





from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__lowerCamelCase	:					Union[str, Any]			  =					numpy.array([0, 0])
__lowerCamelCase	:					Union[str, Any]			  =					numpy.array([0.5, 0.8_6_6_0_2_5_4])
__lowerCamelCase	:					Union[str, Any]			  =					numpy.array([1, 0])
__lowerCamelCase	:					Tuple			  =					[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[numpy.ndarray]:
 UpperCamelCase     : Union[str, Any]		=       initial_vectors
 for _ in range(lowercase__					):
  UpperCamelCase     : List[Any]		=       iteration_step(lowercase__					)
 return vectors



def 		A_    (					_lowerCAmelCase					)						->			list[numpy.ndarray]:
 UpperCamelCase     : Tuple		=       []
 for i, start_vector in enumerate(vectors[:-1]					):
  UpperCamelCase     : List[Any]		=       vectors[i + 1]
  new_vectors.append(lowercase__					)
  UpperCamelCase     : str		=       end_vector - start_vector
  new_vectors.append(start_vector + difference_vector / 3					)
  new_vectors.append(
      start_vector + difference_vector / 3 + rotate(difference_vector / 3							,       60					)					)
  new_vectors.append(start_vector + difference_vector * 2 / 3					)
 new_vectors.append(vectors[-1]					)
 return new_vectors



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			numpy.ndarray:
 UpperCamelCase     : Union[str, Any]		=       numpy.radians(lowercase__					)
 UpperCamelCase     : Tuple		=       numpy.cos(lowercase__					), numpy.sin(lowercase__					)
 UpperCamelCase     : Tuple		=       numpy.array(((c, -s), (s, c))					)
 return numpy.dot(lowercase__							,       lowercase__					)



def 		A_    (					_lowerCAmelCase					)						->			None:
 UpperCamelCase     : Tuple		=       plt.gca()
 axes.set_aspect("equal"					)

 # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
 # y-coordinates as inputs, which are constructed from the vector-list using
 # zip()
 UpperCamelCase     : Any		=       zip(*lowercase__					)
 plt.plot(lowercase__							,       lowercase__					)
 plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowerCamelCase	:					str			  =					iterate(INITIAL_VECTORS, 5)
    plot(processed_vectors)







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

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

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       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__":
    __lowerCamelCase	:					Union[str, 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	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
