


'''simple docstring'''

from __future__ import annotations

from math import pi, pow

def 				__magic_name__(    lowerCamelCase):
						if side_length < 0:
												raise ValueError('''vol_cube() only accepts non-negative values''')
						return pow(lowerCamelCase,					3)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						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(lowerCamelCase,					2) * (3 * radius - height)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						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(lowerCamelCase,					lowerCamelCase))

						__lowerCAmelCase											=					(
						    (radius_a - radius_a + centers_distance)
						    * (radius_a + radius_a - centers_distance)
						    / (2 * centers_distance)
						)
						__lowerCAmelCase											=					(
						    (radius_a - radius_a + centers_distance)
						    * (radius_a + radius_a - centers_distance)
						    / (2 * centers_distance)
						)

						return vol_spherical_cap(lowerCamelCase,					lowerCamelCase) + vol_spherical_cap(lowerCamelCase,					lowerCamelCase)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):

						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(lowerCamelCase,					lowerCamelCase))

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

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

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						if height < 0 or area_of_base < 0:
												raise ValueError('''vol_prism() only accepts non-negative values''')
						return float(area_of_base * height)

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

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

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						# 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(lowerCamelCase,					2) - pow(lowerCamelCase,					2)) * height

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						# 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(lowerCamelCase,					2) + pow(lowerCamelCase,					2) + radius_a * radius_a)
						)

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

def 				__magic_name__(    ):
						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 logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


_UpperCAmelCase					:			List[str]				     =					logging.getLogger(__name__)


class    a__					(			__A   ):







				"""simple docstring"""



				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase=None							,      __lowercase=None		):
										if current_layer == 0:
																__lowerCAmelCase											=					self.embedding_hidden_mapping_in(__lowercase		)
										else:
																__lowerCAmelCase											=					hidden_states[0]

										__lowerCAmelCase											=					int(self.config.num_hidden_layers / self.config.num_hidden_groups		)

										# Index of the hidden group
										__lowerCAmelCase											=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)		)

										__lowerCAmelCase											=					self.albert_layer_groups[group_idx](
										    __lowercase							,      __lowercase							,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]							,      )
										__lowerCAmelCase											=					layer_group_output[0]

										return (hidden_states,)


@add_start_docstrings(
    'The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase											=					AlbertTransformerWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0

										__lowerCAmelCase											=					0




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					threshold




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					patience




				def        _snake_case	(self		):
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase											=					(
										    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
										    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
										)
										print(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=False							,      ):

										if input_ids is not None and inputs_embeds is not None:
																raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''		)
										elif input_ids is not None:
																__lowerCAmelCase											=					input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase											=					inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

										__lowerCAmelCase											=					input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__lowerCAmelCase											=					torch.ones(__lowercase							,      device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase											=					torch.zeros(__lowercase							,      dtype=torch.long							,      device=__lowercase		)

										__lowerCAmelCase											=					attention_mask.unsqueeze(1		).unsqueeze(2		)
										__lowerCAmelCase											=					extended_attention_mask.to(dtype=self.dtype		)  # fp16 compatibility
										__lowerCAmelCase											=					(1.0 - extended_attention_mask) * -1_0_0_0_0.0
										__lowerCAmelCase											=					self.get_head_mask(__lowercase							,      self.config.num_hidden_layers		)

										__lowerCAmelCase											=					self.embeddings(
										    __lowercase							,      position_ids=__lowercase							,      token_type_ids=__lowercase							,      inputs_embeds=__lowercase		)
										__lowerCAmelCase											=					embedding_output

										if self.training:
																__lowerCAmelCase											=					[]
																for i in range(self.config.num_hidden_layers		):
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](output_dropout(__lowercase		)		)
																						res.append(__lowercase		)
										elif self.patience == 0:  # Use all layers for inference
																__lowerCAmelCase											=					self.encoder(__lowercase							,      __lowercase							,      head_mask=__lowercase		)
																__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																__lowerCAmelCase											=					[output_layers[self.config.num_hidden_layers - 1](__lowercase		)]
										else:
																__lowerCAmelCase											=					0
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for i in range(self.config.num_hidden_layers		):
																						calculated_layer_num += 1
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](__lowercase		)
																						if regression:
																												__lowerCAmelCase											=					logits.detach()
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0
																						else:
																												__lowerCAmelCase											=					logits.detach().argmax(dim=1		)
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach().argmax(dim=1		)
																												if (patient_result is not None) and torch.all(labels.eq(__lowercase		)		):
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0

																						__lowerCAmelCase											=					logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase											=					[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res


@add_start_docstrings(
    'Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase											=					config.num_labels

										__lowerCAmelCase											=					AlbertModelWithPabee(__lowercase		)
										__lowerCAmelCase											=					nn.Dropout(config.classifier_dropout_prob		)
										__lowerCAmelCase											=					nn.ModuleList(
										    [nn.Linear(config.hidden_size							,      self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

										self.init_weights()




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      ):
										__lowerCAmelCase											=					self.albert(
										    input_ids=__lowercase							,      attention_mask=__lowercase							,      token_type_ids=__lowercase							,      position_ids=__lowercase							,      head_mask=__lowercase							,      inputs_embeds=__lowercase							,      output_dropout=self.dropout							,      output_layers=self.classifiers							,      regression=self.num_labels == 1							,      )

										__lowerCAmelCase											=					(logits[-1],)

										if labels is not None:
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for ix, logits_item in enumerate(__lowercase		):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__lowerCAmelCase											=					MSELoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1		)							,      labels.view(-1		)		)
																						else:
																												__lowerCAmelCase											=					CrossEntropyLoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1							,      self.num_labels		)							,      labels.view(-1		)		)
																						if total_loss is None:
																												__lowerCAmelCase											=					loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__lowerCAmelCase											=					(total_loss / total_weights,) + outputs

										return outputs

