



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):




				def __init__(						self					:						Tuple,					__lowercase					:						Any,					__lowercase					:						List[Any]=13,					__lowercase					:						Union[str, Any]=7,					__lowercase					:						Tuple=True,					__lowercase					:						int=True,					__lowercase					:						List[str]=True,					__lowercase					:						Any=True,					__lowercase					:						Optional[Any]=99,					__lowercase					:						List[Any]=32,					__lowercase					:						Tuple=5,					__lowercase					:						Optional[int]=4,					__lowercase					:						Dict=37,					__lowercase					:						str="gelu",					__lowercase					:						List[str]=0.1,					__lowercase					:						List[str]=0.1,					__lowercase					:						Tuple=512,					__lowercase					:						Tuple=16,					__lowercase					:						List[str]=2,					__lowercase					:						int=0.02,					__lowercase					:						List[Any]=4,					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				seq_length
							lowercase__					  =				is_training
							lowercase__					  =				use_attention_mask
							lowercase__					  =				use_token_type_ids
							lowercase__					  =				use_labels
							lowercase__					  =				vocab_size
							lowercase__					  =				hidden_size
							lowercase__					  =				num_hidden_layers
							lowercase__					  =				num_attention_heads
							lowercase__					  =				intermediate_size
							lowercase__					  =				hidden_act
							lowercase__					  =				hidden_dropout_prob
							lowercase__					  =				attention_probs_dropout_prob
							lowercase__					  =				max_position_embeddings
							lowercase__					  =				type_vocab_size
							lowercase__					  =				type_sequence_label_size
							lowercase__					  =				initializer_range
							lowercase__					  =				num_choices




				def 							A__      (						self					:						int						):
							lowercase__					  =				ids_tensor([self.batch_size, self.seq_length],					self.vocab_size						)

							lowercase__					  =				None
							if self.use_attention_mask:
										lowercase__					  =				random_attention_mask([self.batch_size, self.seq_length]						)

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

							lowercase__					  =				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=__lowercase,					initializer_range=self.initializer_range,					)

							return config, input_ids, token_type_ids, attention_mask




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs
							lowercase__					  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
							return config, inputs_dict




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs

							lowercase__					  =				True
							lowercase__					  =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
							lowercase__					  =				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			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					int     		=True

				UpperCamelCase__				:					Union[str, Any]     		=(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				FlaxRobertaPreLayerNormModelTester(self						)




				@slow
				def 							A__      (						self					:						Tuple						):
							for model_class_name in self.all_model_classes:
										lowercase__					  =				model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40",					from_pt=__lowercase						)
										lowercase__					  =				model(np.ones((1, 1)						)						)
										self.assertIsNotNone(__lowercase						)
@require_flax
class 				_snake_case			(					unittest.TestCase):




				@slow
				def 							A__      (						self					:						str						):
							lowercase__					  =				FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40",					from_pt=__lowercase						)

							lowercase__					  =				np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],					dtype=jnp.intaa						)
							lowercase__					  =				model(__lowercase						)[0]
							lowercase__					  =				[1, 11, 5_0265]
							self.assertEqual(list(output.shape						),					__lowercase						)
							# compare the actual values for a slice.
							lowercase__					  =				np.array(
							    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]],					dtype=np.floataa						)
							self.assertTrue(np.allclose(output[:, :3, :3],					__lowercase,					atol=1e-4						)						)




				@slow
				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40",					from_pt=__lowercase						)

							lowercase__					  =				np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],					dtype=jnp.intaa						)
							lowercase__					  =				model(__lowercase						)[0]
							# compare the actual values for a slice.
							lowercase__					  =				np.array(
							    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]],					dtype=np.floataa						)
							self.assertTrue(np.allclose(output[:, :3, :3],					__lowercase,					atol=1e-4						)						)









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				0
			for ch in input_str:
						lowercase__					  =				ord(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				pow(2						,  SCREAMING_SNAKE_CASE_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




