




'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						_lowerCamelCase							:						str								=  'platform'

						import jax
						import jax.numpy as jnp

						from transformers import AutoTokenizer
						from transformers.models.mbart.modeling_flax_mbart import (
						    FlaxMBartForConditionalGeneration,
						    FlaxMBartForQuestionAnswering,
						    FlaxMBartForSequenceClassification,
						    FlaxMBartModel,
						    shift_tokens_right,
						)






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							):



		'''simple docstring'''







		if attention_mask is None:
				_snake_case	       =       np.where(input_ids != config.pad_token_id						,							1						,							0  )
		if decoder_attention_mask is None:
				_snake_case	       =       np.where(decoder_input_ids != config.pad_token_id						,							1						,							0  )
		if head_mask is None:
				_snake_case	       =       np.ones((config.encoder_layers, config.encoder_attention_heads)  )
		if decoder_head_mask is None:
				_snake_case	       =       np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		if cross_attn_head_mask is None:
				_snake_case	       =       np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		}





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=13,  __UpperCamelCase=7,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=99,  __UpperCamelCase=16,  __UpperCamelCase=2,  __UpperCamelCase=4,  __UpperCamelCase=4,  __UpperCamelCase="gelu",  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=32,  __UpperCamelCase=2,  __UpperCamelCase=1,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0.02,  ):
							_snake_case	       =       parent
							_snake_case	       =       batch_size
							_snake_case	       =       seq_length
							_snake_case	       =       is_training
							_snake_case	       =       use_labels
							_snake_case	       =       vocab_size
							_snake_case	       =       hidden_size
							_snake_case	       =       num_hidden_layers
							_snake_case	       =       num_attention_heads
							_snake_case	       =       intermediate_size
							_snake_case	       =       hidden_act
							_snake_case	       =       hidden_dropout_prob
							_snake_case	       =       attention_probs_dropout_prob
							_snake_case	       =       max_position_embeddings
							_snake_case	       =       eos_token_id
							_snake_case	       =       pad_token_id
							_snake_case	       =       bos_token_id
							_snake_case	       =       decoder_start_token_id
							_snake_case	       =       initializer_range






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1],  self.vocab_size ),  3,  self.vocab_size )
							_snake_case	       =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),  dtype=np.intaa )),  -1 )

							_snake_case	       =       shift_tokens_right(__UpperCamelCase,  1 )

							_snake_case	       =       MBartConfig(
							    vocab_size=self.vocab_size,  d_model=self.hidden_size,  encoder_layers=self.num_hidden_layers,  decoder_layers=self.num_hidden_layers,  encoder_attention_heads=self.num_attention_heads,  decoder_attention_heads=self.num_attention_heads,  encoder_ffn_dim=self.intermediate_size,  decoder_ffn_dim=self.intermediate_size,  dropout=self.hidden_dropout_prob,  attention_dropout=self.attention_probs_dropout_prob,  max_position_embeddings=self.max_position_embeddings,  eos_token_id=self.eos_token_id,  bos_token_id=self.bos_token_id,  pad_token_id=self.pad_token_id,  decoder_start_token_id=self.decoder_start_token_id,  initializer_range=self.initializer_range,  use_cache=__UpperCamelCase,  )
							_snake_case	       =       prepare_mbart_inputs_dict(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )
							return config, inputs_dict






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.prepare_config_and_inputs()
							return config, inputs_dict






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       20
							_snake_case	       =       model_class_name(__UpperCamelCase )

							_snake_case	       =       model.encode(inputs_dict["input_ids"] )

							_snake_case       ,    _snake_case	       =       (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							_snake_case	       =       model.init_cache(decoder_input_ids.shape[0],  __UpperCamelCase,  __UpperCamelCase )
							_snake_case	       =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length),  dtype="i4" )

							_snake_case	       =       jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :],  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),  )
							_snake_case	       =       model.decode(
							    decoder_input_ids[:, :-1],  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  past_key_values=__UpperCamelCase,  decoder_position_ids=__UpperCamelCase,  )

							_snake_case	       =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype="i4" )
							_snake_case	       =       model.decode(
							    decoder_input_ids[:, -1:],  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_position_ids=__UpperCamelCase,  )

							_snake_case	       =       model.decode(__UpperCamelCase,  __UpperCamelCase )

