


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 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=4			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					parent
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					seq_length
										SCREAMING_SNAKE_CASE_		:     List[str]					=					is_training
										SCREAMING_SNAKE_CASE_		:     Any					=					use_attention_mask
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_token_type_ids
										SCREAMING_SNAKE_CASE_		:     Tuple					=					use_labels
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     int					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     str					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Dict					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					type_vocab_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					type_sequence_label_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					num_choices






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					None
										if self.use_attention_mask:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					random_attention_mask([self.batch_size, self.seq_length]       )

										SCREAMING_SNAKE_CASE_		:     Any					=					None
										if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

										SCREAMING_SNAKE_CASE_		:     int					=					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=lowerCAmelCase__			,						initializer_range=self.initializer_range			,						)

										return config, input_ids, token_type_ids, attention_mask






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					config_and_inputs
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Dict					=					config_and_inputs

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					True
										SCREAMING_SNAKE_CASE_		:     Any					=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										SCREAMING_SNAKE_CASE_		:     Optional[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 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						True

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					FlaxRobertaPreLayerNormModelTester(self       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,						from_pt=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model(np.ones((1, 1)       )       )
													self.assertIsNotNone(lowerCAmelCase__       )







@require_flax
class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,						from_pt=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			,						dtype=jnp.intaa       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					model(lowerCAmelCase__       )[0]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[1, 1_1, 5_0_2_6_5]
										self.assertEqual(list(output.shape       )			,						lowerCAmelCase__       )
										# compare the actual values for a slice.
										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.array(
										    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]			,						dtype=np.floataa       )
										self.assertTrue(np.allclose(output[:, :3, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,						from_pt=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			,						dtype=jnp.intaa       )
										SCREAMING_SNAKE_CASE_		:     Any					=					model(lowerCAmelCase__       )[0]
										# compare the actual values for a slice.
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array(
										    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]			,						dtype=np.floataa       )
										self.assertTrue(np.allclose(output[:, :3, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )








'''simple docstring'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


if __name__ == "__main__":
							print(solution())





