






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


_a    :   Any        =			"""\
@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}
}

"""

_a    :   str        =			"""\
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
"""

_a    :   Tuple        =			"""
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 	_UpperCAmelCase  (  datasets.Metric		):




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''simple docstring'''
          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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase="auto"    ,	_lowerCAmelCase=-1    ,	_lowerCAmelCase=0.9    ,	_lowerCAmelCase=5    ,	_lowerCAmelCase=500    ,	_lowerCAmelCase="gpt2-large"    ,	_lowerCAmelCase=-1    ,	_lowerCAmelCase=1_024    ,	_lowerCAmelCase=25    ,	_lowerCAmelCase=5    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=25    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[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 math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    ['''vqvae''']
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__()
          self.register_modules(unet=_lowerCAmelCase    ,	scheduler=_lowerCAmelCase    ,	mel=_lowerCAmelCase    ,	vqvae=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 50 if isinstance(self.scheduler    ,	_lowerCAmelCase		) else 1_000
      @torch.no_grad()
      def __call__(   self    ,	_lowerCAmelCase = 1    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase=True    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				steps or self.get_default_steps()
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size		) == int:
              lowerCAmelCase__   :Dict      							=				(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
              lowerCAmelCase__   :Optional[int]      							=				randn_tensor(
                  (
                      batch_size,
                      self.unet.config.in_channels,
                      self.unet.config.sample_size[0],
                      self.unet.config.sample_size[1],
                  )    ,	generator=_lowerCAmelCase    ,	device=self.device    ,	)
          lowerCAmelCase__   :Union[str, Any]      							=				noise
          lowerCAmelCase__   :Any      							=				None

          if audio_file is not None or raw_audio is not None:
              self.mel.load_audio(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				self.mel.audio_slice_to_image(_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				np.frombuffer(input_image.tobytes()    ,	dtype="uint8"		).reshape(
                  (input_image.height, input_image.width)		)
              lowerCAmelCase__   :Tuple      							=				(input_image / 255) * 2 - 1
              lowerCAmelCase__   :Any      							=				torch.tensor(input_image[np.newaxis, :, :]    ,	dtype=torch.float		).to(self.device		)

              if self.vqvae is not None:
                  lowerCAmelCase__   :str      							=				self.vqvae.encode(torch.unsqueeze(_lowerCAmelCase    ,	0		)		).latent_dist.sample(
                      generator=_lowerCAmelCase		)[0]
                  lowerCAmelCase__   :Dict      							=				self.vqvae.config.scaling_factor * input_images

              if start_step > 0:
                  lowerCAmelCase__   :Dict      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	self.scheduler.timesteps[start_step - 1]		)

              lowerCAmelCase__   :Optional[Any]      							=				(
                  self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
              )
              lowerCAmelCase__   :Dict      							=				int(mask_start_secs * pixels_per_second		)
              lowerCAmelCase__   :Tuple      							=				int(mask_end_secs * pixels_per_second		)
              lowerCAmelCase__   :str      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	torch.tensor(self.scheduler.timesteps[start_step:]		)		)

          for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
              if isinstance(self.unet    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Optional[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              else:
                  lowerCAmelCase__   :Dict      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]

              if isinstance(self.scheduler    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Any      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	eta=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]
              else:
                  lowerCAmelCase__   :List[str]      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]

              if mask is not None:
                  if mask_start > 0:
                      lowerCAmelCase__   :List[Any]      							=				mask[:, step, :, :mask_start]
                  if mask_end > 0:
                      lowerCAmelCase__   :Optional[Any]      							=				mask[:, step, :, -mask_end:]

          if self.vqvae is not None:
              # 0.18215 was scaling factor used in training to ensure unit variance
              lowerCAmelCase__   :Any      							=				1 / self.vqvae.config.scaling_factor * images
              lowerCAmelCase__   :List[Any]      							=				self.vqvae.decode(_lowerCAmelCase		)["sample"]

          lowerCAmelCase__   :Dict      							=				(images / 2 + 0.5).clamp(0    ,	1		)
          lowerCAmelCase__   :Union[str, Any]      							=				images.cpu().permute(0    ,	2    ,	3    ,	1		).numpy()
          lowerCAmelCase__   :Optional[int]      							=				(images * 255).round().astype("uint8"		)
          lowerCAmelCase__   :Optional[int]      							=				list(
              (Image.fromarray(_[:, :, 0]		) for _ in images)
              if images.shape[3] == 1
              else (Image.fromarray(_lowerCAmelCase    ,	mode="RGB"		).convert("L"		) for _ in images)		)

          lowerCAmelCase__   :Optional[Any]      							=				[self.mel.image_to_audio(_lowerCAmelCase		) for _ in images]
          if not return_dict:
              return images, (self.mel.get_sample_rate(), audios)

          return BaseOutput(**AudioPipelineOutput(np.array(_lowerCAmelCase		)[:, np.newaxis, :]		)    ,	**ImagePipelineOutput(_lowerCAmelCase		)		)
      @torch.no_grad()
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = 50		):



          '''simple docstring'''
          assert isinstance(self.scheduler    ,	_lowerCAmelCase		)
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				np.array(
              [np.frombuffer(image.tobytes()    ,	dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
          lowerCAmelCase__   :Dict      							=				(sample / 255) * 2 - 1
          lowerCAmelCase__   :Optional[Any]      							=				torch.Tensor(_lowerCAmelCase		).to(self.device		)

          for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,	(0,)		)		):
              lowerCAmelCase__   :List[Any]      							=				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
              lowerCAmelCase__   :Any      							=				self.scheduler.alphas_cumprod[t]
              lowerCAmelCase__   :List[Any]      							=				(
                  self.scheduler.alphas_cumprod[prev_timestep]
                  if prev_timestep >= 0
                  else self.scheduler.final_alpha_cumprod
              )
              lowerCAmelCase__   :List[str]      							=				1 - alpha_prod_t
              lowerCAmelCase__   :List[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              lowerCAmelCase__   :int      							=				(1 - alpha_prod_t_prev) ** 0.5 * model_output
              lowerCAmelCase__   :List[Any]      							=				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
              lowerCAmelCase__   :Union[str, Any]      							=				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

          return sample



      @staticmethod
      def 			snake_case_      (   _lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				acos(torch.dot(torch.flatten(_lowerCAmelCase		)    ,	torch.flatten(_lowerCAmelCase		)		) / torch.norm(_lowerCAmelCase		) / torch.norm(_lowerCAmelCase		)		)
          return sin((1 - alpha) * theta		) * xa / sin(_lowerCAmelCase		) + sin(alpha * theta		) * xa / sin(_lowerCAmelCase		)



