






"""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
						_snake_case       :      Optional[int]       =					"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 						lowerCamelCase_				(  __A  :		int			,	__A  :		str			,	__A  :		List[Any]=None			,	__A  :		Tuple=None			,	__A  :		Dict=None			,	__A  :		Tuple=None			,	__A  :		Union[str, Any]=None			,	__A  :		List[str]=None			,	):
					if attention_mask is None:
										lowerCamelCase__				=np.where(input_ids != config.pad_token_id			,	1			,	0			)
					if decoder_attention_mask is None:
										lowerCamelCase__				=np.where(decoder_input_ids != config.pad_token_id			,	1			,	0			)
					if head_mask is None:
										lowerCamelCase__				=np.ones((config.encoder_layers, config.encoder_attention_heads)			)
					if decoder_head_mask is None:
										lowerCamelCase__				=np.ones((config.decoder_layers, config.decoder_attention_heads)			)
					if cross_attn_head_mask is None:
										lowerCamelCase__				=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        __lowercase(       unittest.TestCase  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=13    ,						_SCREAMING_SNAKE_CASE=7    ,						_SCREAMING_SNAKE_CASE=True    ,						_SCREAMING_SNAKE_CASE=False    ,						_SCREAMING_SNAKE_CASE=99    ,						_SCREAMING_SNAKE_CASE=16    ,						_SCREAMING_SNAKE_CASE=2    ,						_SCREAMING_SNAKE_CASE=4    ,						_SCREAMING_SNAKE_CASE=4    ,						_SCREAMING_SNAKE_CASE="gelu"    ,						_SCREAMING_SNAKE_CASE=0.1    ,						_SCREAMING_SNAKE_CASE=0.1    ,						_SCREAMING_SNAKE_CASE=32    ,						_SCREAMING_SNAKE_CASE=2    ,						_SCREAMING_SNAKE_CASE=1    ,						_SCREAMING_SNAKE_CASE=0    ,						_SCREAMING_SNAKE_CASE=2    ,						_SCREAMING_SNAKE_CASE=0.02    ,						)			->  List[Any]:




											"""simple docstring"""





											lowerCamelCase__				=parent
											lowerCamelCase__				=batch_size
											lowerCamelCase__				=seq_length
											lowerCamelCase__				=is_training
											lowerCamelCase__				=use_labels
											lowerCamelCase__				=vocab_size
											lowerCamelCase__				=hidden_size
											lowerCamelCase__				=num_hidden_layers
											lowerCamelCase__				=num_attention_heads
											lowerCamelCase__				=intermediate_size
											lowerCamelCase__				=hidden_act
											lowerCamelCase__				=hidden_dropout_prob
											lowerCamelCase__				=attention_probs_dropout_prob
											lowerCamelCase__				=max_position_embeddings
											lowerCamelCase__				=eos_token_id
											lowerCamelCase__				=pad_token_id
											lowerCamelCase__				=bos_token_id
											lowerCamelCase__				=decoder_start_token_id
											lowerCamelCase__				=initializer_range






						def 		lowerCAmelCase   (					self)			->  int:




											"""simple docstring"""





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

											lowerCamelCase__				=shift_tokens_right(_SCREAMING_SNAKE_CASE    ,						1)

											lowerCamelCase__				=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=_SCREAMING_SNAKE_CASE    ,						)
											lowerCamelCase__				=prepare_mbart_inputs_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											return config, inputs_dict






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




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				=self.prepare_config_and_inputs()
											return config, inputs_dict






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  List[Any]:




											"""simple docstring"""





											lowerCamelCase__				=20
											lowerCamelCase__				=model_class_name(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=model.encode(inputs_dict['input_ids'])

											lowerCamelCase__				,     lowerCamelCase__				=(
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											lowerCamelCase__				=model.init_cache(decoder_input_ids.shape[0]    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,						dtype='i4')

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

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

											lowerCamelCase__				=model.decode(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=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 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  List[Any]:




											"""simple docstring"""





											lowerCamelCase__				=20
											lowerCamelCase__				=model_class_name(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=model.encode(inputs_dict['input_ids'])

											lowerCamelCase__				,     lowerCamelCase__				=(
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

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

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

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

											lowerCamelCase__				=model.decode(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=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        __lowercase(       unittest.TestCase  ):
						lowerCAmelCase_		:							int=       99






						def 		lowerCAmelCase   (					self)			->  Optional[int]:




											"""simple docstring"""





											lowerCamelCase__				=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    ,						)

											lowerCamelCase__				=input_ids.shape[0]
											lowerCamelCase__				=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 		lowerCAmelCase   (					self)			->  str:




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=self._get_config_and_data()
											lowerCamelCase__				=FlaxMBartForSequenceClassification(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=model(input_ids=_SCREAMING_SNAKE_CASE    ,						decoder_input_ids=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=(batch_size, config.num_labels)
											self.assertEqual(outputs['logits'].shape    ,						_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  Dict:




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=self._get_config_and_data()
											lowerCamelCase__				=FlaxMBartForQuestionAnswering(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=model(input_ids=_SCREAMING_SNAKE_CASE)

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






						def 		lowerCAmelCase   (					self)			->  Optional[int]:




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=self._get_config_and_data()
											lowerCamelCase__				=FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=lm_model(input_ids=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=(batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape    ,						_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  Dict:




