






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__a:     Optional[int]   					=					logging.get_logger('''transformers.models.speecht5''')




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Optional[Any]:
       hf_model.apply_weight_norm()

       _UpperCAmelCase							=    checkpoint["""input_conv.weight_g"""]
       _UpperCAmelCase							=    checkpoint["""input_conv.weight_v"""]
       _UpperCAmelCase							=    checkpoint["""input_conv.bias"""]

       for i in range(len(config.upsample_rates ) ):
              _UpperCAmelCase							=    checkpoint[f"""upsamples.{i}.1.weight_g"""]
              _UpperCAmelCase							=    checkpoint[f"""upsamples.{i}.1.weight_v"""]
              _UpperCAmelCase							=    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 ) ):
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

       _UpperCAmelCase							=    checkpoint["""output_conv.1.weight_g"""]
       _UpperCAmelCase							=    checkpoint["""output_conv.1.weight_v"""]
       _UpperCAmelCase							=    checkpoint["""output_conv.1.bias"""]

       hf_model.remove_weight_norm()




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=None   ,      __snake_case=None   ,      )				->						List[Any]:
       if config_path is not None:
              _UpperCAmelCase							=    SpeechTaHifiGanConfig.from_pretrained(__snake_case )
       else:
              _UpperCAmelCase							=    SpeechTaHifiGanConfig()

       _UpperCAmelCase							=    SpeechTaHifiGan(__snake_case )

       _UpperCAmelCase							=    torch.load(__snake_case )
       load_weights(orig_checkpoint["""model"""]["""generator"""]   ,      __snake_case   ,      __snake_case )

       _UpperCAmelCase							=    np.load(__snake_case )
       _UpperCAmelCase							=    stats[0].reshape(-1 )
       _UpperCAmelCase							=    stats[1].reshape(-1 )
       _UpperCAmelCase							=    torch.from_numpy(__snake_case ).float()
       _UpperCAmelCase							=    torch.from_numpy(__snake_case ).float()

       model.save_pretrained(__snake_case )

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


if __name__ == "__main__":
  __a:     List[str]   					=					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.'''
  )

  __a:     Dict   					=					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'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate






@dataclass(frozen=lowerCAmelCase__      )
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			field(default='''automatic-speech-recognition'''					,		metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''audio''': Audio()}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''transcription''': Value('''string'''      )}      )
			UpperCAmelCase_     :	str						=			"audio"
			UpperCAmelCase_     :	str						=			"transcription"



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if self.audio_column not in features:
							raise ValueError(f"Column {self.audio_column} is not present in features.")
					if not isinstance(features[self.audio_column]			,       __lowerCAmelCase):
							raise ValueError(f"Column {self.audio_column} is not an Audio type.")
					lowerCAmelCase     =    copy.deepcopy(self)
					lowerCAmelCase     =    self.input_schema.copy()
					lowerCAmelCase     =    features[self.audio_column]
					lowerCAmelCase     =    input_schema
					return task_template







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {self.audio_column: "audio", self.transcription_column: "transcription"}




