




"""simple docstring"""



import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.albert.modeling_flax_albert import (
          FlaxAlbertForMaskedLM,
          FlaxAlbertForMultipleChoice,
          FlaxAlbertForPreTraining,
          FlaxAlbertForQuestionAnswering,
          FlaxAlbertForSequenceClassification,
          FlaxAlbertForTokenClassification,
          FlaxAlbertModel,
      )




class      UpperCAmelCase_  (						unittest.TestCase							):




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	str     ,			snake_case_						:	Union[str, Any]=13     ,			snake_case_						:	Any=7     ,			snake_case_						:	Tuple=True     ,			snake_case_						:	List[str]=True     ,			snake_case_						:	str=True     ,			snake_case_						:	Union[str, Any]=True     ,			snake_case_						:	Tuple=99     ,			snake_case_						:	str=32     ,			snake_case_						:	List[Any]=5     ,			snake_case_						:	str=4     ,			snake_case_						:	Any=37     ,			snake_case_						:	Optional[Any]="gelu"     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Optional[Any]=0.1     ,			snake_case_						:	str=512     ,			snake_case_						:	Optional[int]=16     ,			snake_case_						:	int=2     ,			snake_case_						:	Tuple=0.02     ,			snake_case_						:	int=4     ,			)				->							List[Any]:




     '''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 							__magic_name__							(   self						:	Union[str, Any]				)				->							List[Any]:




     '''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__	=    AlbertConfig(
         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 							__magic_name__							(   self						:	Optional[int]				)				->							Any:




     '''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






@require_flax
class      UpperCAmelCase_  (						A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )




    def 							__magic_name__							(   self						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    FlaxAlbertModelTester(self				)




    @slow
    def 							__magic_name__							(   self						:	List[Any]				)				->							int:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__	=    model_class_name.from_pretrained("albert-base-v2"				)
      A__	=    model(np.ones((1, 1)				)				)
      self.assertIsNotNone(lowerCamelCase_				)






@require_flax
class      UpperCAmelCase_  (						unittest.TestCase							):




    @slow
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							int:




     '''simple docstring'''

     A__	=    FlaxAlbertModel.from_pretrained("albert-base-v2"				)
     A__	=    np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]				)
     A__	=    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
     A__	=    model(lowerCamelCase_     ,			attention_mask=lowerCamelCase_				)[0]
     A__	=    (1, 11, 768)
     self.assertEqual(output.shape     ,			lowerCamelCase_				)
     A__	=    np.array(
         [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]				)

     self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]     ,			lowerCamelCase_     ,			atol=1e-4				)				)











"""simple docstring"""



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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''lilt'''




    def __init__(   self						:	List[str]     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Any=3_072     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	List[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Optional[Any]=1e-12     ,			snake_case_						:	List[Any]=0     ,			snake_case_						:	Any="absolute"     ,			snake_case_						:	str=None     ,			snake_case_						:	int=4     ,			snake_case_						:	int=1_024     ,			**snake_case_						:	Tuple     ,			)				->							List[Any]:




     '''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__	=    position_embedding_type
     A__	=    classifier_dropout
     A__	=    channel_shrink_ratio
     A__	=    max_ad_position_embeddings





