


"""simple docstring"""







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase__      				=   logging.get_logger('''transformers.models.speecht5''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  hf_model.apply_weight_norm()

  lowerCAmelCase     :     List[str]								=							checkpoint["input_conv.weight_g"]
  lowerCAmelCase     :     List[Any]								=							checkpoint["input_conv.weight_v"]
  lowerCAmelCase     :     List[str]								=							checkpoint["input_conv.bias"]

  for i in range(len(config.upsample_rates				)				):
    lowerCAmelCase     :     Tuple								=							checkpoint[f"""upsamples.{i}.1.weight_g"""]
    lowerCAmelCase     :     Any								=							checkpoint[f"""upsamples.{i}.1.weight_v"""]
    lowerCAmelCase     :     Tuple								=							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				)				):
      lowerCAmelCase     :     List[str]								=							checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
      lowerCAmelCase     :     Tuple								=							checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
      lowerCAmelCase     :     int								=							checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

      lowerCAmelCase     :     Any								=							checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
      lowerCAmelCase     :     List[Any]								=							checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
      lowerCAmelCase     :     Any								=							checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

  lowerCAmelCase     :     Any								=							checkpoint["output_conv.1.weight_g"]
  lowerCAmelCase     :     str								=							checkpoint["output_conv.1.weight_v"]
  lowerCAmelCase     :     Any								=							checkpoint["output_conv.1.bias"]

  hf_model.remove_weight_norm()
@torch.no_grad()
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    Any=None				,      SCREAMING_SNAKE_CASE    :    List[str]=None				,      ):


  '''simple docstring'''







  if config_path is not None:
    lowerCAmelCase     :     Tuple								=							SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
  else:
    lowerCAmelCase     :     Optional[Any]								=							SpeechTaHifiGanConfig()

  lowerCAmelCase     :     List[str]								=							SpeechTaHifiGan(SCREAMING_SNAKE_CASE				)

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

  lowerCAmelCase     :     List[str]								=							np.load(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Any								=							stats[0].reshape(-1				)
  lowerCAmelCase     :     Union[str, Any]								=							stats[1].reshape(-1				)
  lowerCAmelCase     :     List[Any]								=							torch.from_numpy(SCREAMING_SNAKE_CASE				).float()
  lowerCAmelCase     :     Any								=							torch.from_numpy(SCREAMING_SNAKE_CASE				).float()

  model.save_pretrained(SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
     lowerCAmelCase__      				=   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.'''
     )

     lowerCAmelCase__      				=   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 __future__ import annotations

import math


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							size
         # approximate the overall size of segment tree with given value
         lowerCAmelCase     :     Optional[int]								=							[0 for i in range(0		,     4 * size						)]
         # create array to store lazy update
         lowerCAmelCase     :     List[str]								=							[0 for i in range(0		,     4 * size						)]
         lowerCAmelCase     :     Dict								=							[0 for i in range(0		,     4 * size						)]  # flag for lazy update

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2 + 1

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if left_element == right_element:
           lowerCAmelCase     :     List[str]								=							a[left_element - 1]
         else:
           lowerCAmelCase     :     Tuple								=							(left_element + right_element) // 2
           self.build(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__						)
           self.build(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__						)
           lowerCAmelCase     :     Tuple								=							max(
               self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     Optional[int]								=							self.lazy[idx]
           lowerCAmelCase     :     List[str]								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							True
             lowerCAmelCase     :     Optional[Any]								=							True

         if right_element < a or left_element > b:
           return True
         if left_element >= a and right_element <= b:
           lowerCAmelCase     :     str								=							val
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							val
             lowerCAmelCase     :     Union[str, Any]								=							val
             lowerCAmelCase     :     int								=							True
             lowerCAmelCase     :     int								=							True
           return True
         lowerCAmelCase     :     List[str]								=							(left_element + right_element) // 2
         self.update(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         self.update(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							max(
             self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)
         return True

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
           lowerCAmelCase     :     str								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Tuple								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     Optional[int]								=							True
             lowerCAmelCase     :     str								=							True
         if right_element < a or left_element > b:
           return -math.inf
         if left_element >= a and right_element <= b:
           return self.segment_tree[idx]
         lowerCAmelCase     :     Any								=							(left_element + right_element) // 2
         lowerCAmelCase     :     Optional[int]								=							self.query(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Dict								=							self.query(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__						)
         return max(snake_case__		,     snake_case__						)







       def __str__(						self						):






         """simple docstring"""





         return str([self.query(1		,     1		,     self.size		,     snake_case__		,     snake_case__						) for i in range(1		,     self.size + 1						)]						)


if __name__ == "__main__":
     lowerCAmelCase__      				=   [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
     lowerCAmelCase__      				=   15
     lowerCAmelCase__      				=   SegmentTree(size)
     segt.build(1, 1, size, A)
     print(segt.query(1, 1, size, 4, 6))
     print(segt.query(1, 1, size, 7, 11))
     print(segt.query(1, 1, size, 7, 12))
     segt.update(1, 1, size, 1, 3, 111)
     print(segt.query(1, 1, size, 1, 15))
     segt.update(1, 1, size, 7, 8, 235)
     print(segt)





