



'''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     UpperCamelCase_				:





	def __init__(						self	,	A = "cpu"	,	A = "openai/clip-vit-large-patch14"					)    ->    None:
								UpperCAmelCase  :	Dict							  =	device
								UpperCAmelCase  :	Optional[int]							  =	CLIPTokenizerFast.from_pretrained(A					)
								UpperCAmelCase  :	int							  =	[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
								UpperCAmelCase  :	Dict							  =	[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
								UpperCAmelCase  :	int							  =	torchvision.transforms.Normalize(self.image_mean	,	self.image_std					)
								UpperCAmelCase  :	Optional[Any]							  =	torchvision.transforms.Resize(224					)
								UpperCAmelCase  :	Optional[Any]							  =	torchvision.transforms.CenterCrop(224					)





	def 				_lowercase(						self	,	A					)    ->    Optional[int]:
								UpperCAmelCase  :	List[Any]							  =	self.resize(A					)
								UpperCAmelCase  :	int							  =	self.center_crop(A					)
								UpperCAmelCase  :	str							  =	self.normalize(A					)
								return images






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




class     UpperCamelCase_				(							nn.Module    ):





	def __init__(						self	,	A=10	,	A=0.0_1	,	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	,	)    ->    None:
								super().__init__()
								UpperCAmelCase  :	Tuple							  =	None
								UpperCAmelCase  :	Union[str, Any]							  =	device if device else get_device()
								if vqgan:
															UpperCAmelCase  :	Tuple							  =	vqgan
								else:
															UpperCAmelCase  :	Optional[int]							  =	load_vqgan(self.device	,	conf_path=A	,	ckpt_path=A					)
								self.vqgan.eval()
								if clip:
															UpperCAmelCase  :	Tuple							  =	clip
								else:
															UpperCAmelCase  :	int							  =	CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""					)
								self.clip.to(self.device					)
								UpperCAmelCase  :	int							  =	ProcessorGradientFlow(device=self.device					)

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





	def 				_lowercase(						self	,	A=None	,	A=None	,	A=5	,	A=True					)    ->    List[str]:
								UpperCAmelCase  :	Any							  =	[]
								if output_path is None:
															UpperCAmelCase  :	Union[str, Any]							  =	"""./animation.gif"""
								if input_path is None:
															UpperCAmelCase  :	Optional[int]							  =	self.save_path
								UpperCAmelCase  :	Dict							  =	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?)"""					)
								UpperCAmelCase  :	Union[str, Any]							  =	total_duration / len(A					)
								UpperCAmelCase  :	str							  =	[frame_duration] * len(A					)
								if extend_frames:
															UpperCAmelCase  :	List[str]							  =	1.5
															UpperCAmelCase  :	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 				_lowercase(						self	,	A=None	,	A=None					)    ->    Optional[int]:
								if not (path or img):
															raise ValueError("""Input either path or tensor"""					)
								if img is not None:
															raise NotImplementedError
								UpperCAmelCase  :	int							  =	preprocess(Image.open(A					)	,	target_image_size=256					).to(self.device					)
								UpperCAmelCase  :	int							  =	preprocess_vqgan(A					)
								UpperCAmelCase  ,  *UpperCAmelCase  :	int							  =	self.vqgan.encode(A					)
								return z





	def 				_lowercase(						self	,	A					)    ->    int:
								UpperCAmelCase  :	int							  =	self.latent.detach().requires_grad_()
								UpperCAmelCase  :	int							  =	base_latent + transform_vector
								if self.quantize:
															UpperCAmelCase  ,  *UpperCAmelCase  :	Dict							  =	self.vqgan.quantize(A					)
								else:
															UpperCAmelCase  :	Union[str, Any]							  =	trans_latent
								return self.vqgan.decode(A					)





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





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Dict:
								UpperCAmelCase  :	int							  =	self._get_clip_similarity(pos_prompts["""prompts"""]	,	A	,	weights=(1 / pos_prompts["""weights"""])					)
								if neg_prompts:
															UpperCAmelCase  :	Any							  =	self._get_clip_similarity(neg_prompts["""prompts"""]	,	A	,	weights=neg_prompts["""weights"""]					)
								else:
															UpperCAmelCase  :	Optional[int]							  =	torch.tensor([1]	,	device=self.device					)
								UpperCAmelCase  :	List[Any]							  =	-torch.log(A					) + torch.log(A					)
								return loss





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    str:
								UpperCAmelCase  :	int							  =	torch.randn_like(self.latent	,	requires_grad=A	,	device=self.device					)
								UpperCAmelCase  :	List[str]							  =	torch.optim.Adam([vector]	,	lr=self.lr					)

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





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Dict:
								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:
															UpperCAmelCase  :	Any							  =	Image.open(A					)
															UpperCAmelCase  :	int							  =	image.resize((256, 256)					)
															wandb.log("""Original Image"""	,	wandb.Image(A					)					)





	def 				_lowercase(						self	,	A					)    ->    Dict:
								if not prompts:
															return []
								UpperCAmelCase  :	Dict							  =	[]
								UpperCAmelCase  :	List[str]							  =	[]
								if isinstance(A	,	A					):
															UpperCAmelCase  :	Tuple							  =	[prompt.strip() for prompt in prompts.split("""|"""					)]
								for prompt in prompts:
															if isinstance(A	,	(tuple, list)					):
																						UpperCAmelCase  :	str							  =	prompt[0]
																						UpperCAmelCase  :	List[Any]							  =	float(prompt[1]					)
															elif ":" in prompt:
																						UpperCAmelCase  ,  UpperCAmelCase  :	Union[str, Any]							  =	prompt.split(""":"""					)
																						UpperCAmelCase  :	str							  =	float(A					)
															else:
																						UpperCAmelCase  :	Tuple							  =	prompt
																						UpperCAmelCase  :	Dict							  =	1.0
															processed_prompts.append(A					)
															weights.append(A					)
								return {
								    "prompts": processed_prompts,
								    "weights": torch.tensor(A	,	device=self.device					),
								}






	def 				_lowercase(						self	,	A	,	A=None	,	A=None	,	A=True	,	A=False	,	A=True	,	A=True	,	A=None	,	)    ->    Tuple:
								if image_path:
															UpperCAmelCase  :	List[Any]							  =	self._get_latent(A					)
								else:
															UpperCAmelCase  :	List[str]							  =	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."
								UpperCAmelCase  :	List[Any]							  =	self.process_prompts(A					)
								UpperCAmelCase  :	List[str]							  =	self.process_prompts(A					)
								if save_final and save_path is None:
															UpperCAmelCase  :	Tuple							  =	os.path.join("""./outputs/"""	,	"""_""".join(pos_prompts["""prompts"""]					)					)
								if not os.path.exists(A					):
															os.makedirs(A					)
								else:
															UpperCAmelCase  :	List[str]							  =	save_path + """_""" + get_timestamp()
															os.makedirs(A					)
								UpperCAmelCase  :	Optional[Any]							  =	save_path

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

								UpperCAmelCase  :	str							  =	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'''					)					)






'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


a     :    Union[str, Any]      						=					"""\
@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}
}
"""

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


a     :    int      						=					"""
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     UpperCamelCase_				(							datasets.Metric    ):





	def 				_lowercase(						self					)    ->    List[str]:
								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 				_lowercase(						self					)    ->    List[Any]:
								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 				_lowercase(						self	,	A	,	A	,	A=None	,	A="uniform_average"	,	A=True					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	mean_squared_error(
								    A	,	A	,	sample_weight=A	,	multioutput=A	,	squared=A					)

								return {"mse": mse}

