


"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaConfig, 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():
   from transformers.models.roberta.modeling_flax_roberta import (
       FlaxRobertaForCausalLM,
       FlaxRobertaForMaskedLM,
       FlaxRobertaForMultipleChoice,
       FlaxRobertaForQuestionAnswering,
       FlaxRobertaForSequenceClassification,
       FlaxRobertaForTokenClassification,
       FlaxRobertaModel,
   )

class 			lowercase_	(			unittest.TestCase    ):







      def __init__(   self			:				List[Any]		,	_lowercase			:				int		,	_lowercase			:				int=1_3		,	_lowercase			:				str=7		,	_lowercase			:				List[str]=True		,	_lowercase			:				Optional[Any]=True		,	_lowercase			:				Optional[Any]=True		,	_lowercase			:				Any=True		,	_lowercase			:				Any=9_9		,	_lowercase			:				Union[str, Any]=3_2		,	_lowercase			:				List[str]=5		,	_lowercase			:				Any=4		,	_lowercase			:				str=3_7		,	_lowercase			:				List[str]="gelu"		,	_lowercase			:				str=0.1		,	_lowercase			:				int=0.1		,	_lowercase			:				Union[str, Any]=5_1_2		,	_lowercase			:				Any=1_6		,	_lowercase			:				Dict=2		,	_lowercase			:				Union[str, Any]=0.02		,	_lowercase			:				str=4		,	):
        lowerCAmelCase__     :     Any										=       parent
        lowerCAmelCase__     :     List[str]										=       batch_size
        lowerCAmelCase__     :     Any										=       seq_length
        lowerCAmelCase__     :     Optional[Any]										=       is_training
        lowerCAmelCase__     :     Union[str, Any]										=       use_attention_mask
        lowerCAmelCase__     :     Optional[Any]										=       use_token_type_ids
        lowerCAmelCase__     :     List[str]										=       use_labels
        lowerCAmelCase__     :     Union[str, Any]										=       vocab_size
        lowerCAmelCase__     :     List[str]										=       hidden_size
        lowerCAmelCase__     :     Any										=       num_hidden_layers
        lowerCAmelCase__     :     Tuple										=       num_attention_heads
        lowerCAmelCase__     :     List[str]										=       intermediate_size
        lowerCAmelCase__     :     Optional[Any]										=       hidden_act
        lowerCAmelCase__     :     Optional[int]										=       hidden_dropout_prob
        lowerCAmelCase__     :     Union[str, Any]										=       attention_probs_dropout_prob
        lowerCAmelCase__     :     List[Any]										=       max_position_embeddings
        lowerCAmelCase__     :     Dict										=       type_vocab_size
        lowerCAmelCase__     :     str										=       type_sequence_label_size
        lowerCAmelCase__     :     str										=       initializer_range
        lowerCAmelCase__     :     Optional[Any]										=       num_choices







      def 					_lowerCAmelCase							(   self			:				Tuple     ):
        lowerCAmelCase__     :     str										=       ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size     )

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

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

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







      def 					_lowerCAmelCase							(   self			:				str     ):
        lowerCAmelCase__     :     Optional[int]										=       self.prepare_config_and_inputs()
        lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__     :     str										=       config_and_inputs

        lowerCAmelCase__     :     List[str]										=       True
        lowerCAmelCase__     :     List[Any]										=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
        lowerCAmelCase__     :     Union[str, 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
class 			lowercase_	(			a_				,				unittest.TestCase    ):
      __magic_name__  :				Any 							=					True

      __magic_name__  :				Dict 							=					(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )







      def 					_lowerCAmelCase							(   self			:				Optional[int]     ):
        lowerCAmelCase__     :     Dict										=       FlaxRobertaModelTester(self     )







      @slow
      def 					_lowerCAmelCase							(   self			:				Optional[int]     ):







        for model_class_name in self.all_model_classes:
          lowerCAmelCase__     :     Dict										=       model_class_name.from_pretrained("roberta-base"		,	from_pt=_lowercase     )
          lowerCAmelCase__     :     Any										=       model(np.ones((1, 1)     )     )
          self.assertIsNotNone(_lowercase     )




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


UpperCAmelCase   :    Union[str, Any]     =		logging.get_logger(__name__)

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







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		"lilt"







		def __init__( self		,      A=3_05_22		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=2		,      A=0.02		,      A=1e-1_2		,      A=0		,      A="absolute"		,      A=None		,      A=4		,      A=10_24		,      **A		,      )  -> List[Any]:







					'''simple docstring'''







					super().__init__(pad_token_id=A		,      **A	)

					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  max_position_embeddings
					lowerCamelCase    		=  type_vocab_size
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  position_embedding_type
					lowerCamelCase    		=  classifier_dropout
					lowerCamelCase    		=  channel_shrink_ratio
					lowerCamelCase    		=  max_ad_position_embeddings

