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,
)


lowerCAmelCase__  :  Union[str, Any]											=     logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase__  :  List[Any]											=     '''
    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   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase=8	):
				UpperCAmelCase__					=							height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				UpperCAmelCase__					=							width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor



def   a_						( lowerCamelCase ,      lowerCamelCase=5_1_2 ,      lowerCamelCase=5_1_2	):
				UpperCAmelCase__					=							pil_image.resize((w, h) ,      resample=Image.BICUBIC ,      reducing_gap=1	)
				UpperCAmelCase__					=							np.array(pil_image.convert('RGB'	)	)
				UpperCAmelCase__					=							arr.astype(np.floataa	) / 127.5 - 1
				UpperCAmelCase__					=							np.transpose(SCREAMING_SNAKE_CASE_ ,      [2, 0, 1]	)
				UpperCAmelCase__					=							torch.from_numpy(SCREAMING_SNAKE_CASE_	).unsqueeze(0	)
				return image







class     snake_case	(			snake_case_    ):




					"""simple docstring"""

					def __init__(    self       :     int   ,lowerCamelCase__       :     Tuple   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[Any]   ,):
									super().__init__()

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







					def 		__lowerCAmelCase						(    self       :     str   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     Optional[int]      ):
									UpperCAmelCase__					=							min(int(num_inference_steps * strength      )   ,lowerCamelCase__      )

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

									return timesteps, num_inference_steps - t_start







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Tuple   ,lowerCamelCase__       :     Optional[int]   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     Optional[Any]=None      ):


									if not isinstance(lowerCamelCase__   ,(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(lowerCamelCase__      )}'''      )

									UpperCAmelCase__					=							image.to(device=lowerCamelCase__   ,dtype=lowerCamelCase__      )

									UpperCAmelCase__					=							batch_size * num_images_per_prompt

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

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

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

													UpperCAmelCase__					=							self.movq.config.scaling_factor * init_latents

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

									UpperCAmelCase__					=							init_latents.shape
									UpperCAmelCase__					=							randn_tensor(lowerCamelCase__   ,generator=lowerCamelCase__   ,device=lowerCamelCase__   ,dtype=lowerCamelCase__      )

									# get latents
									UpperCAmelCase__					=							self.scheduler.add_noise(lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__      )

									UpperCAmelCase__					=							init_latents

									return latents







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[int]=0      ):


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

									UpperCAmelCase__					=							torch.device(f'''cuda:{gpu_id}'''      )

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







					def 		__lowerCAmelCase						(    self       :     Optional[int]   ,lowerCamelCase__       :     List[str]=0      ):


									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.'      )

									UpperCAmelCase__					=							torch.device(f'''cuda:{gpu_id}'''      )

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

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

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







					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 		__lowerCAmelCase						(    self       :     Dict      ):


									if not hasattr(self.unet   ,'_hf_hook'      ):
													return self.device
									for module in self.unet.modules():
													if (
													    hasattr(lowerCamelCase__   ,'_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(lowerCamelCase__      )
					def __call__(    self       :     str   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[Any]   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Any = 512   ,lowerCamelCase__       :     List[Any] = 512   ,lowerCamelCase__       :     str = 100   ,lowerCamelCase__       :     Union[str, Any] = 4.0   ,lowerCamelCase__       :     Union[str, Any] = 0.3   ,lowerCamelCase__       :     Optional[Any] = 1   ,lowerCamelCase__       :     Union[str, Any] = None   ,lowerCamelCase__       :     Any = "pil"   ,lowerCamelCase__       :     str = True   ,):
									UpperCAmelCase__					=							self._execution_device

									UpperCAmelCase__					=							guidance_scale > 1.0

									if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
													UpperCAmelCase__					=							torch.cat(lowerCamelCase__   ,dim=0      )
									if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
													UpperCAmelCase__					=							torch.cat(lowerCamelCase__   ,dim=0      )
									if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
													UpperCAmelCase__					=							torch.cat(lowerCamelCase__   ,dim=0      )

									UpperCAmelCase__					=							image_embeds.shape[0]

									if do_classifier_free_guidance:
													UpperCAmelCase__					=							image_embeds.repeat_interleave(lowerCamelCase__   ,dim=0      )
													UpperCAmelCase__					=							negative_image_embeds.repeat_interleave(lowerCamelCase__   ,dim=0      )
													UpperCAmelCase__					=							hint.repeat_interleave(lowerCamelCase__   ,dim=0      )

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

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

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

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

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

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

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

													UpperCAmelCase__					=							self.scheduler.step(
													    lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,generator=lowerCamelCase__   ,)[0]

									# post-processing
									UpperCAmelCase__					=							self.movq.decode(lowerCamelCase__   ,force_not_quantize=lowerCamelCase__      )["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"]:
													UpperCAmelCase__					=							image * 0.5 + 0.5
													UpperCAmelCase__					=							image.clamp(0   ,1      )
													UpperCAmelCase__					=							image.cpu().permute(0   ,2   ,3   ,1      ).float().numpy()

									if output_type == "pil":
													UpperCAmelCase__					=							self.numpy_to_pil(lowerCamelCase__      )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=lowerCamelCase__      )




"""simple docstring"""






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[str]											=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


lowerCAmelCase__  :  List[str]											=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> 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])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


lowerCAmelCase__  :  Dict											=     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION    )
class     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[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.accuracy_score.html']   ,)







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



