


'''simple docstring'''




import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
       from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__snake_case       		=logging.get_logger(__name__)  # pylint: disable=invalid-name

__snake_case       		=256

class 						UpperCAmelCase_ (    __snake_case						):
     lowerCamelCase							:      Dict            =	['''melgan''']





     def __init__(					self			: str       , UpperCAmelCase__			: SpectrogramNotesEncoder       , UpperCAmelCase__			: SpectrogramContEncoder       , UpperCAmelCase__			: TaFilmDecoder       , UpperCAmelCase__			: DDPMScheduler       , UpperCAmelCase__			: OnnxRuntimeModel if is_onnx_available() else Any       , )							->    Tuple:
        super().__init__()

        # From MELGAN
        lowerCAmelCase	=     math.log(1E-5       )  # Matches MelGAN training.
        lowerCAmelCase	=     4.0  # Largest value for most examples
        lowerCAmelCase	=     1_2_8

        self.register_modules(
            notes_encoder=_SCREAMING_SNAKE_CASE       , continuous_encoder=_SCREAMING_SNAKE_CASE       , decoder=_SCREAMING_SNAKE_CASE       , scheduler=_SCREAMING_SNAKE_CASE       , melgan=_SCREAMING_SNAKE_CASE       , )





     def 				__UpperCAmelCase (					self			: List[str]       , UpperCAmelCase__			: str       , UpperCAmelCase__			: Any=(-1.0, 1.0)       , UpperCAmelCase__			: Union[str, Any]=False       )							->    Dict:
        lowerCAmelCase	,    lowerCAmelCase	=     output_range
        if clip:
           lowerCAmelCase	=     torch.clip(_SCREAMING_SNAKE_CASE       , self.min_value       , self.max_value       )
        # Scale to [0, 1].
        lowerCAmelCase	=     (features - self.min_value) / (self.max_value - self.min_value)
        # Scale to [min_out, max_out].
        return zero_one * (max_out - min_out) + min_out





     def 				__UpperCAmelCase (					self			: Any       , UpperCAmelCase__			: List[Any]       , UpperCAmelCase__			: List[Any]=(-1.0, 1.0)       , UpperCAmelCase__			: str=False       )							->    Optional[int]:
        lowerCAmelCase	,    lowerCAmelCase	=     input_range
        lowerCAmelCase	=     torch.clip(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       ) if clip else outputs
        # Scale to [0, 1].
        lowerCAmelCase	=     (outputs - min_out) / (max_out - min_out)
        # Scale to [self.min_value, self.max_value].
        return zero_one * (self.max_value - self.min_value) + self.min_value





     def 				__UpperCAmelCase (					self			: Optional[Any]       , UpperCAmelCase__			: List[str]       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Dict       )							->    str:
        lowerCAmelCase	=     input_tokens > 0
        lowerCAmelCase	,    lowerCAmelCase	=     self.notes_encoder(
            encoder_input_tokens=_SCREAMING_SNAKE_CASE       , encoder_inputs_mask=_SCREAMING_SNAKE_CASE       )

        lowerCAmelCase	,    lowerCAmelCase	=     self.continuous_encoder(
            encoder_inputs=_SCREAMING_SNAKE_CASE       , encoder_inputs_mask=_SCREAMING_SNAKE_CASE       )

        return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]





     def 				__UpperCAmelCase (					self			: Union[str, Any]       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: List[Any]       , UpperCAmelCase__			: Union[str, Any]       )							->    Tuple:
        lowerCAmelCase	=     noise_time
        if not torch.is_tensor(_SCREAMING_SNAKE_CASE       ):
           lowerCAmelCase	=     torch.tensor([timesteps]       , dtype=torch.long       , device=input_tokens.device       )
        elif torch.is_tensor(_SCREAMING_SNAKE_CASE       ) and len(timesteps.shape       ) == 0:
           lowerCAmelCase	=     timesteps[None].to(input_tokens.device       )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        lowerCAmelCase	=     timesteps * torch.ones(input_tokens.shape[0]       , dtype=timesteps.dtype       , device=timesteps.device       )

        lowerCAmelCase	=     self.decoder(
            encodings_and_masks=_SCREAMING_SNAKE_CASE       , decoder_input_tokens=_SCREAMING_SNAKE_CASE       , decoder_noise_time=_SCREAMING_SNAKE_CASE       )
        return logits





