'''simple docstring'''




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      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      def __init__(	self						:    str						, A						:    AutoencoderKL						, A						:    CLIPTextModel						, A						:    CLIPTokenizer						, A						:    UNetaDConditionModel						, A						:    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]						, ):
             super().__init__()

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




      def 					_A     (	self						:    Dict						, A						:    Optional[Any]	):
             with torch.no_grad():
                    _UpperCAmelCase					:  int											=   self.vae.encode(tfms.ToTensor()(A	).unsqueeze(0	).to(self.device	) * 2 - 1	)
                    _UpperCAmelCase					:  Dict											=   0.18_215 * latent.latent_dist.sample()
             return latent




      def 					_A     (	self						:    List[Any]						, A						:    Optional[int]	):
             _UpperCAmelCase					:  List[str]											=   (1 / 0.18_215) * latent
             with torch.no_grad():
                    _UpperCAmelCase					:  Dict											=   self.vae.decode(A	).sample
             _UpperCAmelCase					:  List[Any]											=   (img / 2 + 0.5).clamp(0						, 1	)
             _UpperCAmelCase					:  str											=   img.detach().cpu().permute(0						, 2						, 3						, 1	).numpy()
             _UpperCAmelCase					:  List[Any]											=   (img * 255).round().astype("uint8"	)
             return Image.fromarray(img[0]	)




      def 					_A     (	self						:    str						, A						:    List[str]	):
             _UpperCAmelCase					:  Optional[Any]											=   self.tokenizer(
                 A						, padding="max_length"						, max_length=self.tokenizer.model_max_length						, truncation=A						, return_tensors="pt"						, )

             _UpperCAmelCase					:  Any											=   self.text_encoder(text_input.input_ids.to(self.device	)	)[0]

             _UpperCAmelCase					:  int											=   self.tokenizer(
                 ""						, padding="max_length"						, max_length=self.tokenizer.model_max_length						, truncation=A						, return_tensors="pt"						, )

             _UpperCAmelCase					:  Tuple											=   self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

             return torch.cat([uncond_embedding, text_embedding]	)





      def __call__(	self						:    Tuple						, A						:    Image.Image						, A						:    str						, A						:    float = 0.3						, A						:    float = 0.6						, A						:    float = 0.5						, A						:    int = 42						, A						:    int = 50						, A						:    float = 7.5						, ):
             _UpperCAmelCase					:  str											=   steps - int(kmin * steps	)
             _UpperCAmelCase					:  Any											=   steps - int(kmax * steps	)

             _UpperCAmelCase					:  Dict											=   self.prep_text(A	)

             self.scheduler.set_timesteps(A	)

             _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   img.size
             _UpperCAmelCase					:  Any											=   self.encode(A	)

             torch.manual_seed(A	)
             _UpperCAmelCase					:  str											=   torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)						, ).to(self.device	)

             _UpperCAmelCase					:  Optional[int]											=   self.scheduler.add_noise(
                 A						, A						, timesteps=self.scheduler.timesteps[tmax]						, )

             _UpperCAmelCase					:  List[Any]											=   torch.cat([latents] * 2	)

             _UpperCAmelCase					:  Optional[int]											=   self.scheduler.scale_model_input(A						, self.scheduler.timesteps[tmax]	)

             with torch.no_grad():
                    _UpperCAmelCase					:  Optional[Any]											=   self.unet(
                        A						, self.scheduler.timesteps[tmax]						, encoder_hidden_states=A						, ).sample

             _UpperCAmelCase				,  _UpperCAmelCase					:  List[str]											=   pred.chunk(2	)
             _UpperCAmelCase					:  Optional[int]											=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

             _UpperCAmelCase					:  List[str]											=   self.scheduler.step(A						, self.scheduler.timesteps[tmax]						, A	).prev_sample

             for i, t in enumerate(tqdm(self.scheduler.timesteps	)	):
                    if i > tmax:
                           if i < tmin:  # layout generation phase
                                  _UpperCAmelCase					:  Union[str, Any]											=   self.scheduler.add_noise(
                                      A						, A						, timesteps=A						, )

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

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

                           _UpperCAmelCase					:  Union[str, Any]											=   self.scheduler.scale_model_input(A						, A	)

                           with torch.no_grad():
                                  _UpperCAmelCase					:  Any											=   self.unet(
                                      A						, A						, encoder_hidden_states=A						, ).sample

                           _UpperCAmelCase				,  _UpperCAmelCase					:  Optional[int]											=   pred.chunk(2	)
                           _UpperCAmelCase					:  Tuple											=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                           _UpperCAmelCase					:  Tuple											=   self.scheduler.step(A						, A						, A	).prev_sample

             return self.decode(A	)


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
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
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
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}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@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      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             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 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

