timestamp()
																	os.makedirs(SCREAMING_SNAKE_CASE    )
											__snake_case				=		save_path

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

											__snake_case				=		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'''    )    )












from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__							:      List[str]         =    """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

SCREAMING_SNAKE_CASE__							:      Tuple         =    """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION	)
class 	__lowerCAmelCase      (       datasets.Metric	):





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      return datasets.MetricInfo(
          description=_DESCRIPTION       ,      citation=_CITATION       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(self._get_feature_types()   )       ,      reference_urls=[
              "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
          ]       ,      )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"   )   ),
             "references": datasets.Sequence(datasets.Value("float"   )   ),
         }
      else:
         return {
             "predictions": datasets.Value("float"   ),
             "references": datasets.Value("float"   ),
         }





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case=None       ,      snake_case="uniform_average"       ,      snake_case=True   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[str]   		=	mean_squared_error(
          snake_case       ,      snake_case       ,      sample_weight=snake_case       ,      multioutput=snake_case       ,      squared=snake_case   )

      return {"mse": mse}

