'''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 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






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




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				device
       UpperCAmelCase_							:       Tuple       =				CLIPTokenizerFast.from_pretrained(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
       UpperCAmelCase_							:       Union[str, Any]       =				[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
       UpperCAmelCase_							:       Tuple       =				torchvision.transforms.Normalize(self.image_mean				,		self.image_std						)
       UpperCAmelCase_							:       Optional[Any]       =				torchvision.transforms.Resize(2_2_4						)
       UpperCAmelCase_							:       Any       =				torchvision.transforms.CenterCrop(2_2_4						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.resize(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				self.center_crop(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.normalize(snake_case_						)
       return images






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




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.tokenizer(text=snake_case_				,		**snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.preprocess_img(snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				{key: value.to(self.device						) for (key, value) in encoding.items()}
       return encoding







class 					__SCREAMING_SNAKE_CASE						(	nn.Module	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_=1_0				,		snake_case_=0.01				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=False				,		snake_case_=True				,		snake_case_="image"				,		snake_case_=True				,		snake_case_=False				,		snake_case_=False				,		snake_case_=False				,		):




       '''simple docstring'''

       super().__init__()
       UpperCAmelCase_							:       List[str]       =				None
       UpperCAmelCase_							:       Dict       =				device if device else get_device()
       if vqgan:
            UpperCAmelCase_							:       Any       =				vqgan
       else:
            UpperCAmelCase_							:       Dict       =				load_vqgan(self.device				,		conf_path=snake_case_				,		ckpt_path=snake_case_						)
       self.vqgan.eval()
       if clip:
            UpperCAmelCase_							:       List[str]       =				clip
       else:
            UpperCAmelCase_							:       List[Any]       =				CLIPModel.from_pretrained('openai/clip-vit-base-patch32'						)
       self.clip.to(self.device						)
       UpperCAmelCase_							:       Tuple       =				ProcessorGradientFlow(device=self.device						)

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


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=5				,		snake_case_=True						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None						):




       '''simple docstring'''

       if not (path or img):
            raise ValueError('Input either path or tensor'						)
       if img is not None:
            raise NotImplementedError
       UpperCAmelCase_							:       Optional[Any]       =				preprocess(Image.open(snake_case_						)				,		target_image_size=2_5_6						).to(self.device						)
       UpperCAmelCase_							:       Dict       =				preprocess_vqgan(snake_case_						)
       UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.encode(snake_case_						)
       return z


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.latent.detach().requires_grad_()
       UpperCAmelCase_							:       List[Any]       =				base_latent + transform_vector
       if self.quantize:
            UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.quantize(snake_case_						)
       else:
            UpperCAmelCase_							:       Optional[int]       =				trans_latent
       return self.vqgan.decode(snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.clip_preprocessor(text=snake_case_				,		images=snake_case_				,		return_tensors='pt'				,		padding=snake_case_						)
       UpperCAmelCase_							:       Any       =				self.clip(**snake_case_						)
       UpperCAmelCase_							:       Dict       =				clip_outputs.logits_per_image
       if weights is not None:
            UpperCAmelCase_							:       Union[str, Any]       =				similarity_logits * weights
       return similarity_logits.sum()


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

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

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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       wandb.init(reinit=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:
            UpperCAmelCase_							:       str       =				Image.open(snake_case_						)
            UpperCAmelCase_							:       str       =				image.resize((2_5_6, 2_5_6)						)
            wandb.log('Original Image'				,		wandb.Image(snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_=None				,		snake_case_=None				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=True				,		snake_case_=None				,		):




       '''simple docstring'''

       if image_path:
            UpperCAmelCase_							:       List[Any]       =				self._get_latent(snake_case_						)
       else:
            UpperCAmelCase_							:       Any       =				torch.randn(self.latent_dim				,		device=self.device						)
       if self.log:
            self._init_logging(snake_case_				,		snake_case_				,		snake_case_						)

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

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

       UpperCAmelCase_							:       Optional[int]       =				loop_post_process(snake_case_						)
       for iter, transformed_img in enumerate(self._optimize_CLIP(snake_case_				,		snake_case_				,		snake_case_						)						):
            if show_intermediate:
                 show_pil(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(snake_case_						)}						)
       if show_final:
            show_pil(snake_case_						)
       if save_final:
            transformed_img.save(os.path.join(self.save_path				,		F'''iter_{iter:03d}_final.png'''						)						)







'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


snake_case__					:      str				      =  '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


snake_case__					:      int				      =  '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


snake_case__					:      Tuple				      =  '''
@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}
}
'''






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







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return datasets.MetricInfo(
           description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
               {
                   'predictions': datasets.Sequence(datasets.Value('int32'						)						),
                   'references': datasets.Sequence(datasets.Value('int32'						)						),
               }
               if self.config_name == 'multilabel'
               else {
                   'predictions': datasets.Value('int32'						),
                   'references': datasets.Value('int32'						),
               }						)				,		reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']				,		)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=None				,		snake_case_=1				,		snake_case_="binary"				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				fa_score(
           snake_case_				,		snake_case_				,		labels=snake_case_				,		pos_label=snake_case_				,		average=snake_case_				,		sample_weight=snake_case_						)
       return {"f1": float(snake_case_						) if score.size == 1 else score}






