



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  ):




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=13     ,lowercase=7     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=99     ,lowercase=32     ,lowercase=5     ,lowercase=4     ,lowercase=37     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=16     ,lowercase=2     ,lowercase=0.02     ,lowercase=4     ,):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

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

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

             UpperCAmelCase_  :	str				      =		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):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_  :	Tuple				      =		config_and_inputs
             UpperCAmelCase_  :	List[str]				      =		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
             return config, inputs_dict



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_  :	Optional[int]				      =		config_and_inputs

             UpperCAmelCase_  :	List[str]				      =		True
             UpperCAmelCase_  :	Dict				      =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
             UpperCAmelCase_  :	List[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   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				True

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		FlaxRobertaPreLayerNormModelTester(self)



       @slow
       def  A_   (     self):

             """simple docstring"""





             for model_class_name in self.all_model_classes:
                   UpperCAmelCase_  :	str				      =		model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,from_pt=lowercase)
                   UpperCAmelCase_  :	Dict				      =		model(np.ones((1, 1)))
                   self.assertIsNotNone(lowercase)





@require_flax
class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,from_pt=lowercase)

             UpperCAmelCase_  :	Optional[int]				      =		np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     ,dtype=jnp.intaa)
             UpperCAmelCase_  :	Dict				      =		model(lowercase)[0]
             UpperCAmelCase_  :	Tuple				      =		[1, 11, 50265]
             self.assertEqual(list(output.shape)     ,lowercase)
             # compare the actual values for a slice.
             UpperCAmelCase_  :	Optional[int]				      =		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):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,from_pt=lowercase)

             UpperCAmelCase_  :	Optional[Any]				      =		np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     ,dtype=jnp.intaa)
             UpperCAmelCase_  :	List[str]				      =		model(lowercase)[0]
             # compare the actual values for a slice.
             UpperCAmelCase_  :	int				      =		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))





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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""lxmert"""
       _lowerCamelCase			    =				{}



       def __init__(     self     ,lowercase=30522     ,lowercase=768     ,lowercase=12     ,lowercase=9500     ,lowercase=1600     ,lowercase=400     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=2     ,lowercase=0.02     ,lowercase=1E-12     ,lowercase=9     ,lowercase=5     ,lowercase=5     ,lowercase=2048     ,lowercase=4     ,lowercase=6.67     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		vocab_size
             UpperCAmelCase_  :	str				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_attention_heads
             UpperCAmelCase_  :	Dict				      =		hidden_act
             UpperCAmelCase_  :	List[Any]				      =		intermediate_size
             UpperCAmelCase_  :	Tuple				      =		hidden_dropout_prob
             UpperCAmelCase_  :	List[Any]				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Any				      =		max_position_embeddings
             UpperCAmelCase_  :	str				      =		type_vocab_size
             UpperCAmelCase_  :	Any				      =		initializer_range
             UpperCAmelCase_  :	Optional[Any]				      =		layer_norm_eps
             UpperCAmelCase_  :	Any				      =		num_qa_labels
             UpperCAmelCase_  :	str				      =		num_object_labels
             UpperCAmelCase_  :	Dict				      =		num_attr_labels
             UpperCAmelCase_  :	Tuple				      =		l_layers
             UpperCAmelCase_  :	Tuple				      =		x_layers
             UpperCAmelCase_  :	int				      =		r_layers
             UpperCAmelCase_  :	Optional[Any]				      =		visual_feat_dim
             UpperCAmelCase_  :	List[Any]				      =		visual_pos_dim
             UpperCAmelCase_  :	int				      =		visual_loss_normalizer
             UpperCAmelCase_  :	str				      =		task_matched
             UpperCAmelCase_  :	str				      =		task_mask_lm
             UpperCAmelCase_  :	int				      =		task_obj_predict
             UpperCAmelCase_  :	List[str]				      =		task_qa
             UpperCAmelCase_  :	Optional[int]				      =		visual_obj_loss
             UpperCAmelCase_  :	List[str]				      =		visual_attr_loss
             UpperCAmelCase_  :	str				      =		visual_feat_loss
             UpperCAmelCase_  :	List[Any]				      =		{"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
             super().__init__(**lowercase)
