
"""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__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					int ,				lowerCAmelCase				:					Union[str, Any] ) ->      int:






    """simple docstring"""




    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__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int]=None ,				lowerCAmelCase				:					Optional[int]=None ,				) ->      Optional[Any]:






    """simple docstring"""




    if config_path is not None:
        UpperCAmelCase									=					SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase )
    else:
        UpperCAmelCase									=					SpeechTaHifiGanConfig()

    UpperCAmelCase									=					SpeechTaHifiGan(lowerCAmelCase )

    UpperCAmelCase									=					torch.load(lowerCAmelCase )
    load_weights(orig_checkpoint['model']['generator'] ,				lowerCAmelCase ,				lowerCAmelCase )

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

    model.save_pretrained(lowerCAmelCase )

    if repo_id:
        print('Pushing to the hub...' )
        model.push_to_hub(lowerCAmelCase )


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,
   )





"""simple docstring"""

import sys
import turtle




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ) ->      tuple[float, float]:






    """simple docstring"""




    return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					int ,				) ->      None:






    """simple docstring"""




    my_pen.up()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.down()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )

    if depth == 0:
        return

    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )


if __name__ == "__main__":
   if len(sys.argv) != 2:
      raise ValueError(
          '''Correct format for using this script: '''
          '''python fractals.py <int:depth_for_fractal>'''
      )
   SCREAMING_SNAKE_CASE_    					=						turtle.Turtle()
   my_pen.ht()
   my_pen.speed(5)
   my_pen.pencolor('''red''')

   SCREAMING_SNAKE_CASE_    					=						[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
   triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))



