

from math import pi, sqrt, tan

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

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float					,   lowercase_:				float      )  ->   float:
	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 		UpperCamelCase      (lowercase_:				float      )  ->   float:
	if radius < 0:
		raise ValueError("""surface_area_sphere() only accepts non-negative values"""      )
	return 4 * pi * radius**2

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

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float      )  ->   float:
	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 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float					,   lowercase_:				float      )  ->   float:
	if radius_a < 0 or radius_a < 0 or height < 0:
		raise ValueError(
		    """surface_area_conical_frustum() only accepts non-negative values"""      )
	A__							:					int       =	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
	return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

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

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float      )  ->   float:
	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(lowercase_					,   2      ) * torus_radius * tube_radius

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

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

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float      )  ->   float:
	if base < 0 or height < 0:
		raise ValueError("""area_triangle() only accepts non-negative values"""      )
	return (base * height) / 2

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float					,   lowercase_:				float      )  ->   float:
	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"""      )
	A__							:					Optional[Any]       =	(sidea + sidea + sidea) / 2
	A__							:					str       =	sqrt(
	    semi_perimeter
	    * (semi_perimeter - sidea)
	    * (semi_perimeter - sidea)
	    * (semi_perimeter - sidea)      )
	return area

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float      )  ->   float:
	if base < 0 or height < 0:
		raise ValueError("""area_parallelogram() only accepts non-negative values"""      )
	return base * height

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float					,   lowercase_:				float      )  ->   float:
	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 		UpperCamelCase      (lowercase_:				float      )  ->   float:
	if radius < 0:
		raise ValueError("""area_circle() only accepts non-negative values"""      )
	return pi * radius**2

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float      )  ->   float:
	if radius_x < 0 or radius_y < 0:
		raise ValueError("""area_ellipse() only accepts non-negative values"""      )
	return pi * radius_x * radius_y

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float      )  ->   float:
	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 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				float      )  ->   float:
	if not isinstance(lowercase_					,   lowercase_      ) or sides < 3:
		raise ValueError(
		    """area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"""      )
	elif length < 0:
		raise ValueError(
		    """area_reg_polygon() only accepts non-negative values as \
length 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) = }''')






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']






					@register_to_config
					def __init__(				self					, A__					, A__					, A__ = None					, A__ = 5_0257					, A__ = 1024					, A__ = 768					, A__ = 12					, A__ = 12					, A__ = None					, A__ = "gelu_new"					, A__ = 0.1					, A__ = 0.1					, A__ = 0.1					, A__ = 1e-5					, A__ = 0.0_2					, A__ = True					, A__ = True					, A__ = False					, A__ = False					, ):
						super().__init__()

						A__							:					Union[str, Any]       =	prefix_length

						if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
							raise ValueError(
							    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
							    F""" `n_embd`: {n_embd} are not equal."""    )

						A__							:					str       =	prefix_inner_dim
						A__							:					Optional[Any]       =	prefix_hidden_dim

						A__							:					Tuple       =	(
						    nn.Linear(self.prefix_inner_dim					, self.prefix_hidden_dim    )
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A__							:					int       =	(
						    nn.Linear(self.prefix_hidden_dim					, A__    ) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A__							:					Tuple       =	GPTaConfig(
						    vocab_size=A__					, n_positions=A__					, n_embd=A__					, n_layer=A__					, n_head=A__					, n_inner=A__					, activation_function=A__					, resid_pdrop=A__					, embd_pdrop=A__					, attn_pdrop=A__					, layer_norm_epsilon=A__					, initializer_range=A__					, scale_attn_weights=A__					, use_cache=A__					, scale_attn_by_inverse_layer_idx=A__					, reorder_and_upcast_attn=A__					, )
						A__							:					int       =	GPTaLMHeadModel(A__    )






					def 						__A    (				self					, A__					, A__					, A__ = None					, A__ = None					, ):
						A__							:					List[str]       =	self.transformer.transformer.wte(A__    )
						A__							:					int       =	self.encode_prefix(A__    )
						A__							:					int       =	self.decode_prefix(A__    )
						A__							:					Optional[Any]       =	torch.cat((prefix_embeds, embedding_text)					, dim=1    )

						if labels is not None:
							A__							:					Any       =	self.get_dummy_token(input_ids.shape[0]					, input_ids.device    )
							A__							:					List[Any]       =	torch.cat((dummy_token, input_ids)					, dim=1    )
						A__							:					List[str]       =	self.transformer(inputs_embeds=A__					, labels=A__					, attention_mask=A__    )
						if self.prefix_hidden_dim is not None:
							return out, hidden
						else:
							return out






					def 						__A    (				self					, A__					, A__    ):
						return torch.zeros(A__					, self.prefix_length					, dtype=torch.intaa					, device=A__    )






					def 						__A    (				self					, A__    ):
						return self.encode_prefix(A__    )






