


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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







class lowerCAmelCase_						(			A_	):

	'''simple docstring'''

	def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
							super().__init__()

							if hasattr(scheduler.config		,				'''steps_offset''' ) and scheduler.config.steps_offset != 1:
													snake_case_	=				(
													    F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
													    F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
													    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
													    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
													    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
													    " file"
													)
													deprecate('''steps_offset!=1'''		,				'''1.0.0'''		,				snake_case__		,				standard_warn=snake_case__ )
													snake_case_	=				dict(scheduler.config )
													snake_case_	=				1
													snake_case_	=				FrozenDict(snake_case__ )

							if hasattr(scheduler.config		,				'''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
													snake_case_	=				(
													    F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
													    " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
													    " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
													    " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
													    " Hub, it would be very nice if you could open a Pull request for the"
													    " `scheduler/scheduler_config.json` file"
													)
													deprecate('''skip_prk_steps not set'''		,				'''1.0.0'''		,				snake_case__		,				standard_warn=snake_case__ )
													snake_case_	=				dict(scheduler.config )
													snake_case_	=				True
													snake_case_	=				FrozenDict(snake_case__ )

							if safety_checker is None:
													logger.warning(
													    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
													    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
													    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
													    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
													    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
													    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''' )

							self.register_modules(
							    segmentation_model=snake_case__		,				segmentation_processor=snake_case__		,				vae=snake_case__		,				text_encoder=snake_case__		,				tokenizer=snake_case__		,				unet=snake_case__		,				scheduler=snake_case__		,				safety_checker=snake_case__		,				feature_extractor=snake_case__		,				)



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase = "auto" ):


							if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													snake_case_	=				self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(snake_case__ )



	def 						UpperCamelCase__					(     self ):
							self.enable_attention_slicing(snake_case__ )



	def 						UpperCamelCase__					(     self ):


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

							snake_case_	=				torch.device('''cuda''' )

							for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
													if cpu_offloaded_model is not None:
																			cpu_offload(snake_case__		,				snake_case__ )



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


							if self.device != torch.device('''meta''' ) or not hasattr(self.unet		,				'''_hf_hook''' ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(snake_case__		,				'''_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()
	def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = 5_12		,				_UpperCAmelCase = 5_12		,				_UpperCAmelCase = 50		,				_UpperCAmelCase = 7.5		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 1		,				_UpperCAmelCase = 0.0		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = "pil"		,				_UpperCAmelCase = True		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 1		,				**_UpperCAmelCase		,				):
							snake_case_	=				self.segmentation_processor(
							    text=[text]		,				images=[image]		,				padding='''max_length'''		,				return_tensors='''pt''' ).to(self.device )
							snake_case_	=				self.segmentation_model(**snake_case__ )
							snake_case_	=				torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
							snake_case_	=				self.numpy_to_pil(snake_case__ )[0].resize(image.size )

							# Run inpainting pipeline with the generated mask
							snake_case_	=				StableDiffusionInpaintPipeline(
							    vae=self.vae		,				text_encoder=self.text_encoder		,				tokenizer=self.tokenizer		,				unet=self.unet		,				scheduler=self.scheduler		,				safety_checker=self.safety_checker		,				feature_extractor=self.feature_extractor		,				)
							return inpainting_pipeline(
							    prompt=snake_case__		,				image=snake_case__		,				mask_image=snake_case__		,				height=snake_case__		,				width=snake_case__		,				num_inference_steps=snake_case__		,				guidance_scale=snake_case__		,				negative_prompt=snake_case__		,				num_images_per_prompt=snake_case__		,				eta=snake_case__		,				generator=snake_case__		,				latents=snake_case__		,				output_type=snake_case__		,				return_dict=snake_case__		,				callback=snake_case__		,				callback_steps=snake_case__		,				)



import numpy as np

import datasets


UpperCAmelCase      =   """
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

UpperCAmelCase      =   """\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
"""

UpperCAmelCase      =   """
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""







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

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       return datasets.MetricInfo(
           description=_DESCRIPTION		,				citation=_CITATION		,				inputs_description=_KWARGS_DESCRIPTION		,				features=datasets.Features(
               {
                   '''X''': datasets.Sequence(datasets.Value('''float'''		,				id='''sequence''' )		,				id='''X''' ),
               } )		,				)





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       # convert to numpy arrays
       snake_case_	=				np.array(_UpperCAmelCase )
       snake_case_	=				np.array(_UpperCAmelCase )

       # Assert that arrays are 2D
       if len(X.shape ) != 2:
             raise ValueError('''Expected `X` to be a 2D vector''' )
       if len(reference_distribution.shape ) != 2:
             raise ValueError('''Expected `reference_distribution` to be a 2D vector''' )
       if reference_distribution.shape[0] < 2:
             raise ValueError(
                 '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''' )

       # Get mahalanobis distance for each prediction
       snake_case_	=				X - np.mean(_UpperCAmelCase )
       snake_case_	=				np.cov(reference_distribution.T )
       try:
             snake_case_	=				np.linalg.inv(_UpperCAmelCase )
       except np.linalg.LinAlgError:
             snake_case_	=				np.linalg.pinv(_UpperCAmelCase )
       snake_case_	=				np.dot(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				np.dot(_UpperCAmelCase		,				X_minus_mu.T ).diagonal()

       return {"mahalanobis": mahal_dist}