
"""simple docstring"""




from __future__ import annotations

from math import pi, pow

def lowercase_  (       _snake_case						):
							if side_length < 0:
														raise ValueError("""vol_cube() only accepts non-negative values"""						)
							return pow(_snake_case       ,3						)

def lowercase_  (       _snake_case       ,_snake_case						):
							if height < 0 or radius < 0:
														raise ValueError("""vol_spherical_cap() only accepts non-negative values"""						)
							# Volume is 1/3 pi * height squared * (3 * radius - height)
							return 1 / 3 * pi * pow(_snake_case       ,2						) * (3 * radius - height)

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							if radius_a < 0 or radius_a < 0 or centers_distance < 0:
														raise ValueError("""vol_spheres_intersect() only accepts non-negative values"""						)
							if centers_distance == 0:
														return vol_sphere(min(_snake_case       ,_snake_case						)						)

							SCREAMING_SNAKE_CASE__						:	List[str]									=       (
							    (radius_a - radius_a + centers_distance)
							    * (radius_a + radius_a - centers_distance)
							    / (2 * centers_distance)
							)
							SCREAMING_SNAKE_CASE__						:	int									=       (
							    (radius_a - radius_a + centers_distance)
							    * (radius_a + radius_a - centers_distance)
							    / (2 * centers_distance)
							)

							return vol_spherical_cap(_snake_case       ,_snake_case						) + vol_spherical_cap(_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):

							if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
														raise ValueError(
														    """vol_spheres_union() only accepts non-negative values, non-zero radius"""						)

							if centers_distance == 0:
														return vol_sphere(max(_snake_case       ,_snake_case						)						)

							return (
							    vol_sphere(_snake_case						)
							    + vol_sphere(_snake_case						)
							    - vol_spheres_intersect(_snake_case       ,_snake_case       ,_snake_case						)
							)

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							if width < 0 or height < 0 or length < 0:
														raise ValueError("""vol_cuboid() only accepts non-negative values"""						)
							return float(width * height * length						)

def lowercase_  (       _snake_case       ,_snake_case						):
							if height < 0 or area_of_base < 0:
														raise ValueError("""vol_cone() only accepts non-negative values"""						)
							return area_of_base * height / 3.0

def lowercase_  (       _snake_case       ,_snake_case						):
							if height < 0 or radius < 0:
														raise ValueError("""vol_right_circ_cone() only accepts non-negative values"""						)
							return pi * pow(_snake_case       ,2						) * height / 3.0

def lowercase_  (       _snake_case       ,_snake_case						):
							if height < 0 or area_of_base < 0:
														raise ValueError("""vol_prism() only accepts non-negative values"""						)
							return float(area_of_base * height						)

def lowercase_  (       _snake_case       ,_snake_case						):
							if height < 0 or area_of_base < 0:
														raise ValueError("""vol_pyramid() only accepts non-negative values"""						)
							return area_of_base * height / 3.0

def lowercase_  (       _snake_case						):
							if radius < 0:
														raise ValueError("""vol_sphere() only accepts non-negative values"""						)
							# Volume is 4/3 * pi * radius cubed
							return 4 / 3 * pi * pow(_snake_case       ,3						)

def lowercase_  (       _snake_case						):
							if radius < 0:
														raise ValueError("""vol_hemisphere() only accepts non-negative values"""						)
							# Volume is radius cubed * pi * 2/3
							return pow(_snake_case       ,3						) * pi * 2 / 3

def lowercase_  (       _snake_case       ,_snake_case						):
							if height < 0 or radius < 0:
														raise ValueError("""vol_circular_cylinder() only accepts non-negative values"""						)
							# Volume is radius squared * height * pi
							return pow(_snake_case       ,2						) * height * pi

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Volume - (outer_radius squared - inner_radius squared) * pi * height
							if inner_radius < 0 or outer_radius < 0 or height < 0:
														raise ValueError(
														    """vol_hollow_circular_cylinder() only accepts non-negative values"""						)
							if outer_radius <= inner_radius:
														raise ValueError("""outer_radius must be greater than inner_radius"""						)
							return pi * (pow(_snake_case       ,2						) - pow(_snake_case       ,2						)) * height

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Volume is 1/3 * pi * height *
							#           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError("""vol_conical_frustum() only accepts non-negative values"""						)
							return (
							    1
							    / 3
							    * pi
							    * height
							    * (pow(_snake_case       ,2						) + pow(_snake_case       ,2						) + radius_a * radius_a)
							)

def lowercase_  (       _snake_case       ,_snake_case						):
							if torus_radius < 0 or tube_radius < 0:
														raise ValueError("""vol_torus() only accepts non-negative values"""						)
							return 2 * pow(_snake_case       ,2						) * torus_radius * pow(_snake_case       ,2						)

def lowercase_  (       ):
							print("""Volumes:"""						)
							print(f'''Cube: {vol_cube(2						) = }'''						)  # = 8
							print(f'''Cuboid: {vol_cuboid(2       ,2       ,2						) = }'''						)  # = 8
							print(f'''Cone: {vol_cone(2       ,2						) = }'''						)  # ~= 1.33
							print(f'''Right Circular Cone: {vol_right_circ_cone(2       ,2						) = }'''						)  # ~= 8.38
							print(f'''Prism: {vol_prism(2       ,2						) = }'''						)  # = 4
							print(f'''Pyramid: {vol_pyramid(2       ,2						) = }'''						)  # ~= 1.33
							print(f'''Sphere: {vol_sphere(2						) = }'''						)  # ~= 33.5
							print(f'''Hemisphere: {vol_hemisphere(2						) = }'''						)  # ~= 16.75
							print(f'''Circular Cylinder: {vol_circular_cylinder(2       ,2						) = }'''						)  # ~= 25.1
							print(f'''Torus: {vol_torus(2       ,2						) = }'''						)  # ~= 157.9
							print(f'''Conical Frustum: {vol_conical_frustum(2       ,2       ,4						) = }'''						)  # ~= 58.6
							print(f'''Spherical cap: {vol_spherical_cap(1       ,2						) = }'''						)  # ~= 5.24
							print(f'''Spheres intersetion: {vol_spheres_intersect(2       ,2       ,1						) = }'''						)  # ~= 21.21
							print(f'''Spheres union: {vol_spheres_union(2       ,2       ,1						) = }'''						)  # ~= 45.81
							print(
							    f'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1       ,2       ,3						) = }'''						)  # ~= 28.3


if __name__ == "__main__":
			main()






"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       FNetConfig.from_json_file(_snake_case						)
							print(f'''Building PyTorch model from configuration: {config}'''						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       FNetForPreTraining(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	Tuple									=       restore_checkpoint(_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       checkpoint_dict["""target"""]

							# Embeddings
							# Position IDs
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fnet_pretraining_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Any									=       state_dict["""fnet.embeddings.position_ids"""]
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""fnet.embeddings.position_ids""": position_ids}
							# Embedding Layers
							SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Any									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]						)

							# Encoder Layers
							for layer in range(config.num_hidden_layers						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]						)

							# Pooler Layers
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]						)

							# Masked LM Layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_output"""]["""output_bias"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]						)

							# Seq Relationship Layers
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_kernel"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_bias"""]						)

							# Load State Dict
							fnet_pretraining_model.load_state_dict(_snake_case						)

							# Save PreTrained
							print(f'''Saving pretrained model to {save_path}'''						)
							fnet_pretraining_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[int]   							=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--fnet_config_file',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'The config json file corresponding to the pre-trained FNet model. \n'
					        'This specifies the model architecture.'
					    ),
					)
					parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




