

"""simple docstring"""


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     (    __snake_case  ):
							SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  ["""vqvae"""]

							def __init__(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Any					,  )->       Union[str, Any]:



									'''simple docstring'''


									super().__init__()
									self.register_modules(unet=lowerCamelCase_					,  scheduler=lowerCamelCase_					,  mel=lowerCamelCase_					,  vqvae=lowerCamelCase_)

							def 							lowercase_					(     self							:    List[Any])->       int:



									'''simple docstring'''


									return 5_0 if isinstance(self.scheduler					,  lowerCamelCase_) else 1_0_0_0

							@torch.no_grad()
							def __call__(     self							:    List[str]					,  UpperCamelCase__							:    Union[str, Any] = 1					,  UpperCamelCase__							:    str = None					,  UpperCamelCase__							:    Optional[int] = None					,  UpperCamelCase__							:    Optional[int] = 0					,  UpperCamelCase__							:    int = 0					,  UpperCamelCase__							:    Tuple = None					,  UpperCamelCase__							:    Tuple = None					,  UpperCamelCase__							:    Dict = 0					,  UpperCamelCase__							:    Dict = 0					,  UpperCamelCase__							:    Any = None					,  UpperCamelCase__							:    Dict = 0					,  UpperCamelCase__							:    Optional[Any] = None					,  UpperCamelCase__							:    Tuple = None					,  UpperCamelCase__							:    Optional[int]=True					,  )->       Union[
							    Union[AudioPipelineOutput, ImagePipelineOutput],
							    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
							]:



									'''simple docstring'''


									__lowerCAmelCase:	int   				=      steps or self.get_default_steps()
									self.scheduler.set_timesteps(lowerCamelCase_)
									__lowerCAmelCase:	Optional[int]   				=      step_generator or generator
									# For backwards compatibility
									if type(self.unet.config.sample_size) == int:
											__lowerCAmelCase:	Optional[Any]   				=      (self.unet.config.sample_size, self.unet.config.sample_size)
									if noise is None:
											__lowerCAmelCase:	Tuple   				=      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:	Tuple   				=      noise
									__lowerCAmelCase:	Dict   				=      None

									if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(lowerCamelCase_					,  lowerCamelCase_)
											__lowerCAmelCase:	Optional[Any]   				=      self.mel.audio_slice_to_image(lowerCamelCase_)
											__lowerCAmelCase:	str   				=      np.frombuffer(input_image.tobytes()					,  dtype="uint8").reshape(
											    (input_image.height, input_image.width))
											__lowerCAmelCase:	int   				=      (input_image / 2_5_5) * 2 - 1
											__lowerCAmelCase:	List[Any]   				=      torch.tensor(input_image[np.newaxis, :, :]					,  dtype=torch.float).to(self.device)

											if self.vqvae is not None:
													__lowerCAmelCase:	Optional[Any]   				=      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:	Union[str, Any]   				=      self.scheduler.add_noise(lowerCamelCase_					,  lowerCamelCase_					,  self.scheduler.timesteps[start_step - 1])

											__lowerCAmelCase:	Tuple   				=      (
											    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
											)
											__lowerCAmelCase:	Any   				=      int(mask_start_secs * pixels_per_second)
											__lowerCAmelCase:	List[str]   				=      int(mask_end_secs * pixels_per_second)
											__lowerCAmelCase:	Union[str, Any]   				=      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[int]   				=      self.unet(lowerCamelCase_					,  lowerCamelCase_					,  lowerCamelCase_)["sample"]
											else:
													__lowerCAmelCase:	List[Any]   				=      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:	Dict   				=      self.scheduler.step(
													    model_output=lowerCamelCase_					,  timestep=lowerCamelCase_					,  sample=lowerCamelCase_					,  generator=lowerCamelCase_					,  )["prev_sample"]

											if mask is not None:
													if mask_start > 0:
															__lowerCAmelCase:	Union[str, Any]   				=      mask[:, step, :, :mask_start]
													if mask_end > 0:
															__lowerCAmelCase:	str   				=      mask[:, step, :, -mask_end:]

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

									__lowerCAmelCase:	Optional[int]   				=      (images / 2 + 0.5).clamp(0					,  1)
									__lowerCAmelCase:	List[str]   				=      images.cpu().permute(0					,  2					,  3					,  1).numpy()
									__lowerCAmelCase:	Union[str, Any]   				=      (images * 2_5_5).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:	int   				=      [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 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Dict = 5_0)->       np.ndarray:



									'''simple docstring'''


									assert isinstance(self.scheduler					,  lowerCamelCase_)
									self.scheduler.set_timesteps(lowerCamelCase_)
									__lowerCAmelCase:	Tuple   				=      np.array(
									    [np.frombuffer(image.tobytes()					,  dtype="uint8").reshape((1, image.height, image.width)) for image in images])
									__lowerCAmelCase:	Optional[int]   				=      (sample / 2_5_5) * 2 - 1
									__lowerCAmelCase:	Union[str, Any]   				=      torch.Tensor(lowerCamelCase_).to(self.device)

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

									return sample




							@staticmethod
							def 							lowercase_					(     UpperCamelCase__							:    Any					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Dict)->       torch.Tensor:



									'''simple docstring'''


									__lowerCAmelCase:	List[Any]   				=      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_)





"""simple docstring"""


import comet  # From: unbabel-comet
import torch

import datasets


__A						       =      datasets.logging.get_logger(__name__)

__A						       =      "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

__A						       =      "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

__A						       =      "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"





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

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  homepage="https://unbabel.github.io/COMET/html/index.html"					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
						        {
						            "sources": datasets.Value("string"					,  id="sequence"),
						            "predictions": datasets.Value("string"					,  id="sequence"),
						            "references": datasets.Value("string"					,  id="sequence"),
						        })					,  codebase_urls=["https://github.com/Unbabel/COMET"]					,  reference_urls=[
						        "https://github.com/Unbabel/COMET",
						        "https://www.aclweb.org/anthology/2020.emnlp-main.213/",
						        "http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
						    ]					,  )

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Any)->       Dict:



						'''simple docstring'''


						if self.config_name == "default":
								__lowerCAmelCase:	Union[str, Any]   				=      comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"))
						else:
								__lowerCAmelCase:	Tuple   				=      comet.load_from_checkpoint(comet.download_model(self.config_name))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    List[str]=False)->       str:



						'''simple docstring'''


						if gpus is None:
								__lowerCAmelCase:	Union[str, Any]   				=      1 if torch.cuda.is_available() else 0
						__lowerCAmelCase:	Dict   				=      {"src": sources, "mt": predictions, "ref": references}
						__lowerCAmelCase:	Union[str, Any]   				=      [dict(zip(UpperCamelCase__					,  UpperCamelCase__)) for t in zip(*data.values())]
						__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      self.scorer.predict(UpperCamelCase__					,  gpus=UpperCamelCase__					,  progress_bar=UpperCamelCase__)
						return {"mean_score": mean_score, "scores": scores}