     @torch.no_grad()
     def __call__(					self			: Any       , UpperCAmelCase__			: List[List[int]]       , UpperCAmelCase__			: Optional[torch.Generator] = None       , UpperCAmelCase__			: int = 1_0_0       , UpperCAmelCase__			: bool = True       , UpperCAmelCase__			: str = "numpy"       , UpperCAmelCase__			: Optional[Callable[[int, int, torch.FloatTensor], None]] = None       , UpperCAmelCase__			: int = 1       , )							->    str:




        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       ) or callback_steps <= 0)
        ):
           raise ValueError(
               F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               F''' {type(_SCREAMING_SNAKE_CASE       )}.'''       )

        lowerCAmelCase	=     np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]       , dtype=np.floataa       )
        lowerCAmelCase	=     np.zeros([1, 0, self.n_dims]       , np.floataa       )
        lowerCAmelCase	=     torch.ones((1, TARGET_FEATURE_LENGTH)       , dtype=_SCREAMING_SNAKE_CASE       , device=self.device       )

        for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE       ):
           if i == 0:
              lowerCAmelCase	=     torch.from_numpy(pred_mel[:1].copy()       ).to(
                  device=self.device       , dtype=self.decoder.dtype       )
              # The first chunk has no previous context.
              lowerCAmelCase	=     torch.zeros((1, TARGET_FEATURE_LENGTH)       , dtype=_SCREAMING_SNAKE_CASE       , device=self.device       )
           else:
              # The full song pipeline does not feed in a context feature, so the mask
              # will be all 0s after the feature converter. Because we know we're
              # feeding in a full context chunk from the previous prediction, set it
              # to all 1s.
              lowerCAmelCase	=     ones

           lowerCAmelCase	=     self.scale_features(
               _SCREAMING_SNAKE_CASE       , output_range=[-1.0, 1.0]       , clip=_SCREAMING_SNAKE_CASE       )

           lowerCAmelCase	=     self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]       ).to(device=self.device       )       , continuous_inputs=_SCREAMING_SNAKE_CASE       , continuous_mask=_SCREAMING_SNAKE_CASE       , )

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           lowerCAmelCase	=     randn_tensor(
               shape=encoder_continuous_inputs.shape       , generator=_SCREAMING_SNAKE_CASE       , device=self.device       , dtype=self.decoder.dtype       , )

           # set step values
           self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE       )

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
              lowerCAmelCase	=     self.decode(
                  encodings_and_masks=_SCREAMING_SNAKE_CASE       , input_tokens=_SCREAMING_SNAKE_CASE       , noise_time=t / self.scheduler.config.num_train_timesteps       , )

              # Compute previous output: x_t -> x_t-1
              lowerCAmelCase	=     self.scheduler.step(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       ).prev_sample

           lowerCAmelCase	=     self.scale_to_features(_SCREAMING_SNAKE_CASE       , input_range=[-1.0, 1.0]       )
           lowerCAmelCase	=     mel[:1]
           lowerCAmelCase	=     mel.cpu().float().numpy()

           lowerCAmelCase	=     np.concatenate([full_pred_mel, pred_mel[:1]]       , axis=1       )

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
              callback(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       )

           logger.info('Generated segment'       , _SCREAMING_SNAKE_CASE       )

        if output_type == "numpy" and not is_onnx_available():
           raise ValueError(
               'Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'       )
        elif output_type == "numpy" and self.melgan is None:
           raise ValueError(
               'Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'       )

        if output_type == "numpy":
           lowerCAmelCase	=     self.melgan(input_features=full_pred_mel.astype(np.floataa       )       )
        else:
           lowerCAmelCase	=     full_pred_mel

        if not return_dict:
           return (output,)

        return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE       )













from datetime import datetime as dt
import os

from github import Github


__magic_name__   :  Dict        =							[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def 		lowercase__  (				) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  Github(os.environ['GITHUB_TOKEN'])
      UpperCamelCase					       =  g.get_repo('huggingface/transformers')
      UpperCamelCase					       =  repo.get_issues(state='open')

      for issue in open_issues:
            UpperCamelCase					       =  sorted([comment for comment in issue.get_comments()]       ,							key=lambda _UpperCamelCase: i.created_at       ,							reverse=_UpperCamelCase)
            UpperCamelCase					       =  comments[0] if len(_UpperCamelCase) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='closed')
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      'This issue has been automatically marked as stale because it has not had '
                      'recent activity. If you think this still needs to be addressed '
                      'please comment on this thread.\n\nPlease note that issues that do not follow the '
                      '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
                      'are likely to be ignored.')


if __name__ == "__main__":
       main()



