
"""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 __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def __init__(					self    :       Tuple							, _snake_case    :       int							, _snake_case    :       Dict=13							, _snake_case    :       Optional[Any]=7							, _snake_case    :       Optional[Any]=True							, _snake_case    :       Dict=True							, _snake_case    :       List[str]=True							, _snake_case    :       Optional[Any]=True							, _snake_case    :       Union[str, Any]=99							, _snake_case    :       int=32							, _snake_case    :       Optional[Any]=5							, _snake_case    :       List[str]=4							, _snake_case    :       Dict=37							, _snake_case    :       int="gelu"							, _snake_case    :       List[Any]=0.1							, _snake_case    :       Any=0.1							, _snake_case    :       Optional[Any]=512							, _snake_case    :       Union[str, Any]=16							, _snake_case    :       List[str]=2							, _snake_case    :       Optional[int]=0.0_2							, _snake_case    :       List[str]=4							, )   ->			int:







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




			def   lowerCamelCase__  (					self    :       int						)   ->			Tuple:







								"""simple docstring"""
								A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

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

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

								A_								=    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=_snake_case							, initializer_range=self.initializer_range							, )

								return config, input_ids, token_type_ids, attention_mask




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Dict:







								"""simple docstring"""
								A_								=    self.prepare_config_and_inputs()
								A_			,     A_			,     A_			,     A_								=    config_and_inputs
								A_								=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict






			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    self.prepare_config_and_inputs()
								A_			,     A_			,     A_			,     A_								=    config_and_inputs

								A_								=    True
								A_								=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
								A_								=    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					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					True

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




			def   lowerCamelCase__  (					self    :       Any						)   ->			Dict:







								"""simple docstring"""
								A_								=    FlaxRobertaPreLayerNormModelTester(self						)






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







								"""simple docstring"""
								for model_class_name in self.all_model_classes:
													A_								=    model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							, from_pt=_snake_case						)
													A_								=    model(np.ones((1, 1)						)						)
													self.assertIsNotNone(_snake_case						)





@require_flax
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







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







								"""simple docstring"""
								A_								=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							, from_pt=_snake_case						)

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






			@slow
			def   lowerCamelCase__  (					self    :       Tuple						)   ->			int:







								"""simple docstring"""
								A_								=    FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							, from_pt=_snake_case						)

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






"""simple docstring"""





from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# TODO: upload to AWS
UpperCamelCase_       :    List[str]						      =			{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"retribert"




			def __init__(					self    :       str							, _snake_case    :       List[str]=30_522							, _snake_case    :       List[Any]=768							, _snake_case    :       List[Any]=8							, _snake_case    :       Union[str, Any]=12							, _snake_case    :       Optional[Any]=3_072							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       Dict=0.1							, _snake_case    :       str=512							, _snake_case    :       Tuple=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Optional[Any]=1e-12							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=128							, _snake_case    :       Union[str, Any]=0							, **_snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								super().__init__(pad_token_id=_snake_case							, **_snake_case						)

								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    hidden_act
								A_								=    intermediate_size
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    max_position_embeddings
								A_								=    type_vocab_size
								A_								=    initializer_range
								A_								=    layer_norm_eps
								A_								=    share_encoders
								A_								=    projection_dim




