'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__a:			Optional[Any]	       =       logging.get_logger("""transformers.models.speecht5""")
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 hf_model.apply_weight_norm()

 lowercase__    :   str						= checkpoint['''input_conv.weight_g''']
 lowercase__    :   Optional[Any]						= checkpoint['''input_conv.weight_v''']
 lowercase__    :   Optional[int]						= checkpoint['''input_conv.bias''']

 for i in range(len(config.upsample_rates					)					):
  lowercase__    :   Optional[Any]						= checkpoint[F"""upsamples.{i}.1.weight_g"""]
  lowercase__    :   List[Any]						= checkpoint[F"""upsamples.{i}.1.weight_v"""]
  lowercase__    :   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					)					):
   lowercase__    :   int						= checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   lowercase__    :   Any						= checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   lowercase__    :   Dict						= checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   lowercase__    :   Optional[Any]						= checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   lowercase__    :   Optional[Any]						= checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   lowercase__    :   List[str]						= checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 lowercase__    :   str						= checkpoint['''output_conv.1.weight_g''']
 lowercase__    :   Optional[int]						= checkpoint['''output_conv.1.weight_v''']
 lowercase__    :   str						= checkpoint['''output_conv.1.bias''']

 hf_model.remove_weight_norm()
@torch.no_grad()
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=None		,					UpperCAmelCase=None		,					):
 if config_path is not None:
  lowercase__    :   Tuple						= SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase					)
 else:
  lowercase__    :   Union[str, Any]						= SpeechTaHifiGanConfig()

 lowercase__    :   str						= SpeechTaHifiGan(UpperCAmelCase					)

 lowercase__    :   Tuple						= torch.load(UpperCAmelCase					)
 load_weights(orig_checkpoint['''model''']['''generator''']		,					UpperCAmelCase		,					UpperCAmelCase					)

 lowercase__    :   List[Any]						= np.load(UpperCAmelCase					)
 lowercase__    :   Optional[int]						= stats[0].reshape(-1					)
 lowercase__    :   Optional[Any]						= stats[1].reshape(-1					)
 lowercase__    :   List[str]						= torch.from_numpy(UpperCAmelCase					).float()
 lowercase__    :   Optional[Any]						= 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__":
  __a:			Dict	       =       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:			Optional[Any]	       =       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 manim import *



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   int						= Rectangle(height=0.5     ,       width=0.5			)
        lowercase__    :   Optional[int]						= Rectangle(height=0.2_5     ,       width=0.2_5			)
        lowercase__    :   Tuple						= Rectangle(height=0.4_6     ,       width=0.4_6			).set_stroke(width=0			)

        lowercase__    :   str						= [mem.copy() for i in range(6			)]
        lowercase__    :   Dict						= [mem.copy() for i in range(6			)]
        lowercase__    :   Tuple						= VGroup(*__lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)
        lowercase__    :   List[str]						= VGroup(*__lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)
        lowercase__    :   str						= VGroup(__lowerCAmelCase     ,       __lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)
        lowercase__    :   str						= Text('''CPU'''     ,       font_size=24			)
        lowercase__    :   List[Any]						= Group(__lowerCAmelCase     ,       __lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0.5     ,       aligned_edge=__lowerCAmelCase			)
        cpu.move_to([-2.5, -0.5, 0]			)
        self.add(__lowerCAmelCase			)

        lowercase__    :   Any						= [mem.copy() for i in range(4			)]
        lowercase__    :   int						= VGroup(*__lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)
        lowercase__    :   Optional[int]						= Text('''GPU'''     ,       font_size=24			)
        lowercase__    :   Tuple						= Group(__lowerCAmelCase     ,       __lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0.5     ,       aligned_edge=__lowerCAmelCase			)
        gpu.move_to([-1, -1, 0]			)
        self.add(__lowerCAmelCase			)

        lowercase__    :   int						= [mem.copy() for i in range(6			)]
        lowercase__    :   Optional[int]						= VGroup(*__lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)

        lowercase__    :   Any						= Text('''Model'''     ,       font_size=24			)
        lowercase__    :   Optional[Any]						= Group(__lowerCAmelCase     ,       __lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0.5     ,       aligned_edge=__lowerCAmelCase			)
        model.move_to([3, -1.0, 0]			)
        self.add(__lowerCAmelCase			)

        lowercase__    :   int						= []
        lowercase__    :   int						= []
        lowercase__    :   Any						= []

        for i, rect in enumerate(__lowerCAmelCase			):
         rect.set_stroke(__lowerCAmelCase			)

         lowercase__    :   Optional[int]						= Rectangle(height=0.4_6 / 4     ,       width=0.4_6 / 3			).set_stroke(width=0.0			).set_fill(__lowerCAmelCase     ,       opacity=0.7			)

