"""simple docstring"""





from math import pi, sqrt, tan


def    lowercase_   (   _lowerCamelCase:	float      ) ->      Tuple:



							'''simple docstring'''

							if side_length < 0:
														raise ValueError("surface_area_cube() only accepts non-negative values"      )
							return 6 * side_length**2


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      Any:



							'''simple docstring'''

							if length < 0 or breadth < 0 or height < 0:
														raise ValueError("surface_area_cuboid() only accepts non-negative values"      )
							return 2 * ((length * breadth) + (breadth * height) + (length * height))


def    lowercase_   (   _lowerCamelCase:	float      ) ->      Any:



							'''simple docstring'''

							if radius < 0:
														raise ValueError("surface_area_sphere() only accepts non-negative values"      )
							return 4 * pi * radius**2


def    lowercase_   (   _lowerCamelCase:	float      ) ->      Optional[Any]:



							'''simple docstring'''

							if radius < 0:
														raise ValueError("surface_area_hemisphere() only accepts non-negative values"      )
							return 3 * pi * radius**2


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      Tuple:



							'''simple docstring'''

							if radius < 0 or height < 0:
														raise ValueError("surface_area_cone() only accepts non-negative values"      )
							return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      List[str]:



							'''simple docstring'''

							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError(
														    "surface_area_conical_frustum() only accepts non-negative values"      )
							__lowerCamelCase					:		List[str]		   =     (height**2 + (radius_a - radius_a) ** 2) ** 0.5
							return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      List[Any]:



							'''simple docstring'''

							if radius < 0 or height < 0:
														raise ValueError("surface_area_cylinder() only accepts non-negative values"      )
							return 2 * pi * radius * (height + radius)


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      Dict:



							'''simple docstring'''

							if torus_radius < 0 or tube_radius < 0:
														raise ValueError("surface_area_torus() only accepts non-negative values"      )
							if torus_radius < tube_radius:
														raise ValueError(
														    "surface_area_torus() does not support spindle or self intersecting tori"      )
							return 4 * pow(_lowerCamelCase						,   2      ) * torus_radius * tube_radius


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      List[str]:



							'''simple docstring'''

							if length < 0 or width < 0:
														raise ValueError("area_rectangle() only accepts non-negative values"      )
							return length * width


def    lowercase_   (   _lowerCamelCase:	float      ) ->      Union[str, Any]:



							'''simple docstring'''

							if side_length < 0:
														raise ValueError("area_square() only accepts non-negative values"      )
							return side_length**2


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      int:



							'''simple docstring'''

							if base < 0 or height < 0:
														raise ValueError("area_triangle() only accepts non-negative values"      )
							return (base * height) / 2


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      Union[str, Any]:



							'''simple docstring'''

							if sidea < 0 or sidea < 0 or sidea < 0:
														raise ValueError("area_triangle_three_sides() only accepts non-negative values"      )
							elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
														raise ValueError("Given three sides do not form a triangle"      )
							__lowerCamelCase					:		List[str]		   =     (sidea + sidea + sidea) / 2
							__lowerCamelCase					:		Optional[int]		   =     sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)      )
							return area


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      List[str]:



							'''simple docstring'''

							if base < 0 or height < 0:
														raise ValueError("area_parallelogram() only accepts non-negative values"      )
							return base * height


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      str:



							'''simple docstring'''

							if basea < 0 or basea < 0 or height < 0:
														raise ValueError("area_trapezium() only accepts non-negative values"      )
							return 1 / 2 * (basea + basea) * height


def    lowercase_   (   _lowerCamelCase:	float      ) ->      List[str]:



							'''simple docstring'''

							if radius < 0:
														raise ValueError("area_circle() only accepts non-negative values"      )
							return pi * radius**2


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      Optional[Any]:



							'''simple docstring'''

							if radius_x < 0 or radius_y < 0:
														raise ValueError("area_ellipse() only accepts non-negative values"      )
							return pi * radius_x * radius_y


def    lowercase_   (   _lowerCamelCase:	float						,   _lowerCamelCase:	float      ) ->      List[str]:



							'''simple docstring'''

							if diagonal_a < 0 or diagonal_a < 0:
														raise ValueError("area_rhombus() only accepts non-negative values"      )
							return 1 / 2 * diagonal_a * diagonal_a


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	float      ) ->      Tuple:



							'''simple docstring'''

							if not isinstance(_lowerCamelCase						,   _lowerCamelCase      ) or sides < 3:
														raise ValueError(
														    "area_reg_polygon() only accepts integers greater than or \\nequal to three as number of sides"      )
							elif length < 0:
														raise ValueError(
														    "area_reg_polygon() only accepts non-negative values as \\nlength of a side"      )
							return (sides * length**2) / (4 * tan(pi / sides      ))
							return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

				print('''[DEMO] Areas of various geometric shapes: \n''')
				print(F"""Rectangle: {area_rectangle(10, 20) = }""")
				print(F"""Square: {area_square(10) = }""")
				print(F"""Triangle: {area_triangle(10, 10) = }""")
				print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
				print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
				print(F"""Rhombus: {area_rhombus(10, 20) = }""")
				print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
				print(F"""Circle: {area_circle(20) = }""")
				print(F"""Ellipse: {area_ellipse(10, 20) = }""")
				print('''\nSurface Areas of various geometric shapes: \n''')
				print(F"""Cube: {surface_area_cube(20) = }""")
				print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
				print(F"""Sphere: {surface_area_sphere(20) = }""")
				print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
				print(F"""Cone: {surface_area_cone(10, 20) = }""")
				print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
				print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
				print(F"""Torus: {surface_area_torus(20, 10) = }""")
				print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
				print(F"""Square: {area_reg_polygon(4, 10) = }""")
				print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__A  =				logging.get_logger('''transformers.models.speecht5''')


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Union[str, Any]:



							'''simple docstring'''

							hf_model.apply_weight_norm()

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["input_conv.weight_g"]
							__lowerCamelCase					:		int		   =     checkpoint["input_conv.weight_v"]
							__lowerCamelCase					:		Dict		   =     checkpoint["input_conv.bias"]

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

																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

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

							hf_model.remove_weight_norm()


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Optional[int]=None						,   ) ->      Optional[int]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SpeechTaHifiGanConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Tuple		   =     SpeechTaHifiGanConfig()

							__lowerCamelCase					:		List[Any]		   =     SpeechTaHifiGan(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     torch.load(_lowerCamelCase      )
							load_weights(orig_checkpoint["model"]["generator"]						,   _lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     np.load(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     stats[0].reshape(-1      )
							__lowerCamelCase					:		Optional[int]		   =     stats[1].reshape(-1      )
							__lowerCamelCase					:		int		   =     torch.from_numpy(_lowerCamelCase      ).float()
							__lowerCamelCase					:		List[str]		   =     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__":
				__A  =				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  =				parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)