					@torch.no_grad()
					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					List[Any]       =	torch.split(A__					, 1					, dim=0    )
						A__							:					Optional[int]       =	[]
						A__							:					str       =	[]
						for feature in features:
							A__							:					Dict       =	self.decode_prefix(feature.to(A__    )    )  # back to the clip feature
							# Only support beam search for now
							A__     ,     A__							:					Union[str, Any]       =	self.generate_beam(
							    input_embeds=A__					, device=A__					, eos_token_id=A__    )
							generated_tokens.append(output_tokens[0]    )
							generated_seq_lengths.append(seq_lengths[0]    )
						A__							:					int       =	torch.stack(A__    )
						A__							:					List[Any]       =	torch.stack(A__    )
						return generated_tokens, generated_seq_lengths






					@torch.no_grad()
					def 						__A    (				self					, A__=None					, A__=None					, A__=None					, A__ = 5					, A__ = 67					, A__ = 1.0					, A__ = None					, ):
						A__							:					Any       =	eos_token_id
						A__							:					Any       =	None
						A__							:					Optional[int]       =	None
						A__							:					Optional[Any]       =	torch.ones(A__					, device=A__					, dtype=torch.int    )
						A__							:					Any       =	torch.zeros(A__					, device=A__					, dtype=torch.bool    )

						if input_embeds is not None:
							A__							:					Dict       =	input_embeds
						else:
							A__							:					str       =	self.transformer.transformer.wte(A__    )

						for i in range(A__    ):
							A__							:					Dict       =	self.transformer(inputs_embeds=A__    )
							A__							:					str       =	outputs.logits
							A__							:					Union[str, Any]       =	logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
							A__							:					Any       =	logits.softmax(-1    ).log()

							if scores is None:
								A__     ,     A__							:					Optional[int]       =	logits.topk(A__					, -1    )
								A__							:					List[Any]       =	generated.expand(A__					, *generated.shape[1:]    )
								A__     ,     A__							:					List[Any]       =	next_tokens.permute(1					, 0    ), scores.squeeze(0    )
								if tokens is None:
									A__							:					Optional[Any]       =	next_tokens
								else:
									A__							:					List[Any]       =	tokens.expand(A__					, *tokens.shape[1:]    )
									A__							:					int       =	torch.cat((tokens, next_tokens)					, dim=1    )
							else:
								A__							:					Optional[int]       =	-float(np.inf    )
								A__							:					List[Any]       =	0
								A__							:					str       =	scores[:, None] + logits
								seq_lengths[~is_stopped] += 1
								A__							:					Dict       =	scores_sum / seq_lengths[:, None]
								A__     ,     A__							:					List[Any]       =	scores_sum_average.view(-1    ).topk(A__					, -1    )
								A__							:					Tuple       =	next_tokens // scores_sum.shape[1]
								A__							:					Optional[Any]       =	seq_lengths[next_tokens_source]
								A__							:					List[str]       =	next_tokens % scores_sum.shape[1]
								A__							:					Optional[int]       =	next_tokens.unsqueeze(1    )
								A__							:					int       =	tokens[next_tokens_source]
								A__							:					List[Any]       =	torch.cat((tokens, next_tokens)					, dim=1    )
								A__							:					str       =	generated[next_tokens_source]
								A__							:					Optional[Any]       =	scores_sum_average * seq_lengths
								A__							:					Union[str, Any]       =	is_stopped[next_tokens_source]

							A__							:					str       =	self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]					, 1					, -1    )
							A__							:					Optional[int]       =	torch.cat((generated, next_token_embed)					, dim=1    )
							A__							:					List[str]       =	is_stopped + next_tokens.eq(A__    ).squeeze()
							if is_stopped.all():
								break

						A__							:					Dict       =	scores / seq_lengths
						A__							:					Dict       =	scores.argsort(descending=A__    )
						# tokens tensors are already padded to max_seq_length
						A__							:					Union[str, Any]       =	[tokens[i] for i in order]
						A__							:					Any       =	torch.stack(A__					, dim=0    )
						A__							:					Dict       =	torch.tensor([seq_lengths[i] for i in order]					, dtype=seq_lengths.dtype    )
						return output_texts, seq_lengths



