





from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class     lowercase__       (	yaml.SafeLoader				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      [self.constructed_objects[key_node] for key_node, _ in node.value]
       UpperCamelCase__							:				Dict          =      [tuple(__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else key for key in keys]
       UpperCamelCase__							:				Tuple          =      Counter(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      [key for key in counter if counter[key] > 1]
       if duplicate_keys:
           raise TypeError(f"Got duplicate yaml keys: {duplicate_keys}"							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=False							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      super().construct_mapping(__magic_name__,		deep=__magic_name__							)
       self._check_no_duplicates_on_constructed_node(__magic_name__							)
       return mapping




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str      )      ->	Tuple[Optional[str], str]:
    UpperCamelCase__							:				str          =      list(readme_content.splitlines()      )
    if full_content and full_content[0] == "---" and "---" in full_content[1:]:
        UpperCamelCase__							:				str          =      full_content[1:].index('''---'''      ) + 1
        UpperCamelCase__							:				List[Any]          =      '''\n'''.join(full_content[1:sep_idx]      )
        return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

    return None, "\n".join(__UpperCAmelCase      )






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		{"train_eval_index"}  # train-eval-index in the YAML metadata




   @classmethod
   def       UpperCamelCase__  (    cls,		__magic_name__							) ->      "DatasetMetadata":






       """simple docstring"""
       with open(__magic_name__,		encoding='''utf-8'''							) as readme_file:
           UpperCamelCase__   ,UpperCamelCase__							:				Tuple          =      _split_yaml_from_readme(readme_file.read()							)
       if yaml_string is not None:
           return cls.from_yaml_string(__magic_name__							)
       else:
           return cls()




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Dict:






       """simple docstring"""
       if path.exists():
           with open(__magic_name__,		encoding='''utf-8'''							) as readme_file:
               UpperCamelCase__							:				Optional[Any]          =      readme_file.read()
       else:
           UpperCamelCase__							:				Dict          =      None
       UpperCamelCase__							:				Union[str, Any]          =      self._to_readme(__magic_name__							)
       with open(__magic_name__,		'''w''',		encoding='''utf-8'''							) as readme_file:
           readme_file.write(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__ = None							) ->      str:






       """simple docstring"""
       if readme_content is not None:
           UpperCamelCase__   ,UpperCamelCase__							:				Tuple          =      _split_yaml_from_readme(__magic_name__							)
           UpperCamelCase__							:				int          =      '''---\n''' + self.to_yaml_string() + '''---\n''' + content
       else:
           UpperCamelCase__							:				Union[str, Any]          =      '''---\n''' + self.to_yaml_string() + '''---\n'''
       return full_content




   @classmethod
   def       UpperCamelCase__  (    cls,		__magic_name__							) ->      "DatasetMetadata":






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      yaml.load(__magic_name__,		Loader=_NoDuplicateSafeLoader							) or {}

       # Convert the YAML keys to DatasetMetadata fields
       UpperCamelCase__							:				Optional[Any]          =      {
           (key.replace('''-''',		'''_'''							) if key.replace('''-''',		'''_'''							) in cls._FIELDS_WITH_DASHES else key): value
           for key, value in metadata_dict.items()
       }
       return cls(**__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       return yaml.safe_dump(
           {
               (key.replace('''_''',		'''-'''							) if key in self._FIELDS_WITH_DASHES else key): value
               for key, value in self.items()
           },		sort_keys=__magic_name__,		allow_unicode=__magic_name__,		encoding='''utf-8''',		).decode('''utf-8'''							)




UpperCAmelCase_     							= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
  from argparse import ArgumentParser

  UpperCAmelCase_     							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
  ap.add_argument('readme_filepath')
  UpperCAmelCase_     							= ap.parse_args()

  UpperCAmelCase_     							= Path(args.readme_filepath)
  UpperCAmelCase_     							= DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)








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


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

UpperCAmelCase_     							= 256






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		["melgan"]




   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      None:






       """simple docstring"""
       super().__init__()

       # From MELGAN
       UpperCamelCase__							:				Optional[int]          =      math.log(1E-5							)  # Matches MelGAN training.
       UpperCamelCase__							:				int          =      4.0  # Largest value for most examples
       UpperCamelCase__							:				Optional[int]          =      128

