



"""simple docstring"""
from __future__ import annotations

import math
class 					a	:



  def __init__(						self :					List[str]       ,		lowerCAmelCase :					int		)     ->					None:


     '''simple docstring'''




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



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


     '''simple docstring'''




     return idx * 2



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


     '''simple docstring'''




     return idx * 2 + 1



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					list[int]		)     ->					None:


     '''simple docstring'''




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



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


     '''simple docstring'''




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

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



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int		)     ->					int | float:


     '''simple docstring'''




     if self.flag[idx] is True:
        SCREAMING_SNAKE_CASE_: List[str]     			=self.lazy[idx]
        SCREAMING_SNAKE_CASE_: Dict     			=False
        if left_element != right_element:
           SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.lazy[idx]
           SCREAMING_SNAKE_CASE_: Any     			=self.lazy[idx]
           SCREAMING_SNAKE_CASE_: List[str]     			=True
           SCREAMING_SNAKE_CASE_: 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]
     SCREAMING_SNAKE_CASE_: Dict     			=(left_element + right_element) // 2
     SCREAMING_SNAKE_CASE_: List[str]     			=self.query(self.left(lowerCAmelCase		)       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=self.query(self.right(lowerCAmelCase		)       ,		mid + 1       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
     return max(lowerCAmelCase       ,		lowerCAmelCase		)





  def __str__(						self :					List[str]		)     ->					str:


     '''simple docstring'''




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


if __name__ == "__main__":
   _UpperCAmelCase     =			[1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
   _UpperCAmelCase     =			1_5
   _UpperCAmelCase     =			SegmentTree(size)
   segt.build(1, 1, size, A)
   print(segt.query(1, 1, size, 4, 6))
   print(segt.query(1, 1, size, 7, 1_1))
   print(segt.query(1, 1, size, 7, 1_2))
   segt.update(1, 1, size, 1, 3, 1_1_1)
   print(segt.query(1, 1, size, 1, 1_5))
   segt.update(1, 1, size, 7, 8, 2_3_5)
   print(segt)








"""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,
   )