         if i == 0:
          cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT			)     ,       buff=0.0_2     ,       direction=__lowerCAmelCase			)
          cpu_target.set_x(cpu_target.get_x() + 0.1			)
         elif i == 3:
          cpu_target.next_to(model_cpu_arr[0]     ,       direction=__lowerCAmelCase     ,       buff=0.0			)
         else:
          cpu_target.next_to(model_cpu_arr[i - 1]     ,       direction=__lowerCAmelCase     ,       buff=0.0			)
         self.add(__lowerCAmelCase			)
         model_cpu_arr.append(__lowerCAmelCase			)

        self.add(*__lowerCAmelCase     ,       *__lowerCAmelCase     ,       *__lowerCAmelCase			)

        lowercase__    :   Optional[int]						= [mem.copy() for i in range(6			)]
        lowercase__    :   List[Any]						= VGroup(*__lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)

        lowercase__    :   Optional[Any]						= Text('''Loaded Checkpoint'''     ,       font_size=24			)
        lowercase__    :   Dict						= Group(__lowerCAmelCase     ,       __lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0.5     ,       aligned_edge=__lowerCAmelCase			)
        checkpoint.move_to([3, 0.5, 0]			)

        self.add(__lowerCAmelCase			)

        lowercase__    :   str						= []
        lowercase__    :   List[str]						= []

        for i, rect in enumerate(__lowerCAmelCase			):
         lowercase__    :   List[str]						= fill.copy().set_fill(__lowerCAmelCase     ,       opacity=0.7			)
         target.move_to(__lowerCAmelCase			)
         ckpt_arr.append(__lowerCAmelCase			)

         lowercase__    :   Any						= target.copy()
         if i < 5:
          cpu_target.move_to(cpu_left_col_base[i + 1]			)
         else:
          cpu_target.move_to(cpu_right_col_base[i - 5]			)
         ckpt_cpu_arr.append(__lowerCAmelCase			)
        self.add(*__lowerCAmelCase     ,       *__lowerCAmelCase			)

        lowercase__    :   Optional[int]						= Square(side_length=2.2			)
        key.move_to([-5, 2, 0]			)

        lowercase__    :   Optional[Any]						= MarkupText(
            F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""     ,       font_size=18     ,       )

        key_text.move_to([-5, 2.4, 0]			)

        self.add(__lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   str						= MarkupText(
            F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""     ,       font_size=18     ,       )

        blue_text.next_to(__lowerCAmelCase     ,       DOWN * 2.4     ,       aligned_edge=key_text.get_left()			)
        self.add(__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= MarkupText(
            F"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device."""     ,       font_size=24     ,       )
        step_a.move_to([2, 2, 0]			)

        lowercase__    :   Tuple						= [meta_mem.copy() for i in range(6			)]
        lowercase__    :   Any						= [meta_mem.copy() for i in range(6			)]
        lowercase__    :   str						= VGroup(*__lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)
        lowercase__    :   Any						= VGroup(*__lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)
        lowercase__    :   Union[str, Any]						= VGroup(__lowerCAmelCase     ,       __lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0			)
        lowercase__    :   Union[str, Any]						= Text('''Disk'''     ,       font_size=24			)
        lowercase__    :   Tuple						= Group(__lowerCAmelCase     ,       __lowerCAmelCase			).arrange(__lowerCAmelCase     ,       buff=0.5     ,       aligned_edge=__lowerCAmelCase			)
        disk.move_to([-4.0, -1.2_5, 0]			)
        self.play(Write(__lowerCAmelCase     ,       run_time=3			)     ,       Write(__lowerCAmelCase     ,       run_time=1			)     ,       Create(__lowerCAmelCase     ,       run_time=1			)			)

        lowercase__    :   Tuple						= []
        for i, rect in enumerate(__lowerCAmelCase			):
         lowercase__    :   Dict						= rect.copy()
         target.generate_target()
         target.target.move_to(disk_left_col_base[i]			).scale(0.5			)
         animations.append(MoveToTarget(__lowerCAmelCase     ,       run_time=1.5			)			)
        self.play(*__lowerCAmelCase			)

        self.play(FadeOut(__lowerCAmelCase			)			)

        lowercase__    :   Dict						= MarkupText(F"""Then, the checkpoint is removed from memory\nthrough garbage collection."""     ,       font_size=24			)
        step_a.move_to([2, 2, 0]			)

        self.play(Write(__lowerCAmelCase     ,       run_time=3			)			)

        self.play(
            FadeOut(__lowerCAmelCase     ,       __lowerCAmelCase     ,       *__lowerCAmelCase     ,       *__lowerCAmelCase			)     ,       )

        self.wait()

