

"""simple docstring"""





from sklearn.metrics import precision_score

import datasets


_a					 =				'''
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
'''


_a					 =				'''
Args:
    predictions (`list` of `int`): Predicted class labels.
    references (`list` of `int`): Actual class labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. 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.
    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.

        - 0: Returns 0 when there is a zero division.
        - 1: Returns 1 when there is a zero division.
        - \'warn\': Raises warnings and then returns 0 when there is a zero division.

Returns:
    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.

Examples:

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

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

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_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(results)
        {\'precision\': 0.23529411764705882}

    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 = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'precision\': 0.3333333333333333}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)
        >>> print([round(res, 2) for res in results[\'precision\']])
        [0.67, 0.0, 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    _lowerCAmelCase    (      datasets.Metric  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Union[str, 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.precision_score.html"],   )



 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     int=None,   UpperCAmelCase__				:     List[Any]=1,   UpperCAmelCase__				:     List[Any]="binary",   UpperCAmelCase__				:     Optional[Any]=None,   UpperCAmelCase__				:     List[Any]="warn",   ):
    __lowercase								=		precision_score(
        UpperCAmelCase__,   UpperCAmelCase__,   labels=UpperCAmelCase__,   pos_label=UpperCAmelCase__,   average=UpperCAmelCase__,   sample_weight=UpperCAmelCase__,   zero_division=UpperCAmelCase__,   )
    return {"precision": float(UpperCAmelCase__      ) if score.size == 1 else score}










from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE_    :       int       =							logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE_    :       int       =							'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_=8		):
    a__  			=							height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    a__  			=							width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_=512 ,     UpperCAmelCase_=512		):
    a__  			=							pil_image.resize((w, h) ,     resample=Image.BICUBIC ,     reducing_gap=1		)
    a__  			=							np.array(pil_image.convert("""RGB"""		)		)
    a__  			=							arr.astype(np.floataa		) / 1_2_7.5 - 1
    a__  			=							np.transpose(UpperCAmelCase_ ,     [2, 0, 1]		)
    a__  			=							torch.from_numpy(UpperCAmelCase_		).unsqueeze(0		)
    return image







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]    ,					A			:							UNetaDConditionModel    ,					A			:							DDPMScheduler    ,					A			:							VQModel    ,					)      ->      Any:






         """simple docstring"""
         super().__init__()

         self.register_modules(
             unet=A    ,					scheduler=A    ,					movq=A    ,					)
         a__  			=							2 ** (len(self.movq.config.block_out_channels     ) - 1)

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Optional[int]    ,					A			:							int    ,					A			:							Any     )      ->      Dict:






         """simple docstring"""
         # get the original timestep using init_timestep
         a__  			=							min(int(num_inference_steps * strength     )    ,					A     )

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

         return timesteps, num_inference_steps - t_start

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							List[Any]    ,					A			:							Dict    ,					A			:							str    ,					A			:							Union[str, Any]    ,					A			:							Union[str, Any]    ,					A			:							str    ,					A			:							str=None     )      ->      List[Any]:






         """simple docstring"""
         if not isinstance(A    ,					(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(A     )}"""     )

         a__  			=							image.to(device=A    ,					dtype=A     )

         a__  			=							batch_size * num_images_per_prompt

         if image.shape[1] == 4:
             a__  			=							image

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

             elif isinstance(A    ,					A     ):
                 a__  			=							[
                     self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(A     )
                 ]
                 a__  			=							torch.cat(A    ,					dim=0     )
             else:
                 a__  			=							self.movq.encode(A     ).latent_dist.sample(A     )

             a__  			=							self.movq.config.scaling_factor * init_latents

         a__  			=							torch.cat([init_latents]    ,					dim=0     )

         a__  			=							init_latents.shape
         a__  			=							randn_tensor(A    ,					generator=A    ,					device=A    ,					dtype=A     )

         # get latents
         a__  			=							self.scheduler.add_noise(A    ,					A    ,					A     )

         a__  			=							init_latents

         return latents

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Union[str, Any]=0     )      ->      Tuple:






         """simple docstring"""
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         a__  			=							torch.device(f"""cuda:{gpu_id}"""     )

         a__  			=							[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(A    ,					A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Optional[Any]=0     )      ->      Dict:






         """simple docstring"""
         if is_accelerate_available() and is_accelerate_version(""">="""    ,					"""0.17.0.dev0"""     ):
             from accelerate import cpu_offload_with_hook
         else:
             raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""     )

         a__  			=							torch.device(f"""cuda:{gpu_id}"""     )

         if self.device.type != "cpu":
             self.to("""cpu"""    ,					silence_dtype_warnings=A     )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         a__  			=							None
         for cpu_offloaded_model in [self.unet, self.movq]:
             a__			,		a__  			=							cpu_offload_with_hook(A    ,					A    ,					prev_module_hook=A     )

