




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase      :						int							=  logging.get_logger("""transformers.models.speecht5""")







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

       hf_model.apply_weight_norm()

       A__ 					=       checkpoint['''input_conv.weight_g''']
       A__ 					=       checkpoint['''input_conv.weight_v''']
       A__ 					=       checkpoint['''input_conv.bias''']

       for i in range(len(config.upsample_rates		)		):
              A__ 					=       checkpoint[f"""upsamples.{i}.1.weight_g"""]
              A__ 					=       checkpoint[f"""upsamples.{i}.1.weight_v"""]
              A__ 					=       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		)		):
                     A__ 					=       checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

                     A__ 					=       checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

       A__ 					=       checkpoint['''output_conv.1.weight_g''']
       A__ 					=       checkpoint['''output_conv.1.weight_v''']
       A__ 					=       checkpoint['''output_conv.1.bias''']

       hf_model.remove_weight_norm()







@torch.no_grad()
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None					,							)  ->			str:



       """simple docstring"""

       if config_path is not None:
              A__ 					=       SpeechTaHifiGanConfig.from_pretrained(lowercase_		)
       else:
              A__ 					=       SpeechTaHifiGanConfig()

       A__ 					=       SpeechTaHifiGan(lowercase_		)

       A__ 					=       torch.load(lowercase_		)
       load_weights(orig_checkpoint['''model''']['''generator''']					,							lowercase_					,							lowercase_		)

       A__ 					=       np.load(lowercase_		)
       A__ 					=       stats[0].reshape(-1		)
       A__ 					=       stats[1].reshape(-1		)
       A__ 					=       torch.from_numpy(lowercase_		).float()
       A__ 					=       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__":
  _lowerCamelCase      :						Any							=  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."""
  )

  _lowerCamelCase      :						List[str]							=  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 colorsys

from PIL import Image  # type: ignore

def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       float							,    __UpperCamelCase					:       int		)					->	float:







     """simple docstring"""
     _A				=							x
     _A				=							y
     for step in range(__UpperCamelCase		):  # noqa: B007
          _A				=							a * a - b * b + x
          _A				=							2 * a * b + y
          _A				=							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_5_5, 2_5_5, 2_5_5)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_5_5		) for i in colorsys.hsv_to_rgb(__UpperCamelCase							,    1							,    1		)		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 8_0_0							,    __UpperCamelCase					:       int = 6_0_0							,    __UpperCamelCase					:       float = -0.6							,    __UpperCamelCase					:       float = 0							,    __UpperCamelCase					:       float = 3.2							,    __UpperCamelCase					:       int = 5_0							,    __UpperCamelCase					:       bool = True							,    )					->	Image.Image:







     """simple docstring"""
     _A				=							Image.new('RGB'							,    (image_width, image_height)		)
     _A				=							img.load()

     # loop through the image-coordinates
     for image_x in range(__UpperCamelCase		):
          for image_y in range(__UpperCamelCase		):
               # determine the figure-coordinates based on the image-coordinates
               _A				=							figure_width / image_width * image_height
               _A				=							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _A				=							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _A				=							get_distance(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _A				=							get_color_coded_rgb(__UpperCamelCase		)
               else:
                    _A				=							get_black_and_white_rgb(__UpperCamelCase		)

     return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    lowerCAmelCase						=					get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()





