






import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


UpperCAmelCase_ :						List[str]      =      '''\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

'''

UpperCAmelCase_ :						Optional[Any]      =      '''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''

UpperCAmelCase_ :						Dict      =      '''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: "c" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric(\'mauve\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
'''

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





	def 						_A (		self				:					Dict	):
		return datasets.MetricInfo(
		    description=_DESCRIPTION ,  citation=_CITATION ,  homepage="""https://github.com/krishnap25/mauve""" ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""string""" ,  id="""sequence"""	),
		            """references""": datasets.Value("""string""" ,  id="""sequence"""	),
		        }	) ,  codebase_urls=["""https://github.com/krishnap25/mauve"""] ,  reference_urls=[
		        """https://arxiv.org/abs/2102.01454""",
		        """https://github.com/krishnap25/mauve""",
		    ] ,  )





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					str=None ,  __lowerCamelCase				:					Union[str, Any]=None ,  __lowerCamelCase				:					int=None ,  __lowerCamelCase				:					Tuple=None ,  __lowerCamelCase				:					Any="auto" ,  __lowerCamelCase				:					List[Any]=-1 ,  __lowerCamelCase				:					Optional[int]=0.9 ,  __lowerCamelCase				:					Dict=5 ,  __lowerCamelCase				:					Optional[Any]=500 ,  __lowerCamelCase				:					int="gpt2-large" ,  __lowerCamelCase				:					Union[str, Any]=-1 ,  __lowerCamelCase				:					List[str]=1_024 ,  __lowerCamelCase				:					Dict=25 ,  __lowerCamelCase				:					Any=5 ,  __lowerCamelCase				:					List[Any]=True ,  __lowerCamelCase				:					Tuple=25 ,  ):
		UpperCamelCase     :int          =	compute_mauve(
		    p_text=__lowerCamelCase ,  q_text=__lowerCamelCase ,  p_features=__lowerCamelCase ,  q_features=__lowerCamelCase ,  p_tokens=__lowerCamelCase ,  q_tokens=__lowerCamelCase ,  num_buckets=__lowerCamelCase ,  pca_max_data=__lowerCamelCase ,  kmeans_explained_var=__lowerCamelCase ,  kmeans_num_redo=__lowerCamelCase ,  kmeans_max_iter=__lowerCamelCase ,  featurize_model_name=__lowerCamelCase ,  device_id=__lowerCamelCase ,  max_text_length=__lowerCamelCase ,  divergence_curve_discretization_size=__lowerCamelCase ,  mauve_scaling_factor=__lowerCamelCase ,  verbose=__lowerCamelCase ,  seed=__lowerCamelCase ,  )
		return out







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


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






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

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







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         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	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         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	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, 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	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         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	:List[str]    ,   _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    ,   )->					Union[ImagePipelineOutput, Tuple]:
         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 )





