



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

from collections.abc import Callable
from typing import Generic, TypeVar

A     =			TypeVar("""T""")
A     =			TypeVar("""U""")
class 					a	(  Generic[T, U]       ):



  def __init__(						self :					Any       ,		lowerCAmelCase :					T | None       ,		lowerCAmelCase :					U | None		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=key
     SCREAMING_SNAKE_CASE_: int     			=val
     SCREAMING_SNAKE_CASE_: DoubleLinkedListNode[T, U] | None     			=None
     SCREAMING_SNAKE_CASE_: DoubleLinkedListNode[T, U] | None     			=None





  def __repr__(						self :					int		)     ->					Optional[int]:


     '''simple docstring'''




     return (
         f'''Node: key: {self.key}, val: {self.val}, '''
         f'''has next: {bool(self.next		)}, has prev: {bool(self.prev		)}'''
     )







class 					a	(  Generic[T, U]       ):



  def __init__(						self :					Any		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: DoubleLinkedListNode[T, U]     			=DoubleLinkedListNode(__A       ,		__A		)
     SCREAMING_SNAKE_CASE_: DoubleLinkedListNode[T, U]     			=DoubleLinkedListNode(__A       ,		__A		)
     SCREAMING_SNAKE_CASE_: List[str]     			=self.rear, self.head



  def __repr__(						self :					Tuple		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=["DoubleLinkedList"]
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.head
     while node.next is not None:
        rep.append(str(__A		)		)
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=node.next
     rep.append(str(self.rear		)		)
     return ",\n    ".join(__A		)



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					DoubleLinkedListNode[T, U]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.rear.prev

     # All nodes other than self.head are guaranteed to have non-None previous
     assert previous is not None

     SCREAMING_SNAKE_CASE_: Dict     			=node
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=previous
     SCREAMING_SNAKE_CASE_: int     			=node
     SCREAMING_SNAKE_CASE_: int     			=self.rear





  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					DoubleLinkedListNode[T, U]		)     ->					Optional[Any]:


     '''simple docstring'''




     if node.prev is None or node.next is None:
        return None

     SCREAMING_SNAKE_CASE_: List[Any]     			=node.next
     SCREAMING_SNAKE_CASE_: Any     			=node.prev
     SCREAMING_SNAKE_CASE_: Any     			=None
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=None
     return node







class 					a	(  Generic[T, U]       ):
  UpperCamelCase	:				Optional[Any]     						=					{}



  def __init__(						self :					str       ,		lowerCAmelCase :					int		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: DoubleLinkedList[T, U]     			=DoubleLinkedList()
     SCREAMING_SNAKE_CASE_: Dict     			=capacity
     SCREAMING_SNAKE_CASE_: List[Any]     			=0
     SCREAMING_SNAKE_CASE_: int     			=0
     SCREAMING_SNAKE_CASE_: Optional[int]     			=0
     SCREAMING_SNAKE_CASE_: dict[T, DoubleLinkedListNode[T, U]]     			={}



  def __repr__(						self :					Any		)     ->					Any:


     '''simple docstring'''




     return (
         f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
         f'''capacity={self.capacity}, current size={self.num_keys})'''
     )



  def __contains__(						self :					Union[str, Any]       ,		lowerCAmelCase :					T		)     ->					Any:


     '''simple docstring'''




     return key in self.cache



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


     '''simple docstring'''




     if key in self.cache:
        self.hits += 1
        SCREAMING_SNAKE_CASE_: DoubleLinkedListNode[T, U]     			=self.cache[key]
        SCREAMING_SNAKE_CASE_: int     			=self.list.remove(self.cache[key]		)
        assert node == value_node

        # node is guaranteed not None because it is in self.cache
        assert node is not None
        self.list.add(__A		)
        return node.val
     self.miss += 1
     return None



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					T       ,		lowerCAmelCase :					U		)     ->					str:


     '''simple docstring'''




     if key not in self.cache:
        if self.num_keys >= self.capacity:
           # delete first node (oldest) when over capacity
           SCREAMING_SNAKE_CASE_: Dict     			=self.list.head.next

           # guaranteed to have a non-None first node when num_keys > 0
           # explain to type checker via assertions
           assert first_node is not None
           assert first_node.key is not None
           assert (
               self.list.remove(__A		) is not None
           )  # node guaranteed to be in list assert node.key is not None

           del self.cache[first_node.key]
           self.num_keys -= 1
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=DoubleLinkedListNode(__A       ,		__A		)
        self.list.add(self.cache[key]		)
        self.num_keys += 1

     else:
        # bump node to the end of the list, update value
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.list.remove(self.cache[key]		)
        assert node is not None  # node guaranteed to be in list
        SCREAMING_SNAKE_CASE_: List[Any]     			=value
        self.list.add(__A		)





  @classmethod
  def 	lowerCamelCase__       (						cls :					Tuple       ,		lowerCAmelCase :					int = 128		)     ->					str:


     '''simple docstring'''




     def cache_decorator_inner(lowerCAmelCase :					Callable[[T], U]		) -> Callable[..., U]:
        def cache_decorator_wrapper(*lowerCAmelCase :					T		) -> U:
           if func not in cls.decorator_function_to_instance_map:
              SCREAMING_SNAKE_CASE_: Tuple     			=LRUCache(__A		)

           SCREAMING_SNAKE_CASE_: str     			=cls.decorator_function_to_instance_map[func].get(args[0]		)
           if result is None:
              SCREAMING_SNAKE_CASE_: Optional[int]     			=func(*__A		)
              cls.decorator_function_to_instance_map[func].put(args[0]       ,		__A		)
           return result

        def cache_info() -> LRUCache[T, U]:
           return cls.decorator_function_to_instance_map[func]

        setattr(__A       ,		"""cache_info"""       ,		__A		)  # noqa: B010

        return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()








"""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,
   )



