






from typing import List, Optional, Union

import torch

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,
)


__snake_case    :    Optional[int]         =     logging.get_logger(__name__)  # pylint: disable=invalid-name

__snake_case    :    List[str]         =     """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""







def 	_UpperCamelCase   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					List[Any]=8		)  ->   int:

						"""simple docstring"""



						lowerCAmelCase__    						=						height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowerCAmelCase__    						=						width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						):






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

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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if latents is None:
																		lowerCAmelCase__    						=						randn_tensor(_UpperCamelCase     ,						generator=_UpperCamelCase     ,						device=_UpperCamelCase     ,						dtype=_UpperCamelCase   )
												else:
																		if latents.shape != shape:
																								raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
																		lowerCAmelCase__    						=						latents.to(_UpperCamelCase   )

												lowerCAmelCase__    						=						latents * scheduler.init_noise_sigma
												return latents






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=0   ):






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

												lowerCAmelCase__    						=						torch.device(F"cuda:{gpu_id}"   )

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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=0   ):






												"""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.'   )

												lowerCAmelCase__    						=						torch.device(F"cuda:{gpu_id}"   )

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

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

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






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






												"""simple docstring"""
												if not hasattr(self.unet     ,						'_hf_hook'   ):
																		return self.device
												for module in self.unet.modules():
																		if (
																		    hasattr(_UpperCamelCase     ,						'_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(_UpperCamelCase   )
						def __call__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = 5_12     ,						_UpperCamelCase = 5_12     ,						_UpperCamelCase = 1_00     ,						_UpperCamelCase = 4.0     ,						_UpperCamelCase = 1     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = "pil"     ,						_UpperCamelCase = True     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						self._execution_device

												lowerCAmelCase__    						=						guidance_scale > 1.0

												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						torch.cat(_UpperCamelCase     ,						dim=0   )
												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						torch.cat(_UpperCamelCase     ,						dim=0   )
												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						torch.cat(_UpperCamelCase     ,						dim=0   )

												lowerCAmelCase__    						=						image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																		lowerCAmelCase__    						=						image_embeds.repeat_interleave(_UpperCamelCase     ,						dim=0   )
																		lowerCAmelCase__    						=						negative_image_embeds.repeat_interleave(_UpperCamelCase     ,						dim=0   )
																		lowerCAmelCase__    						=						hint.repeat_interleave(_UpperCamelCase     ,						dim=0   )

												lowerCAmelCase__    						=						torch.cat([negative_image_embeds, image_embeds]     ,						dim=0   ).to(dtype=self.unet.dtype     ,						device=_UpperCamelCase   )
												lowerCAmelCase__    						=						torch.cat([hint, hint]     ,						dim=0   ).to(dtype=self.unet.dtype     ,						device=_UpperCamelCase   )
												self.scheduler.set_timesteps(_UpperCamelCase     ,						device=_UpperCamelCase   )
												lowerCAmelCase__    						=						self.scheduler.timesteps

												lowerCAmelCase__    						=						self.movq.config.latent_channels

												lowerCAmelCase__						,       lowerCAmelCase__    						=						downscale_height_and_width(_UpperCamelCase     ,						_UpperCamelCase     ,						self.movq_scale_factor   )

												# create initial latent
												lowerCAmelCase__    						=						self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)     ,						image_embeds.dtype     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						self.scheduler     ,						)

												for i, t in enumerate(self.progress_bar(_UpperCamelCase   )   ):
																		# expand the latents if we are doing classifier free guidance
																		lowerCAmelCase__    						=						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

																		lowerCAmelCase__    						=						{'image_embeds': image_embeds, 'hint': hint}
																		lowerCAmelCase__    						=						self.unet(
																		    sample=_UpperCamelCase     ,						timestep=_UpperCamelCase     ,						encoder_hidden_states=_UpperCamelCase     ,						added_cond_kwargs=_UpperCamelCase     ,						return_dict=_UpperCamelCase     ,						)[0]