											"""simple docstring"""





											lowerCamelCase__				=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    ,						)
											lowerCamelCase__				=FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,						dtype=np.intaa)
											lowerCamelCase__				=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,						dtype=np.intaa)
											lowerCamelCase__				=lm_model(input_ids=_SCREAMING_SNAKE_CASE    ,						decoder_input_ids=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=(*summary.shape, config.vocab_size)
											self.assertEqual(outputs['logits'].shape    ,						_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  Dict:




											"""simple docstring"""





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




@require_flax
class        __lowercase(       _UpperCamelCase						,							unittest.TestCase						,							_UpperCamelCase  ):
						lowerCAmelCase_		:							Union[str, Any]=       True
						lowerCAmelCase_		:							Optional[Any]=       (
						    (
						        FlaxMBartModel,
						        FlaxMBartForConditionalGeneration,
						        FlaxMBartForSequenceClassification,
						        FlaxMBartForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)
						lowerCAmelCase_		:							Dict=       (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






						def 		lowerCAmelCase   (					self)			->  Dict:




											"""simple docstring"""





											lowerCamelCase__				=FlaxMBartModelTester(self)






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




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				=self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)






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




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				=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(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)






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




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				=self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__):
																					lowerCamelCase__				=self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__				=model_class(_SCREAMING_SNAKE_CASE)

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

																					with self.subTest('JIT Enabled'):
																										lowerCamelCase__				=encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																					with self.subTest('JIT Disabled'):
																										with jax.disable_jit():
																															lowerCamelCase__				=encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

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






						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				=self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__):
																					lowerCamelCase__				=model_class(_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__				=model.encode(inputs_dict['input_ids']    ,						inputs_dict['attention_mask'])

																					lowerCamelCase__				={
																					    '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(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE):
																										return model.decode(
																										    decoder_input_ids=_SCREAMING_SNAKE_CASE    ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE    ,						encoder_outputs=_SCREAMING_SNAKE_CASE    ,						)

																					with self.subTest('JIT Enabled'):
																										lowerCamelCase__				=decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																					with self.subTest('JIT Disabled'):
																										with jax.disable_jit():
																															lowerCamelCase__				=decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

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






						@slow
						def 		lowerCAmelCase   (					self)			->  Optional[Any]:




											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																lowerCamelCase__				=model_class_name.from_pretrained('facebook/mbart-large-cc25'    ,						from_pt=_SCREAMING_SNAKE_CASE)
																# FlaxMBartForSequenceClassification expects eos token in input_ids
																lowerCamelCase__				=np.ones((1, 1)) * model.config.eos_token_id
																lowerCamelCase__				=model(_SCREAMING_SNAKE_CASE)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE)




@require_flax
@require_sentencepiece
@require_tokenizers
class        __lowercase(       unittest.TestCase  ):
						lowerCAmelCase_		:							Union[str, Any]=       [
						    """ UN Chief Says There Is No Military Solution in Syria""",
						]
						lowerCAmelCase_		:							int=       [
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						]
						lowerCAmelCase_		:							Union[str, Any]=       """facebook/mbart-large-en-ro"""






						@cached_property
						def 		lowerCAmelCase   (					self)			->  str:




											"""simple docstring"""





											return AutoTokenizer.from_pretrained(self.model_name)






						@cached_property
						def 		lowerCAmelCase   (					self)			->  List[Any]:




											"""simple docstring"""





											lowerCamelCase__				=FlaxMBartForConditionalGeneration.from_pretrained(self.model_name    ,						from_pt=_SCREAMING_SNAKE_CASE)
											return model






						def 		lowerCAmelCase   (					self    ,						**_SCREAMING_SNAKE_CASE)			->  Dict:




											"""simple docstring"""





											lowerCamelCase__				=self.translate_src_text(**_SCREAMING_SNAKE_CASE)
											self.assertListEqual(self.expected_text    ,						_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self    ,						**_SCREAMING_SNAKE_CASE)			->  str:




											"""simple docstring"""





											lowerCamelCase__				=self.tokenizer(self.src_text    ,						**_SCREAMING_SNAKE_CASE    ,						return_tensors='np')
											lowerCamelCase__				=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=_SCREAMING_SNAKE_CASE    ,						num_beams=2    ,						).sequences
											lowerCamelCase__				=self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,						skip_special_tokens=_SCREAMING_SNAKE_CASE)
											return generated_words






						@slow
						def 		lowerCAmelCase   (					self)			->  List[Any]:




											"""simple docstring"""





											self._assert_generated_batch_equal_expected()







"""simple docstring"""



from doctest import testmod
from math import sqrt




def 						lowerCamelCase_				(  __A  :		int			):
					lowerCamelCase__				=[]
					if num < 1:
										return facs
					facs.append(1			)
					if num == 1:
										return facs
					facs.append(__A			)
					for i in range(2			,	int(sqrt(__A			)			) + 1			):
										if num % i == 0:  # If i is a factor of num
															facs.append(__A			)
															lowerCamelCase__				=num // i  # num//i is the other factor of num
															if d != i:  # If d and i are distinct
																				facs.append(__A			)  # we have found another factor
					facs.sort()
					return facs


if __name__ == "__main__":
						testmod(name="factors_of_a_number", verbose=True)