






'''simple docstring'''

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__     =       TypeVar('KEY')
SCREAMING_SNAKE_CASE__     =       TypeVar('VAL')






@dataclass(frozen=lowerCamelCase     ,	slots=lowerCamelCase    )
class     a_						(      Generic[KEY, VAL]    ):
  lowercase							=				42
  lowercase							=				42






class     a_						(      _Item    ):

  def __init__(				self				)  ->				None:



        """simple docstring"""






        super().__init__(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)


  def __bool__(				self				)  ->				bool:



        """simple docstring"""






        return False


SCREAMING_SNAKE_CASE__     =       _DeletedItem()






class     a_						(      MutableMapping[KEY, VAL]    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE = 8      ,		_SCREAMING_SNAKE_CASE = 0.7_5				)  ->				None:



        """simple docstring"""






        UpperCamelCase      = initial_block_size
        UpperCamelCase      = [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        UpperCamelCase      = capacity_factor
        UpperCamelCase      = 0

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return hash(_SCREAMING_SNAKE_CASE				) % len(self._buckets				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return (ind + 1) % len(self._buckets				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				bool:



        """simple docstring"""






        UpperCamelCase      = self._buckets[ind]
        if not stored:
              UpperCamelCase      = _Item(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
              self._len += 1
              return True
        elif stored.key == key:
              UpperCamelCase      = _Item(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
              return True
        else:
              return False

  def   A__						(				self				)  ->				bool:



        """simple docstring"""






        UpperCamelCase      = len(self._buckets				) * self._capacity_factor
        return len(self				) >= int(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				bool:



        """simple docstring"""






        if len(self._buckets				) <= self._initial_block_size:
              return False
        UpperCamelCase      = len(self._buckets				) * self._capacity_factor / 2
        return len(self				) < limit

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				None:



        """simple docstring"""






        UpperCamelCase      = self._buckets
        UpperCamelCase      = [None] * new_size
        UpperCamelCase      = 0
        for item in old_buckets:
              if item:
                    self._add_item(item.key      ,		item.val				)

  def   A__						(				self				)  ->				None:



        """simple docstring"""






        self._resize(len(self._buckets				) * 2				)

  def   A__						(				self				)  ->				None:



        """simple docstring"""






        self._resize(len(self._buckets				) // 2				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Iterator[int]:



        """simple docstring"""






        UpperCamelCase      = self._get_bucket_index(_SCREAMING_SNAKE_CASE				)
        for _ in range(len(self._buckets				)				):
              yield ind
              UpperCamelCase      = self._get_next_ind(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				None:



        """simple docstring"""






        for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE				):
              if self._try_set(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
                    break

  def __setitem__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				None:



        """simple docstring"""






        if self._is_full():
              self._size_up()

        self._add_item(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def __delitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				None:



        """simple docstring"""






        for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = self._buckets[ind]
              if item is None:
                    raise KeyError(_SCREAMING_SNAKE_CASE				)
              if item is _deleted:
                    continue
              if item.key == key:
                    UpperCamelCase      = _deleted
                    self._len -= 1
                    break
        if self._is_sparse():
              self._size_down()

  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				VAL:



        """simple docstring"""






        for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = self._buckets[ind]
              if item is None:
                    break
              if item is _deleted:
                    continue
              if item.key == key:
                    return item.val
        raise KeyError(_SCREAMING_SNAKE_CASE				)

  def __len__(				self				)  ->				int:



        """simple docstring"""






        return self._len

  def __iter__(				self				)  ->				Iterator[KEY]:



        """simple docstring"""






        yield from (item.key for item in self._buckets if item)


  def __repr__(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = """ ,""".join(
            F"{item.key}: {item.val}" for item in self._buckets if item				)
        return F"HashMap({val_string})"








'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger('transformers.models.speecht5')







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			List[Any]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaHifiGanConfig()

      UpperCamelCase      = SpeechTaHifiGan(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      load_weights(orig_checkpoint["""model"""]["""generator"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

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

      model.save_pretrained(__UpperCamelCase   )

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


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

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