




"""simple docstring"""

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


a         =      transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 						_snake_case					( _snake_case							: Optional[int]							)   -> List[Any]:






      '''simple docstring'''


      if isinstance(_snake_case    ,  torch.Tensor							):
            return image
      elif isinstance(_snake_case    ,  PIL.Image.Image							):
            _A        =       [image]

      _A        =       [trans(img.convert('RGB'							)							) for img in image]
      _A        =       torch.stack(_snake_case							)
      return image







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	int		):
       super().__init__()

       # make sure scheduler can always be converted to DDIM
       _A        =       DDIMScheduler.from_config(scheduler.config		)

       self.register_modules(unet=_UpperCAmelCase   ,							scheduler=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Tuple		):
       if strength < 0 or strength > 1:
             raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Any		):
       # get the original timestep using init_timestep
       _A        =       min(int(num_inference_steps * strength		)   ,							_UpperCAmelCase		)

       _A        =       max(num_inference_steps - init_timestep   ,							0		)
       _A        =       self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	Union[str, Any]=None		):
       if not isinstance(_UpperCAmelCase   ,							(torch.Tensor, PIL.Image.Image, list)		):
             raise ValueError(
                 F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCAmelCase		)}'''		)

       _A        =       image.to(device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		)

       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) and len(_UpperCAmelCase		) != batch_size:
             raise ValueError(
                 F'''You have passed a list of generators of length {len(_UpperCAmelCase		)}, but requested an effective batch'''
                 F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

       _A        =       init_latents.shape
       _A        =       randn_tensor(_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		)

       # get latents
       print('add noise to latents at timestep'   ,							_UpperCAmelCase		)
       _A        =       self.scheduler.add_noise(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       _A        =       init_latents

       return latents



 @torch.no_grad()
 def __call__(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, PIL.Image.Image] = None   ,							_UpperCAmelCase	:	float = 0.8   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	float = 0.0   ,							_UpperCAmelCase	:	int = 50   ,							_UpperCAmelCase	:	Optional[bool] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							):
       self.check_inputs(_UpperCAmelCase		)

       # 2. Preprocess image
       _A        =       preprocess(_UpperCAmelCase		)

       # 3. set timesteps
       self.scheduler.set_timesteps(_UpperCAmelCase   ,							device=self.device		)
       _A       ,   _A        =       self.get_timesteps(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.device		)
       _A        =       timesteps[:1].repeat(_UpperCAmelCase		)

       # 4. Prepare latent variables
       _A        =       self.prepare_latents(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.unet.dtype   ,							self.device   ,							_UpperCAmelCase		)
       _A        =       latents

       # 5. Denoising loop
       for t in self.progress_bar(_UpperCAmelCase		):
             # 1. predict noise model_output
             _A        =       self.unet(_UpperCAmelCase   ,							_UpperCAmelCase		).sample

             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             _A        =       self.scheduler.step(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							eta=_UpperCAmelCase   ,							use_clipped_model_output=_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							).prev_sample

       _A        =       (image / 2 + 0.5).clamp(0   ,							1		)
       _A        =       image.cpu().permute(0   ,							2   ,							3   ,							1		).numpy()
       if output_type == "pil":
             _A        =       self.numpy_to_pil(_UpperCAmelCase		)

       if not return_dict:
             return (image, latent_timestep.item())

       return ImagePipelineOutput(images=_UpperCAmelCase		)







"""simple docstring"""

from sklearn.metrics import fa_score

import datasets


a         =      '''
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)
'''


a         =      '''
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. ])}
'''


a         =      '''
@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     lowercase_	(					datasets.Metric							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Any		):
       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       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	List[str]=None   ,							_UpperCAmelCase	:	Dict=1   ,							_UpperCAmelCase	:	Union[str, Any]="binary"   ,							_UpperCAmelCase	:	List[str]=None		):
       _A        =       fa_score(
           _UpperCAmelCase   ,							_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							pos_label=_UpperCAmelCase   ,							average=_UpperCAmelCase   ,							sample_weight=_UpperCAmelCase		)
       return {"f1": float(_UpperCAmelCase		) if score.size == 1 else score}