							_snake_case	       =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
							self.parent.assertTrue(diff < 1E-3,  msg=f'''Max diff is {diff}''' )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       20
							_snake_case	       =       model_class_name(__UpperCamelCase )

							_snake_case	       =       model.encode(inputs_dict["input_ids"] )

							_snake_case       ,    _snake_case	       =       (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							_snake_case	       =       jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
							    ],  axis=-1,  )

							_snake_case	       =       model.init_cache(decoder_input_ids.shape[0],  __UpperCamelCase,  __UpperCamelCase )
							_snake_case	       =       jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :],  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),  )

							_snake_case	       =       model.decode(
							    decoder_input_ids[:, :-1],  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  past_key_values=__UpperCamelCase,  decoder_position_ids=__UpperCamelCase,  )
							_snake_case	       =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype="i4" )
							_snake_case	       =       model.decode(
							    decoder_input_ids[:, -1:],  __UpperCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_attention_mask=__UpperCamelCase,  decoder_position_ids=__UpperCamelCase,  )

							_snake_case	       =       model.decode(__UpperCamelCase,  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase )

							_snake_case	       =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
							self.parent.assertTrue(diff < 1E-3,  msg=f'''Max diff is {diff}''' )





@require_flax
class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = 9_9






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ],  dtype=np.intaa,  )

							_snake_case	       =       input_ids.shape[0]
							_snake_case	       =       MBartConfig(
							    vocab_size=self.vocab_size,  d_model=24,  encoder_layers=2,  decoder_layers=2,  encoder_attention_heads=2,  decoder_attention_heads=2,  encoder_ffn_dim=32,  decoder_ffn_dim=32,  max_position_embeddings=48,  eos_token_id=2,  pad_token_id=1,  bos_token_id=0,  )
							return config, input_ids, batch_size






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case       ,    _snake_case	       =       self._get_config_and_data()
							_snake_case	       =       FlaxMBartForSequenceClassification(__UpperCamelCase )
							_snake_case	       =       model(input_ids=__UpperCamelCase,  decoder_input_ids=__UpperCamelCase )
							_snake_case	       =       (batch_size, config.num_labels)
							self.assertEqual(outputs["logits"].shape,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case       ,    _snake_case	       =       self._get_config_and_data()
							_snake_case	       =       FlaxMBartForQuestionAnswering(__UpperCamelCase )
							_snake_case	       =       model(input_ids=__UpperCamelCase )

							self.assertEqual(outputs["start_logits"].shape,  input_ids.shape )
							self.assertEqual(outputs["end_logits"].shape,  input_ids.shape )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case       ,    _snake_case	       =       self._get_config_and_data()
							_snake_case	       =       FlaxMBartForConditionalGeneration(__UpperCamelCase )
							_snake_case	       =       lm_model(input_ids=__UpperCamelCase )
							_snake_case	       =       (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       MBartConfig(
							    vocab_size=self.vocab_size,  d_model=14,  encoder_layers=2,  decoder_layers=2,  encoder_attention_heads=2,  decoder_attention_heads=2,  encoder_ffn_dim=8,  decoder_ffn_dim=8,  max_position_embeddings=48,  )
							_snake_case	       =       FlaxMBartForConditionalGeneration(__UpperCamelCase )
							_snake_case	       =       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],  dtype=np.intaa )
							_snake_case	       =       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],  dtype=np.intaa )
							_snake_case	       =       lm_model(input_ids=__UpperCamelCase,  decoder_input_ids=__UpperCamelCase )
							_snake_case	       =       (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],  dtype=np.intaa )
							_snake_case	       =       shift_tokens_right(__UpperCamelCase,  1 )
							_snake_case	       =       np.equal(__UpperCamelCase,  1 ).astype(np.floataa ).sum()
							_snake_case	       =       np.equal(__UpperCamelCase,  1 ).astype(np.floataa ).sum()
							self.assertEqual(shifted.shape,  input_ids.shape )
							self.assertEqual(__UpperCamelCase,  n_pad_before - 1 )
							self.assertTrue(np.equal(shifted[:, 0],  2 ).all() )





