





from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class    __A  (    a     ):


				def __init__(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			):
								super().__init__()

								self.register_modules(vae=UpperCAmelCase_ ,			text_encoder=UpperCAmelCase_ ,			tokenizer=UpperCAmelCase_ ,			unet=UpperCAmelCase_ ,			scheduler=UpperCAmelCase_				)


				def _snake_case							(		self ,			UpperCAmelCase_				):
								with torch.no_grad():
												lowerCamelCase									=self.vae.encode(tfms.ToTensor()(UpperCAmelCase_				).unsqueeze(0				).to(self.device				) * 2 - 1				)
												lowerCamelCase									=0.1_8_2_1_5 * latent.latent_dist.sample()
								return latent


				def _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=(1 / 0.1_8_2_1_5) * latent
								with torch.no_grad():
												lowerCamelCase									=self.vae.decode(UpperCAmelCase_				).sample
								lowerCamelCase									=(img / 2 + 0.5).clamp(0 ,			1				)
								lowerCamelCase									=img.detach().cpu().permute(0 ,			2 ,			3 ,			1				).numpy()
								lowerCamelCase									=(img * 255).round().astype("""uint8"""				)
								return Image.fromarray(img[0]				)


				def _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=self.tokenizer(
								    UpperCAmelCase_ ,			padding="""max_length""" ,			max_length=self.tokenizer.model_max_length ,			truncation=UpperCAmelCase_ ,			return_tensors="""pt""" ,			)

								lowerCamelCase									=self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

								lowerCamelCase									=self.tokenizer(
								    """""" ,			padding="""max_length""" ,			max_length=self.tokenizer.model_max_length ,			truncation=UpperCAmelCase_ ,			return_tensors="""pt""" ,			)

								lowerCamelCase									=self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

								return torch.cat([uncond_embedding, text_embedding]				)


				def __call__(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ = 0.3 ,			UpperCAmelCase_ = 0.6 ,			UpperCAmelCase_ = 0.5 ,			UpperCAmelCase_ = 42 ,			UpperCAmelCase_ = 50 ,			UpperCAmelCase_ = 7.5 ,			):
								lowerCamelCase									=steps - int(kmin * steps				)
								lowerCamelCase									=steps - int(kmax * steps				)

								lowerCamelCase									=self.prep_text(UpperCAmelCase_				)

								self.scheduler.set_timesteps(UpperCAmelCase_				)

								lowerCamelCase			,	lowerCamelCase									=img.size
								lowerCamelCase									=self.encode(UpperCAmelCase_				)

								torch.manual_seed(UpperCAmelCase_				)
								lowerCamelCase									=torch.randn(
								    (1, self.unet.config.in_channels, height // 8, width // 8) ,			).to(self.device				)

								lowerCamelCase									=self.scheduler.add_noise(
								    UpperCAmelCase_ ,			UpperCAmelCase_ ,			timesteps=self.scheduler.timesteps[tmax] ,			)

								lowerCamelCase									=torch.cat([latents] * 2				)

								lowerCamelCase									=self.scheduler.scale_model_input(UpperCAmelCase_ ,			self.scheduler.timesteps[tmax]				)

								with torch.no_grad():
												lowerCamelCase									=self.unet(
												    UpperCAmelCase_ ,			self.scheduler.timesteps[tmax] ,			encoder_hidden_states=UpperCAmelCase_ ,			).sample

								lowerCamelCase			,	lowerCamelCase									=pred.chunk(2				)
								lowerCamelCase									=pred_uncond + guidance_scale * (pred_text - pred_uncond)

								lowerCamelCase									=self.scheduler.step(UpperCAmelCase_ ,			self.scheduler.timesteps[tmax] ,			UpperCAmelCase_				).prev_sample

								for i, t in enumerate(tqdm(self.scheduler.timesteps				)				):
												if i > tmax:
																if i < tmin:  # layout generation phase
																				lowerCamelCase									=self.scheduler.add_noise(
																				    UpperCAmelCase_ ,			UpperCAmelCase_ ,			timesteps=UpperCAmelCase_ ,			)

																				lowerCamelCase									=(mix_factor * latents) + (
																				    1 - mix_factor
																				) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																				lowerCamelCase									=torch.cat([input] * 2				)

																else:  # content generation phase
																				lowerCamelCase									=torch.cat([latents] * 2				)

																lowerCamelCase									=self.scheduler.scale_model_input(UpperCAmelCase_ ,			UpperCAmelCase_				)

																with torch.no_grad():
																				lowerCamelCase									=self.unet(
																				    UpperCAmelCase_ ,			UpperCAmelCase_ ,			encoder_hidden_states=UpperCAmelCase_ ,			).sample

																lowerCamelCase			,	lowerCamelCase									=pred.chunk(2				)
																lowerCamelCase									=pred_uncond + guidance_scale * (pred_text - pred_uncond)

																lowerCamelCase									=self.scheduler.step(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				).prev_sample

								return self.decode(UpperCAmelCase_				)












from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      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}
}
'''

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


				def _snake_case							(		self				):
								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.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





