






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  lowerCAmelCase_       (				unittest.TestCase					):




							def __init__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Any=13 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=7 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=True ,							SCREAMING_SNAKE_CASE_     :   Dict=True ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							SCREAMING_SNAKE_CASE_     :   Any=99 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=32 ,							SCREAMING_SNAKE_CASE_     :   Tuple=5 ,							SCREAMING_SNAKE_CASE_     :   str=4 ,							SCREAMING_SNAKE_CASE_     :   Dict=37 ,							SCREAMING_SNAKE_CASE_     :   List[str]="gelu" ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[str]=512 ,							SCREAMING_SNAKE_CASE_     :   int=16 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=2 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.02 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=4 ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						seq_length
											lowerCAmelCase__      			=						is_training
											lowerCAmelCase__      			=						use_attention_mask
											lowerCAmelCase__      			=						use_token_type_ids
											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__      			=						type_vocab_size
											lowerCAmelCase__      			=						type_sequence_label_size
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						num_choices




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size	)

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

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

											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.prepare_config_and_inputs()
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						config_and_inputs
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						self.prepare_config_and_inputs()
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						config_and_inputs

											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
											lowerCAmelCase__      			=						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  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Dict		      =    True

							UpperCamelCase_		:List[Any]		      =    (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						FlaxRobertaPreLayerNormModelTester(self	)




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



@require_flax
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,							from_pt=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] ,							dtype=jnp.intaa	)
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											lowerCAmelCase__      			=						[1, 11, 50_265]
											self.assertEqual(list(output.shape	) ,							SCREAMING_SNAKE_CASE_	)
											# compare the actual values for a slice.
											lowerCAmelCase__      			=						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] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)




							@slow
							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,							from_pt=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] ,							dtype=jnp.intaa	)
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											# compare the actual values for a slice.
											lowerCAmelCase__      			=						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] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


_UpperCAmelCase			:		Optional[Any]   				=    {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ErnieForCausalLM",
			    "ErnieForMaskedLM",
			    "ErnieForMultipleChoice",
			    "ErnieForNextSentencePrediction",
			    "ErnieForPreTraining",
			    "ErnieForQuestionAnswering",
			    "ErnieForSequenceClassification",
			    "ErnieForTokenClassification",
			    "ErnieModel",
			    "ErniePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ernie import (
						    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ErnieForCausalLM,
						    ErnieForMaskedLM,
						    ErnieForMultipleChoice,
						    ErnieForNextSentencePrediction,
						    ErnieForPreTraining,
						    ErnieForQuestionAnswering,
						    ErnieForSequenceClassification,
						    ErnieForTokenClassification,
						    ErnieModel,
						    ErniePreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





