



"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase     =			logging.get_logger("""transformers.models.speecht5""")





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint[f'''upsamples.{i}.1.bias''']

   for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
      for j in range(len(config.resblock_dilation_sizes ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: List[Any]     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""output_conv.1.weight_g"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint["""output_conv.1.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()

   model.save_pretrained(lowercase )

   if repo_id:
      print("""Pushing to the hub...""" )
      model.push_to_hub(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
   parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )








"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 					a	(  yaml.SafeLoader       ):



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE_: Any     			=[tuple(lowerCAmelCase		) if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) else key for key in keys]
     SCREAMING_SNAKE_CASE_: Dict     			=Counter(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[key for key in counter if counter[key] > 1]
     if duplicate_keys:
        raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''		)





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]=False		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=super().construct_mapping(lowerCAmelCase       ,		deep=lowerCAmelCase		)
     self._check_no_duplicates_on_constructed_node(lowerCAmelCase		)
     return mapping







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=list(readme_content.splitlines() )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=full_content[1:].index("""---""" ) + 1
      SCREAMING_SNAKE_CASE_: List[str]     			="""\n""".join(full_content[1:sep_idx] )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

   return None, "\n".join(lowercase )
class 					a	(  UpperCAmelCase__       ):
  # class attributes
  UpperCamelCase	:				Tuple     						=					{'train_eval_index'}  # train-eval-index in the YAML metadata



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					Path		)     ->					"DatasetMetadata":


     '''simple docstring'''




     with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_split_yaml_from_readme(readme_file.read()		)
     if yaml_string is not None:
        return cls.from_yaml_string(lowerCAmelCase		)
     else:
        return cls()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Path		)     ->					List[str]:


     '''simple docstring'''




     if path.exists():
        with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
           SCREAMING_SNAKE_CASE_: str     			=readme_file.read()
     else:
        SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=self._to_readme(lowerCAmelCase		)
     with open(lowerCAmelCase       ,		"""w"""       ,		encoding="""utf-8"""		) as readme_file:
        readme_file.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[str] = None		)     ->					str:


     '''simple docstring'''




     if readme_content is not None:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=_split_yaml_from_readme(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[str]     			="""---\n""" + self.to_yaml_string() + """---\n""" + content
     else:
        SCREAMING_SNAKE_CASE_: List[Any]     			="""---\n""" + self.to_yaml_string() + """---\n"""
     return full_content



  @classmethod
  def 	lowerCamelCase__       (						cls :					Optional[int]       ,		lowerCAmelCase :					str		)     ->					"DatasetMetadata":


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=yaml.load(lowerCAmelCase       ,		Loader=_NoDuplicateSafeLoader		) or {}

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





  def 	lowerCamelCase__       (						self :					Dict		)     ->					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=lowerCAmelCase       ,		allow_unicode=lowerCAmelCase       ,		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)



