





'''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 							__lowercase       (      lowerCAmelCase__						):







 '''simple docstring'''

 a    :				Dict											=       ["vqvae"]


 def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							Tuple:







    '''simple docstring'''





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


 def 						_UpperCAmelCase     (self )							->							int:







    '''simple docstring'''





    return 50 if isinstance(self.scheduler     ,_lowerCamelCase ) else 1000


 @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     ,)							->							Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:







    '''simple docstring'''





    __lowercase										=							steps or self.get_default_steps()
    self.scheduler.set_timesteps(_lowerCamelCase )
    __lowercase										=							step_generator or generator
    # For backwards compatibility
    if type(self.unet.config.sample_size ) == int:
       __lowercase										=							(self.unet.config.sample_size, self.unet.config.sample_size)
    if noise is None:
       __lowercase										=							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     ,)
    __lowercase										=							noise
    __lowercase										=							None

    if audio_file is not None or raw_audio is not None:
       self.mel.load_audio(_lowerCamelCase     ,_lowerCamelCase )
       __lowercase										=							self.mel.audio_slice_to_image(_lowerCamelCase )
       __lowercase										=							np.frombuffer(input_image.tobytes()     ,dtype='''uint8''' ).reshape(
           (input_image.height, input_image.width) )
       __lowercase										=							(input_image / 255) * 2 - 1
       __lowercase										=							torch.tensor(input_image[np.newaxis, :, :]     ,dtype=torch.float ).to(self.device )

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

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

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

       if isinstance(self.scheduler     ,_lowerCamelCase ):
          __lowercase										=							self.scheduler.step(
              model_output=_lowerCamelCase     ,timestep=_lowerCamelCase     ,sample=_lowerCamelCase     ,eta=_lowerCamelCase     ,generator=_lowerCamelCase     ,)['''prev_sample''']
       else:
          __lowercase										=							self.scheduler.step(
              model_output=_lowerCamelCase     ,timestep=_lowerCamelCase     ,sample=_lowerCamelCase     ,generator=_lowerCamelCase     ,)['''prev_sample''']

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

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

    __lowercase										=							(images / 2 + 0.5).clamp(0     ,1 )
    __lowercase										=							images.cpu().permute(0     ,2     ,3     ,1 ).numpy()
    __lowercase										=							(images * 255).round().astype('''uint8''' )
    __lowercase										=							list(
        (Image.fromarray(_[:, :, 0] ) for _ in images)
        if images.shape[3] == 1
        else (Image.fromarray(_lowerCamelCase     ,mode='''RGB''' ).convert('''L''' ) for _ in images) )

    __lowercase										=							[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     ,_lowerCamelCase     ,_lowerCamelCase = 50 )							->							np.ndarray:







    '''simple docstring'''





    assert isinstance(self.scheduler     ,_lowerCamelCase )
    self.scheduler.set_timesteps(_lowerCamelCase )
    __lowercase										=							np.array(
        [np.frombuffer(image.tobytes()     ,dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
    __lowercase										=							(sample / 255) * 2 - 1
    __lowercase										=							torch.Tensor(_lowerCamelCase ).to(self.device )

    for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,(0,) ) ):
       __lowercase										=							t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
       __lowercase										=							self.scheduler.alphas_cumprod[t]
       __lowercase										=							(
           self.scheduler.alphas_cumprod[prev_timestep]
           if prev_timestep >= 0
           else self.scheduler.final_alpha_cumprod
       )
       __lowercase										=							1 - alpha_prod_t
       __lowercase										=							self.unet(_lowerCamelCase     ,_lowerCamelCase )['''sample''']
       __lowercase										=							(1 - alpha_prod_t_prev) ** 0.5 * model_output
       __lowercase										=							(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
       __lowercase										=							sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

    return sample


 @staticmethod
 def 						_UpperCAmelCase     (_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							torch.Tensor:







    '''simple docstring'''





    __lowercase										=							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'''



from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_SCREAMING_SNAKE_CASE					=    '''\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
'''

_SCREAMING_SNAKE_CASE					=    '''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

_SCREAMING_SNAKE_CASE					=    '''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''




def    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  int				):
			return float((preds == labels).mean()				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str				):
			__lowercase										=							simple_accuracy(lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							float(fa_score(y_true=lowerCamelCase_		,       y_pred=lowerCamelCase_				)				)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Any				):
			__lowercase										=							float(pearsonr(lowerCamelCase_		,       lowerCamelCase_				)[0]				)
			__lowercase										=							float(spearmanr(lowerCamelCase_		,       lowerCamelCase_				)[0]				)
			return {
			    "pearson": pearson_corr,
			    "spearmanr": spearman_corr,
			}






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







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				if self.config_name not in [
				    "sst2",
				    "mnli",
				    "mnli_mismatched",
				    "mnli_matched",
				    "cola",
				    "stsb",
				    "mrpc",
				    "qqp",
				    "qnli",
				    "rte",
				    "wnli",
				    "hans",
				]:
							raise KeyError(
							    '''You should supply a configuration name selected in '''
							    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
							    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]''' )
				return datasets.MetricInfo(
				    description=_DESCRIPTION     ,citation=_CITATION     ,inputs_description=_KWARGS_DESCRIPTION     ,features=datasets.Features(
				        {
				            '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32''' ),
				            '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32''' ),
				        } )     ,codebase_urls=[]     ,reference_urls=[]     ,format='''numpy'''     ,)


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				if self.config_name == "cola":
							return {"matthews_correlation": matthews_corrcoef(_lowerCamelCase     ,_lowerCamelCase )}
				elif self.config_name == "stsb":
							return pearson_and_spearman(_lowerCamelCase     ,_lowerCamelCase )
				elif self.config_name in ["mrpc", "qqp"]:
							return acc_and_fa(_lowerCamelCase     ,_lowerCamelCase )
				elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
							return {"accuracy": simple_accuracy(_lowerCamelCase     ,_lowerCamelCase )}
				else:
							raise KeyError(
							    '''You should supply a configuration name selected in '''
							    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
							    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]''' )