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

																		# compute the previous noisy sample x_t -> x_t-1
																		lowerCAmelCase__    						=						self.scheduler.step(
																		    _UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						generator=_UpperCamelCase     ,						)[0]
												# post-processing
												lowerCAmelCase__    						=						self.movq.decode(_UpperCamelCase     ,						force_not_quantize=_UpperCamelCase   )['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"]:
																		lowerCAmelCase__    						=						image * 0.5 + 0.5
																		lowerCAmelCase__    						=						image.clamp(0     ,						1   )
																		lowerCAmelCase__    						=						image.cpu().permute(0     ,						2     ,						3     ,						1   ).float().numpy()

												if output_type == "pil":
																		lowerCAmelCase__    						=						self.numpy_to_pil(_UpperCamelCase   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_UpperCamelCase   )













from typing import Dict, Optional

import numpy as np

import datasets


__snake_case    :    str         =     """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""

__snake_case    :    Tuple         =     """
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
"""

__snake_case    :    Any         =     """\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   List[Any]:

						"""simple docstring"""



						if label_map is not None:
												for old_id, new_id in label_map.items():
																		lowerCAmelCase__    						=						new_id

    # turn into Numpy arrays
						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)

						if reduce_labels:
												lowerCAmelCase__    						=						255
												lowerCAmelCase__    						=						label - 1
												lowerCAmelCase__    						=						255

						lowerCAmelCase__    						=						label != ignore_index
						lowerCAmelCase__    						=						np.not_equal(UpperCamelCase_    ,       UpperCamelCase_		)
						lowerCAmelCase__    						=						pred_label[mask]
						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)[mask]

						lowerCAmelCase__    						=						pred_label[pred_label == label]

						lowerCAmelCase__    						=						np.histogram(UpperCamelCase_    ,       bins=UpperCamelCase_    ,       range=(0, num_labels - 1)		)[0]
						lowerCAmelCase__    						=						np.histogram(UpperCamelCase_    ,       bins=UpperCamelCase_    ,       range=(0, num_labels - 1)		)[0]
						lowerCAmelCase__    						=						np.histogram(UpperCamelCase_    ,       bins=UpperCamelCase_    ,       range=(0, num_labels - 1)		)[0]

						lowerCAmelCase__    						=						area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						for result, gt_seg_map in zip(UpperCamelCase_    ,       UpperCamelCase_		):
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						intersect_and_union(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
												total_area_intersect += area_intersect
												total_area_union += area_union
												total_area_pred_label += area_pred_label
												total_area_label += area_label
						return total_area_intersect, total_area_union, total_area_pred_label, total_area_label







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[int] = None    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						total_intersect_and_union(
						    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# compute metrics
						lowerCAmelCase__    						=						{}

						lowerCAmelCase__    						=						total_area_intersect.sum() / total_area_label.sum()
						lowerCAmelCase__    						=						total_area_intersect / total_area_union
						lowerCAmelCase__    						=						total_area_intersect / total_area_label

						lowerCAmelCase__    						=						np.nanmean(UpperCamelCase_		)
						lowerCAmelCase__    						=						np.nanmean(UpperCamelCase_		)
						lowerCAmelCase__    						=						all_acc
						lowerCAmelCase__    						=						iou
						lowerCAmelCase__    						=						acc

						if nan_to_num is not None:
												lowerCAmelCase__    						=						{metric: np.nan_to_num(UpperCamelCase_    ,       nan=UpperCamelCase_		) for metric, metric_value in metrics.items()}

						return metrics


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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(
												        # 1st Seq - height dim, 2nd - width dim
												        {
												            'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'   )   )   ),
												            'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'   )   )   ),
												        }   )     ,						reference_urls=[
												        'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
												    ]     ,						)







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = False     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						mean_iou(
												    results=_UpperCamelCase     ,						gt_seg_maps=_UpperCamelCase     ,						num_labels=_UpperCamelCase     ,						ignore_index=_UpperCamelCase     ,						nan_to_num=_UpperCamelCase     ,						label_map=_UpperCamelCase     ,						reduce_labels=_UpperCamelCase     ,						)
												return iou_result





