


'''simple docstring'''







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)






class 		_snake_case     (					unittest.TestCase		):






						'''simple docstring'''







						def __init__(	self:						Tuple   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						Union[str, Any]=13   ,				__UpperCamelCase:						Any=7   ,				__UpperCamelCase:						Any=True   ,				__UpperCamelCase:						Dict=True   ,				__UpperCamelCase:						int=True   ,				__UpperCamelCase:						Optional[Any]=True   ,				__UpperCamelCase:						List[Any]=99   ,				__UpperCamelCase:						List[str]=32   ,				__UpperCamelCase:						int=5   ,				__UpperCamelCase:						List[str]=4   ,				__UpperCamelCase:						Any=37   ,				__UpperCamelCase:						Optional[Any]="gelu"   ,				__UpperCamelCase:						Union[str, Any]=0.1   ,				__UpperCamelCase:						int=0.1   ,				__UpperCamelCase:						Any=512   ,				__UpperCamelCase:						Union[str, Any]=16   ,				__UpperCamelCase:						str=2   ,				__UpperCamelCase:						Tuple=0.0_2   ,				__UpperCamelCase:						List[Any]=4   ,				)				->						Tuple:
										__magic_name__					:       Optional[int]    							=							parent
										__magic_name__					:       str    							=							batch_size
										__magic_name__					:       Union[str, Any]    							=							seq_length
										__magic_name__					:       Dict    							=							is_training
										__magic_name__					:       Union[str, Any]    							=							use_attention_mask
										__magic_name__					:       Tuple    							=							use_token_type_ids
										__magic_name__					:       List[str]    							=							use_labels
										__magic_name__					:       Union[str, Any]    							=							vocab_size
										__magic_name__					:       Any    							=							hidden_size
										__magic_name__					:       Any    							=							num_hidden_layers
										__magic_name__					:       int    							=							num_attention_heads
										__magic_name__					:       Dict    							=							intermediate_size
										__magic_name__					:       str    							=							hidden_act
										__magic_name__					:       Dict    							=							hidden_dropout_prob
										__magic_name__					:       List[str]    							=							attention_probs_dropout_prob
										__magic_name__					:       int    							=							max_position_embeddings
										__magic_name__					:       Dict    							=							type_vocab_size
										__magic_name__					:       Any    							=							type_sequence_label_size
										__magic_name__					:       int    							=							initializer_range
										__magic_name__					:       List[str]    							=							num_choices







						def lowerCAmelCase__			(	self:						List[Any]  )				->						Union[str, Any]:
										__magic_name__					:       List[Any]    							=							ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size  )

										__magic_name__					:       List[str]    							=							None
										if self.use_attention_mask:
														__magic_name__					:       Optional[int]    							=							random_attention_mask([self.batch_size, self.seq_length]  )

										__magic_name__					:       Any    							=							None
										if self.use_token_type_ids:
														__magic_name__					:       Optional[int]    							=							ids_tensor([self.batch_size, self.seq_length]   ,				self.type_vocab_size  )

										__magic_name__					:       List[str]    							=							RobertaPreLayerNormConfig(
										    vocab_size=self.vocab_size   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				type_vocab_size=self.type_vocab_size   ,				is_decoder=_SCREAMING_SNAKE_CASE   ,				initializer_range=self.initializer_range   ,				)

										return config, input_ids, token_type_ids, attention_mask







						def lowerCAmelCase__			(	self:						List[str]  )				->						List[str]:
										__magic_name__					:       Dict    							=							self.prepare_config_and_inputs()
										__magic_name__					:       Any    							=							config_and_inputs
										__magic_name__					:       Optional[int]    							=							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict







						def lowerCAmelCase__			(	self:						List[str]  )				->						Union[str, Any]:
										__magic_name__					:       Tuple    							=							self.prepare_config_and_inputs()
										__magic_name__					:       Any    							=							config_and_inputs

										__magic_name__					:       Optional[Any]    							=							True
										__magic_name__					:       Dict    							=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
										__magic_name__					:       Union[str, Any]    							=							ids_tensor([self.batch_size, self.seq_length]   ,				vocab_size=2  )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 		_snake_case     (					__SCREAMING_SNAKE_CASE	,	unittest.TestCase		):






						'''simple docstring'''







						__snake_case				=   True

						__snake_case				=   (
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)







						def lowerCAmelCase__			(	self:						List[str]  )				->						List[Any]:
										__magic_name__					:       str    							=							FlaxRobertaPreLayerNormModelTester(self  )







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

										for model_class_name in self.all_model_classes:
														__magic_name__					:       Optional[int]    							=							model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,				from_pt=_SCREAMING_SNAKE_CASE  )
														__magic_name__					:       str    							=							model(np.ones((1, 1)  )  )
														self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )






@require_flax
class 		_snake_case     (					unittest.TestCase		):






						'''simple docstring'''







						@slow
						def lowerCAmelCase__			(	self:						int  )				->						int:
										__magic_name__					:       List[Any]    							=							FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,				from_pt=_SCREAMING_SNAKE_CASE  )