@require_flax
class      lowerCamelCase_							(       UpperCAmelCase_,       unittest.TestCase,       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = True
					lowercase__    = (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					lowercase__    = (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       FlaxMBartModelTester(self )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__ ):
											_snake_case	       =       self._prepare_for_class(__UpperCamelCase,  __UpperCamelCase )
											_snake_case	       =       model_class(__UpperCamelCase )

											@jax.jit
											def encode_jitted(__UpperCamelCase,  __UpperCamelCase=None,  **__UpperCamelCase ):
													return model.encode(input_ids=__UpperCamelCase,  attention_mask=__UpperCamelCase )

											with self.subTest("JIT Enabled" ):
													_snake_case	       =       encode_jitted(**__UpperCamelCase ).to_tuple()

											with self.subTest("JIT Disabled" ):
													with jax.disable_jit():
															_snake_case	       =       encode_jitted(**__UpperCamelCase ).to_tuple()

											self.assertEqual(len(__UpperCamelCase ),  len(__UpperCamelCase ) )
											for jitted_output, output in zip(__UpperCamelCase,  __UpperCamelCase ):
													self.assertEqual(jitted_output.shape,  output.shape )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__ ):
											_snake_case	       =       model_class(__UpperCamelCase )
											_snake_case	       =       model.encode(inputs_dict["input_ids"],  inputs_dict["attention_mask"] )

											_snake_case	       =       {
											    "decoder_input_ids": inputs_dict["decoder_input_ids"],
											    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
											    "encoder_outputs": encoder_outputs,
											}

											@jax.jit
											def decode_jitted(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
													return model.decode(
													    decoder_input_ids=__UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  encoder_outputs=__UpperCamelCase,  )

											with self.subTest("JIT Enabled" ):
													_snake_case	       =       decode_jitted(**__UpperCamelCase ).to_tuple()

											with self.subTest("JIT Disabled" ):
													with jax.disable_jit():
															_snake_case	       =       decode_jitted(**__UpperCamelCase ).to_tuple()

											self.assertEqual(len(__UpperCamelCase ),  len(__UpperCamelCase ) )
											for jitted_output, output in zip(__UpperCamelCase,  __UpperCamelCase ):
													self.assertEqual(jitted_output.shape,  output.shape )






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for model_class_name in self.all_model_classes:
									_snake_case	       =       model_class_name.from_pretrained("facebook/mbart-large-cc25",  from_pt=__UpperCamelCase )
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									_snake_case	       =       np.ones((1, 1) ) * model.config.eos_token_id
									_snake_case	       =       model(__UpperCamelCase )
									self.assertIsNotNone(__UpperCamelCase )





@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = [
					    """ UN Chief Says There Is No Military Solution in Syria""",
					]
					lowercase__    = [
					    """Şeful ONU declară că nu există o soluţie militară în Siria""",
					]
					lowercase__    = """facebook/mbart-large-en-ro"""






					@cached_property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return AutoTokenizer.from_pretrained(self.model_name )






					@cached_property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,  from_pt=__UpperCamelCase )
							return model






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							_snake_case	       =       self.translate_src_text(**__UpperCamelCase )
							self.assertListEqual(self.expected_text,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							_snake_case	       =       self.tokenizer(self.src_text,  **__UpperCamelCase,  return_tensors="np" )
							_snake_case	       =       self.model.generate(
							    model_inputs.input_ids,  attention_mask=model_inputs.attention_mask,  decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"],  early_stopping=__UpperCamelCase,  num_beams=2,  ).sequences
							_snake_case	       =       self.tokenizer.batch_decode(__UpperCamelCase,  skip_special_tokens=__UpperCamelCase )
							return generated_words






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self._assert_generated_batch_equal_expected()











'''simple docstring'''







from __future__ import annotations

from math import pi, pow






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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(lowercase_						,							2  ) * (3 * radius - height)






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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(lowercase_						,							lowercase_  )  )

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

		return vol_spherical_cap(lowercase_						,							lowercase_  ) + vol_spherical_cap(lowercase_						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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(lowercase_						,							lowercase_  )  )

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






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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		(	lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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(lowercase_						,							2  ) - pow(lowercase_						,							2  )) * height






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		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(lowercase_						,							2  ) + pow(lowercase_						,							2  ) + radius_a * radius_a)
		)






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	):



		'''simple docstring'''







		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()





