

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       :  str			,		A       :  Optional[Any]			,		A       :  Tuple=13			,		A       :  Tuple=7			,		A       :  Optional[Any]=True			,		A       :  List[Any]=True			,		A       :  Optional[int]=True			,		A       :  List[str]=True			,		A       :  Dict=99			,		A       :  List[str]=32			,		A       :  Optional[int]=5			,		A       :  Optional[int]=4			,		A       :  str=37			,		A       :  Tuple="gelu"			,		A       :  List[str]=0.1			,		A       :  List[str]=0.1			,		A       :  List[Any]=5_12			,		A       :  Tuple=16			,		A       :  List[str]=2			,		A       :  int=0.0_2			,		A       :  Optional[int]=4			,		)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_attention_mask
						_UpperCAmelCase			=       use_token_type_ids
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       type_sequence_label_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       num_choices



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



						"""simple docstring"""






						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

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

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

						_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  int)       ->   List[Any]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       config_and_inputs

						_UpperCAmelCase			=       True
						_UpperCAmelCase			=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
						_UpperCAmelCase			=       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   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					True

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



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



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxRobertaPreLayerNormModelTester(self)



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



						"""simple docstring"""






						for model_class_name in self.all_model_classes:
									_UpperCAmelCase			=       model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,		from_pt=lowerCamelCase_)
									_UpperCAmelCase			=       model(np.ones((1, 1)))
									self.assertIsNotNone(lowerCamelCase_)







@require_flax
class       __lowerCAmelCase   (      unittest.TestCase						):



			@slow
			def    _lowerCamelCase	(						self       :  Dict)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,		from_pt=lowerCamelCase_)

						_UpperCAmelCase			=       np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			,		dtype=jnp.intaa)
						_UpperCAmelCase			=       model(lowerCamelCase_)[0]
						_UpperCAmelCase			=       [1, 11, 5_02_65]
						self.assertEqual(list(output.shape)			,		lowerCamelCase_)
						# compare the actual values for a slice.
						_UpperCAmelCase			=       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]			,		lowerCamelCase_			,		atol=1E-4))



			@slow
			def    _lowerCamelCase	(						self       :  int)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,		from_pt=lowerCamelCase_)

						_UpperCAmelCase			=       np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			,		dtype=jnp.intaa)
						_UpperCAmelCase			=       model(lowerCamelCase_)[0]
						# compare the actual values for a slice.
						_UpperCAmelCase			=       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]			,		lowerCamelCase_			,		atol=1E-4))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["DebertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DebertaForMaskedLM",
							    "DebertaForQuestionAnswering",
							    "DebertaForSequenceClassification",
							    "DebertaForTokenClassification",
							    "DebertaModel",
							    "DebertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDebertaForMaskedLM",
							    "TFDebertaForQuestionAnswering",
							    "TFDebertaForSequenceClassification",
							    "TFDebertaForTokenClassification",
							    "TFDebertaModel",
							    "TFDebertaPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
