

from math import pi, sqrt, tan

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

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

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

def 			lowerCamelCase__			(       _a				,      _a):
	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 			lowerCamelCase__			(       _a				,      _a				,      _a):
	if radius_a < 0 or radius_a < 0 or height < 0:
		raise ValueError(
		    "surface_area_conical_frustum() only accepts non-negative values")
	SCREAMING_SNAKE_CASE :   Any					  =							(height**2 + (radius_a - radius_a) ** 2) ** 0.5
	return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

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

def 			lowerCamelCase__			(       _a				,      _a):
	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(_a				,      2) * torus_radius * tube_radius

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

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

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

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	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")
	SCREAMING_SNAKE_CASE :   List[str]					  =							(sidea + sidea + sidea) / 2
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							sqrt(
	    semi_perimeter
	    * (semi_perimeter - sidea)
	    * (semi_perimeter - sidea)
	    * (semi_perimeter - sidea))
	return area

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

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

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

def 			lowerCamelCase__			(       _a				,      _a):
	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 			lowerCamelCase__			(       _a				,      _a):
	if not isinstance(_a				,      _a) 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) = }''')

"""simple docstring"""





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		UpperCamelCase_       :    List[Any]						      =			'''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch
def 				A_  (__a					, __a					, __a=None					):

					'''simple docstring'''





					if rng is None:
										A_								=    random.Random()

					A_								=    1
					for dim in shape:
										total_dims *= dim

					A_								=    []
					for _ in range(__a					):
										values.append(rng.randint(0					, vocab_size - 1					)					)

					A_								=    np.array(__a					, dtype=jnp.intaa					).reshape(__a					)

					return output
def 				A_  (__a					, __a=None					):

					'''simple docstring'''





					A_								=    ids_tensor(__a					, vocab_size=2					, rng=__a					)
					# make sure that at least one token is attended to for each batch
					A_								=    1
					return attn_mask



@require_flax
class __lowerCAmelCase					:




			"""simple docstring"""







			snake_case						   =					None
			snake_case						   =					()




			def   lowerCamelCase__  (					self    :       Any						)   ->			Tuple:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								A_								=    2
								A_								=    inputs["input_ids"].shape[-1] // 2
								A_								=    inputs["input_ids"][:max_batch_size, :sequence_length]

								A_								=    jnp.ones_like(_snake_case						)
								A_								=    attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								A_								=    input_ids.shape[-1] + 5
								if config.eos_token_id is not None and config.pad_token_id is None:
													# hack to allow generate for models such as GPT2 as is done in `generate()`
													A_								=    config.eos_token_id
								return config, input_ids, attention_mask, max_length




			@is_pt_flax_cross_test
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			List[Any]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    0

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
													A_								=    getattr(_snake_case							, _snake_case						)
													A_								=    pt_model_class(_snake_case						).eval()
													A_								=    load_flax_weights_in_pytorch_model(_snake_case							, flax_model.params						)

													A_								=    flax_model.generate(_snake_case						).sequences
													A_								=    pt_model.generate(torch.tensor(_snake_case							, dtype=torch.long						)						)

													if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																		A_								=    flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

													self.assertListEqual(pt_generation_outputs.numpy().tolist()							, flax_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Any						)   ->			Optional[Any]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			List[str]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    True
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Dict:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    2

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       str						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    2
								A_								=    2

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[0]							, input_ids.shape[0] * config.num_return_sequences						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    True
								A_								=    max_length
								A_								=    0.8
								A_								=    10
								A_								=    0.3
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Any:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    max_length
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Dict:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    max_length
								A_								=    2
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Any:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    False
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			int:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    True
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)






			def   lowerCamelCase__  (					self    :       Dict						)   ->			int:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    2
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)





@require_flax
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Any:







								"""simple docstring"""
								A_								=    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"						)
								A_								=    FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"						)

								A_								=    "Hello world"
								A_								=    tokenizer(_snake_case							, return_tensors="np"						).input_ids

								# typos are quickly detected (the correct argument is `do_sample`)
								with self.assertRaisesRegex(_snake_case							, "do_samples"						):
													model.generate(_snake_case							, do_samples=_snake_case						)

								# arbitrary arguments that will not be used anywhere are also not accepted
								with self.assertRaisesRegex(_snake_case							, "foo"						):
													A_								=    {"foo": "bar"}
													model.generate(_snake_case							, **_snake_case						)