										__magic_name__					:       str    							=							np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   ,				dtype=jnp.intaa  )
										__magic_name__					:       int    							=							model(_SCREAMING_SNAKE_CASE  )[0]
										__magic_name__					:       Any    							=							[1, 11, 5_0265]
										self.assertEqual(list(output.shape  )   ,				_SCREAMING_SNAKE_CASE  )
										# compare the actual values for a slice.
										__magic_name__					:       List[Any]    							=							np.array(
										    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,				dtype=np.floataa  )
										self.assertTrue(np.allclose(output[:, :3, :3]   ,				_SCREAMING_SNAKE_CASE   ,				atol=1E-4  )  )







						@slow
						def lowerCAmelCase__			(	self:						Any  )				->						Any:
										__magic_name__					:       int    							=							FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,				from_pt=_SCREAMING_SNAKE_CASE  )

										__magic_name__					:       Union[str, Any]    							=							np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   ,				dtype=jnp.intaa  )
										__magic_name__					:       Dict    							=							model(_SCREAMING_SNAKE_CASE  )[0]
										# compare the actual values for a slice.
										__magic_name__					:       List[str]    							=							np.array(
										    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,				dtype=np.floataa  )
										self.assertTrue(np.allclose(output[:, :3, :3]   ,				_SCREAMING_SNAKE_CASE   ,				atol=1E-4  )  )




"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
	from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case				:  List[Any] =       get_tests_dir('fixtures/test_sentencepiece.model')

__snake_case				:  Union[str, Any] =       {'target_lang': 'fi', 'source_lang': 'en'}
__snake_case				:  Union[str, Any] =       '>>zh<<'
__snake_case				:  List[str] =       'Helsinki-NLP/'

if is_torch_available():
	__snake_case				:  Optional[int] =       'pt'
elif is_tf_available():
	__snake_case				:  List[Any] =       'tf'
else:
	__snake_case				:  Union[str, Any] =       'jax'



@require_sentencepiece
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      MarianTokenizer
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      True

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Tuple:

				"""simple docstring"""

				super().setUp()
				__lowerCAmelCase :    Dict				=			["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
				__lowerCAmelCase :    Union[str, Any]				=			dict(zip(_SCREAMING_SNAKE_CASE			,     range(len(_SCREAMING_SNAKE_CASE))))
				__lowerCAmelCase :    int				=			Path(self.tmpdirname)
				save_json(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["vocab"])
				save_json(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"])
				if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
							copyfile(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["source_spm"])
							copyfile(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["target_spm"])

				__lowerCAmelCase :    Tuple				=			MarianTokenizer.from_pretrained(self.tmpdirname)
				tokenizer.save_pretrained(self.tmpdirname)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			MarianTokenizer:

				"""simple docstring"""

				return MarianTokenizer.from_pretrained(self.tmpdirname			,     **_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				return (
				    "This is a test",
				    "This is a test",
				)

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

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			"</s>"
				__lowerCAmelCase :    int				=			0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			list(self.get_tokenizer().get_vocab().keys())

				self.assertEqual(vocab_keys[0]			,     "</s>")
				self.assertEqual(vocab_keys[1]			,     "<unk>")
				self.assertEqual(vocab_keys[-1]			,     "<pad>")
				self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     9)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Dict:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size			,     9)

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
				__lowerCAmelCase :    int				=			en_de_tokenizer(["I am a small frog"]			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			[38, 121, 14, 697, 3_8848, 0]
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     batch.input_ids[0])

				__lowerCAmelCase :    Tuple				=			tempfile.mkdtemp()
				en_de_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			[x.name for x in Path(_SCREAMING_SNAKE_CASE).glob("*")]
				self.assertIn("source.spm"			,     _SCREAMING_SNAKE_CASE)
				MarianTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.get_tokenizer()

				__lowerCAmelCase :    int				=			tok(
				    ["I am a small frog" * 1000, "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     truncation=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch.input_ids.shape			,     (2, 512))

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

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.get_tokenizer()
				__lowerCAmelCase :    str				=			tok(["I am a tiny frog", "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch_smaller.input_ids.shape			,     (2, 10))

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_SCREAMING_SNAKE_CASE			,     model_name="Helsinki-NLP/opus-mt-en-de"			,     revision="1a8c2263da11e68e50938f97e10cd57820bd504c"			,     decode_kwargs={"use_source_tokenizer": True}			,     )







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

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

				__lowerCAmelCase :    List[str]				=			"Tämä on testi"
				__lowerCAmelCase :    int				=			"This is a test"

				__lowerCAmelCase :    Union[str, Any]				=			[76, 7, 2047, 2]
				__lowerCAmelCase :    Dict				=			[69, 12, 11, 940, 2]

				__lowerCAmelCase :    List[str]				=			tokenizer(_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    str				=			tokenizer(text_target=_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			tokenizer.decode(_SCREAMING_SNAKE_CASE			,     skip_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)