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Any:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				str          =      output_range
       if clip:
           UpperCamelCase__							:				Union[str, Any]          =      torch.clip(__magic_name__,		self.min_value,		self.max_value							)
       # Scale to [0, 1].
       UpperCamelCase__							:				List[str]          =      (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,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      input_range
       UpperCamelCase__							:				Any          =      torch.clip(__magic_name__,		__magic_name__,		__magic_name__							) if clip else outputs
       # Scale to [0, 1].
       UpperCamelCase__							:				Any          =      (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,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      input_tokens > 0
       UpperCamelCase__   ,UpperCamelCase__							:				Any          =      self.notes_encoder(
           encoder_input_tokens=__magic_name__,		encoder_inputs_mask=__magic_name__							)

       UpperCamelCase__   ,UpperCamelCase__							:				Optional[Any]          =      self.continuous_encoder(
           encoder_inputs=__magic_name__,		encoder_inputs_mask=__magic_name__							)

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      noise_time
       if not torch.is_tensor(__magic_name__							):
           UpperCamelCase__							:				Tuple          =      torch.tensor([timesteps],		dtype=torch.long,		device=input_tokens.device							)
       elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
           UpperCamelCase__							:				Union[str, Any]          =      timesteps[None].to(input_tokens.device							)

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

       UpperCamelCase__							:				List[str]          =      self.decoder(
           encodings_and_masks=__magic_name__,		decoder_input_tokens=__magic_name__,		decoder_noise_time=__magic_name__							)
       return logits




   @torch.no_grad()
   def __call__(    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = 100,		__magic_name__ = True,		__magic_name__ = "numpy",		__magic_name__ = None,		__magic_name__ = 1,		) ->      Union[AudioPipelineOutput, Tuple]:






       """simple docstring"""
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__magic_name__,		__magic_name__							) or callback_steps <= 0)
       ):
           raise ValueError(
               f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
               f" {type(__magic_name__							)}."							)

       UpperCamelCase__							:				Dict          =      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],		dtype=np.floataa							)
       UpperCamelCase__							:				Tuple          =      np.zeros([1, 0, self.n_dims],		np.floataa							)
       UpperCamelCase__							:				List[Any]          =      torch.ones((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		device=self.device							)

       for i, encoder_input_tokens in enumerate(__magic_name__							):
           if i == 0:
               UpperCamelCase__							:				str          =      torch.from_numpy(pred_mel[:1].copy()							).to(
                   device=self.device,		dtype=self.decoder.dtype							)
               # The first chunk has no previous context.
               UpperCamelCase__							:				Any          =      torch.zeros((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		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.
               UpperCamelCase__							:				List[str]          =      ones

           UpperCamelCase__							:				int          =      self.scale_features(
               __magic_name__,		output_range=[-1.0, 1.0],		clip=__magic_name__							)

           UpperCamelCase__							:				Union[str, Any]          =      self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							),		continuous_inputs=__magic_name__,		continuous_mask=__magic_name__,		)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           UpperCamelCase__							:				Optional[int]          =      randn_tensor(
               shape=encoder_continuous_inputs.shape,		generator=__magic_name__,		device=self.device,		dtype=self.decoder.dtype,		)

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

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
               UpperCamelCase__							:				Union[str, Any]          =      self.decode(
                   encodings_and_masks=__magic_name__,		input_tokens=__magic_name__,		noise_time=t / self.scheduler.config.num_train_timesteps,		)

               # Compute previous output: x_t -> x_t-1
               UpperCamelCase__							:				List[Any]          =      self.scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		generator=__magic_name__							).prev_sample

           UpperCamelCase__							:				List[Any]          =      self.scale_to_features(__magic_name__,		input_range=[-1.0, 1.0]							)
           UpperCamelCase__							:				List[Any]          =      mel[:1]
           UpperCamelCase__							:				int          =      mel.cpu().float().numpy()

           UpperCamelCase__							:				Union[str, Any]          =      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(__magic_name__,		__magic_name__							)

           logger.info('''Generated segment''',		__magic_name__							)

       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":
           UpperCamelCase__							:				Optional[int]          =      self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
       else:
           UpperCamelCase__							:				Any          =      full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=__magic_name__							)

