"""simple docstring"""






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase    :       str					     =logging.get_logger("""transformers.models.speecht5""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :int		) ->    List[str]:
      '''simple docstring'''


      hf_model.apply_weight_norm()

      lowercase														=	checkpoint["""input_conv.weight_g"""]
      lowercase														=	checkpoint["""input_conv.weight_v"""]
      lowercase														=	checkpoint["""input_conv.bias"""]

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

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

      lowercase														=	checkpoint["""output_conv.1.weight_g"""]
      lowercase														=	checkpoint["""output_conv.1.weight_v"""]
      lowercase														=	checkpoint["""output_conv.1.bias"""]

      hf_model.remove_weight_norm()


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :str=None					,		lowerCAmelCase__     :Dict=None					,		) ->    Union[str, Any]:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=	SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__		)
      else:
            lowercase														=	SpeechTaHifiGanConfig()

      lowercase														=	SpeechTaHifiGan(lowerCAmelCase__		)

      lowercase														=	torch.load(lowerCAmelCase__		)
      load_weights(orig_checkpoint["""model"""]["""generator"""]					,		lowerCAmelCase__					,		lowerCAmelCase__		)

      lowercase														=	np.load(lowerCAmelCase__		)
      lowercase														=	stats[0].reshape(-1		)
      lowercase														=	stats[1].reshape(-1		)
      lowercase														=	torch.from_numpy(lowerCAmelCase__		).float()
      lowercase														=	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    :       List[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    :       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 __future__ import annotations

from scipy.special import comb  # type: ignore





class  _A						:

      def __init__(						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	list_of_points
            # Degree determines the flexibility of the curve.
            # Degree = 1 will produce a straight line.
            lowercase														=	len(__lowerCAmelCase						) - 1

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            assert 0 <= t <= 1, "Time t must be between 0 and 1."
            lowercase														=	[]
            for i in range(len(self.list_of_points						)						):
                  # basis function for each i
                  output_values.append(
                      comb(self.degree		,       __lowerCAmelCase						) * ((1 - t) ** (self.degree - i)) * (t**i)						)
            # the basis must sum up to 1 for it to produce a valid Bezier curve.
            assert round(sum(__lowerCAmelCase						)		,       5						) == 1
            return output_values

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            assert 0 <= t <= 1, "Time t must be between 0 and 1."

            lowercase														=	self.basis_function(__lowerCAmelCase						)
            lowercase														=	0.0
            lowercase														=	0.0
            for i in range(len(self.list_of_points						)						):
                  # For all points, sum up the product of i-th basis function and i-th point.
                  x += basis_function[i] * self.list_of_points[i][0]
                  y += basis_function[i] * self.list_of_points[i][1]
            return (x, y)







      def 				A__       (						self		,       __lowerCAmelCase = 0.0_1						):







            """simple docstring"""



            from matplotlib import pyplot as plt  # type: ignore

            lowercase														=	[]  # x coordinates of points to plot
            lowercase														=	[]  # y coordinates of points to plot

            lowercase														=	0.0
            while t <= 1:
                  lowercase														=	self.bezier_curve_function(__lowerCAmelCase						)
                  to_plot_x.append(value[0]						)
                  to_plot_y.append(value[1]						)
                  t += step_size

            lowercase														=	[i[0] for i in self.list_of_points]
            lowercase														=	[i[1] for i in self.list_of_points]

            plt.plot(
                __lowerCAmelCase		,       __lowerCAmelCase		,       color="""blue"""		,       label="""Curve of Degree """ + str(self.degree						)		,       )
            plt.scatter(__lowerCAmelCase		,       __lowerCAmelCase		,       color="""red"""		,       label="""Control Points"""						)
            plt.legend()
            plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
       BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
       BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



