




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 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      ["""vqvae"""]





				def __init__(   self       :  Optional[int]      ,   __lowerCamelCase       :  AutoencoderKL      ,   __lowerCamelCase       :  UNetaDConditionModel      ,   __lowerCamelCase       :  Mel      ,   __lowerCamelCase       :  Union[DDIMScheduler, DDPMScheduler]      ,   )      ->  List[Any]:
							super().__init__()
							self.register_modules(unet=__lowerCamelCase      ,   scheduler=__lowerCamelCase      ,   mel=__lowerCamelCase      ,   vqvae=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  int:
							return 50 if isinstance(self.scheduler      ,   __lowerCamelCase							) else 10_00





				@torch.no_grad()
				def __call__(   self       :  int      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  str = None      ,   __lowerCamelCase       :  np.ndarray = None      ,   __lowerCamelCase       :  int = 0      ,   __lowerCamelCase       :  int = 0      ,   __lowerCamelCase       :  int = None      ,   __lowerCamelCase       :  torch.Generator = None      ,   __lowerCamelCase       :  float = 0      ,   __lowerCamelCase       :  float = 0      ,   __lowerCamelCase       :  torch.Generator = None      ,   __lowerCamelCase       :  float = 0      ,   __lowerCamelCase       :  torch.Tensor = None      ,   __lowerCamelCase       :  torch.Tensor = None      ,   __lowerCamelCase       :  List[Any]=True      ,   )      ->  Union[
				    Union[AudioPipelineOutput, ImagePipelineOutput],
				    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
				]:
							a					=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__lowerCamelCase							)
							a					=    step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size							) == int:
										a					=    (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
										a					=    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      ,   )
							a					=    noise
							a					=    None

							if audio_file is not None or raw_audio is not None:
										self.mel.load_audio(__lowerCamelCase      ,   __lowerCamelCase							)
										a					=    self.mel.audio_slice_to_image(__lowerCamelCase							)
										a					=    np.frombuffer(input_image.tobytes()      ,   dtype="uint8"							).reshape(
										    (input_image.height, input_image.width)							)
										a					=    (input_image / 2_55) * 2 - 1
										a					=    torch.tensor(input_image[np.newaxis, :, :]      ,   dtype=torch.float							).to(self.device							)

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

										if start_step > 0:
													a					=    self.scheduler.add_noise(__lowerCamelCase      ,   __lowerCamelCase      ,   self.scheduler.timesteps[start_step - 1]							)

										a					=    (
										    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
										)
										a					=    int(mask_start_secs * pixels_per_second							)
										a					=    int(mask_end_secs * pixels_per_second							)
										a					=    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							):
													a					=    self.unet(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase							)["sample"]
										else:
													a					=    self.unet(__lowerCamelCase      ,   __lowerCamelCase							)["sample"]

										if isinstance(self.scheduler      ,   __lowerCamelCase							):
													a					=    self.scheduler.step(
													    model_output=__lowerCamelCase      ,   timestep=__lowerCamelCase      ,   sample=__lowerCamelCase      ,   eta=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   )["prev_sample"]
										else:
													a					=    self.scheduler.step(
													    model_output=__lowerCamelCase      ,   timestep=__lowerCamelCase      ,   sample=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   )["prev_sample"]

										if mask is not None:
													if mask_start > 0:
																a					=    mask[:, step, :, :mask_start]
													if mask_end > 0:
																a					=    mask[:, step, :, -mask_end:]

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

							a					=    (images / 2 + 0.5).clamp(0      ,   1							)
							a					=    images.cpu().permute(0      ,   2      ,   3      ,   1							).numpy()
							a					=    (images * 2_55).round().astype("uint8"							)
							a					=    list(
							    (Image.fromarray(_[:, :, 0]							) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__lowerCamelCase      ,   mode="RGB"							).convert("L"							) for _ in images)							)

							a					=    [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 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[Image.Image]      ,   __lowerCamelCase       :  int = 50							)      ->  np.ndarray:
							assert isinstance(self.scheduler      ,   __lowerCamelCase							)
							self.scheduler.set_timesteps(__lowerCamelCase							)
							a					=    np.array(
							    [np.frombuffer(image.tobytes()      ,   dtype="uint8"							).reshape((1, image.height, image.width)							) for image in images]							)
							a					=    (sample / 2_55) * 2 - 1
							a					=    torch.Tensor(__lowerCamelCase							).to(self.device							)

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

							return sample





				@staticmethod
				def 							__UpperCAmelCase							(   __lowerCamelCase       :  torch.Tensor      ,   __lowerCamelCase       :  torch.Tensor      ,   __lowerCamelCase       :  float							)      ->  torch.Tensor:
							a					=    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							)












from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowerCAmelCase	:       Optional[Any]     =	'\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

__lowerCAmelCase	:       str     =	'\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

__lowerCAmelCase	:       List[Any]     =	'\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION     )
class 					snake_case__							(datasets.Metric     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  int							)      ->  MetricInfo:
							return datasets.MetricInfo(
							    description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
							        {
							            "predictions": datasets.Sequence(datasets.Value("string"      ,   id="token"							)      ,   id="sequence"							),
							            "references": datasets.Sequence(
							                datasets.Sequence(datasets.Value("string"      ,   id="token"							)      ,   id="sequence"							)      ,   id="references"							),
							        }							)      ,   )





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[List[List[str]]]      ,   __lowerCamelCase       :  List[List[str]]      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  int = 4      ,   )      ->  Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=__lowerCamelCase      ,   hypotheses=__lowerCamelCase      ,   min_len=__lowerCamelCase      ,   max_len=__lowerCamelCase							)
							}