         # We'll offload the last model manually.
         a__  			=							hook

     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      Optional[int]:






         """simple docstring"""
         if not hasattr(self.unet    ,					"""_hf_hook"""     ):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(A    ,					"""_hf_hook"""     )
                 and hasattr(module._hf_hook    ,					"""execution_device"""     )
                 and module._hf_hook.execution_device is not None
             ):
                 return torch.device(module._hf_hook.execution_device     )
         return self.device






     @torch.no_grad()
     @replace_example_docstring(A     )
     def __call__(   self			:							int    ,					A			:							Union[torch.FloatTensor, List[torch.FloatTensor]]    ,					A			:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]    ,					A			:							Union[torch.FloatTensor, List[torch.FloatTensor]]    ,					A			:							torch.FloatTensor    ,					A			:							int = 5_12    ,					A			:							int = 5_12    ,					A			:							int = 1_00    ,					A			:							float = 4.0    ,					A			:							float = 0.3    ,					A			:							int = 1    ,					A			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,					A			:							Optional[str] = "pil"    ,					A			:							bool = True    ,					)      ->      List[Any]:






         """simple docstring"""
         a__  			=							self._execution_device

         a__  			=							guidance_scale > 1.0

         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )
         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )
         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )

         a__  			=							image_embeds.shape[0]

         if do_classifier_free_guidance:
             a__  			=							image_embeds.repeat_interleave(A    ,					dim=0     )
             a__  			=							negative_image_embeds.repeat_interleave(A    ,					dim=0     )
             a__  			=							hint.repeat_interleave(A    ,					dim=0     )

         a__  			=							torch.cat([negative_image_embeds, image_embeds]    ,					dim=0     ).to(dtype=self.unet.dtype    ,					device=A     )
         a__  			=							torch.cat([hint, hint]    ,					dim=0     ).to(dtype=self.unet.dtype    ,					device=A     )

         if not isinstance(A    ,					A     ):
             a__  			=							[image]
         if not all(isinstance(A    ,					(PIL.Image.Image, torch.Tensor)     ) for i in image     ):
             raise ValueError(
                 f"""Input is in incorrect format: {[type(A     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""     )

         a__  			=							torch.cat([prepare_image(A    ,					A    ,					A     ) for i in image]    ,					dim=0     )
         a__  			=							image.to(dtype=image_embeds.dtype    ,					device=A     )

         a__  			=							self.movq.encode(A     )["""latents"""]
         a__  			=							latents.repeat_interleave(A    ,					dim=0     )
         self.scheduler.set_timesteps(A    ,					device=A     )
         a__			,		a__  			=							self.get_timesteps(A    ,					A    ,					A     )
         a__  			=							timesteps[:1].repeat(batch_size * num_images_per_prompt     )
         a__			,		a__  			=							downscale_height_and_width(A    ,					A    ,					self.movq_scale_factor     )
         a__  			=							self.prepare_latents(
             A    ,					A    ,					A    ,					A    ,					image_embeds.dtype    ,					A    ,					A     )
         for i, t in enumerate(self.progress_bar(A     )     ):
             # expand the latents if we are doing classifier free guidance
             a__  			=							torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

             a__  			=							{"""image_embeds""": image_embeds, """hint""": hint}
             a__  			=							self.unet(
                 sample=A    ,					timestep=A    ,					encoder_hidden_states=A    ,					added_cond_kwargs=A    ,					return_dict=A    ,					)[0]

             if do_classifier_free_guidance:
                 a__			,		a__  			=							noise_pred.split(latents.shape[1]    ,					dim=1     )
                 a__			,		a__  			=							noise_pred.chunk(2     )
                 a__			,		a__  			=							variance_pred.chunk(2     )
                 a__  			=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 a__  			=							torch.cat([noise_pred, variance_pred_text]    ,					dim=1     )

             if not (
                 hasattr(self.scheduler.config    ,					"""variance_type"""     )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                 a__			,		a__  			=							noise_pred.split(latents.shape[1]    ,					dim=1     )

             # compute the previous noisy sample x_t -> x_t-1

             a__  			=							self.scheduler.step(
                 A    ,					A    ,					A    ,					generator=A    ,					)[0]

         # post-processing
         a__  			=							self.movq.decode(A    ,					force_not_quantize=A     )["""sample"""]

         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""     )

         if output_type in ["np", "pil"]:
             a__  			=							image * 0.5 + 0.5
             a__  			=							image.clamp(0    ,					1     )
             a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     ).float().numpy()

         if output_type == "pil":
             a__  			=							self.numpy_to_pil(A     )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A     )






