



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   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		int      							=     "lilt"



					def __init__(  self       :							int					,       _snake_case       :							Dict=3_05_22					,       _snake_case       :							List[str]=7_68					,       _snake_case       :							Optional[int]=12					,       _snake_case       :							Tuple=12					,       _snake_case       :							Union[str, Any]=30_72					,       _snake_case       :							str="gelu"					,       _snake_case       :							List[Any]=0.1					,       _snake_case       :							Union[str, Any]=0.1					,       _snake_case       :							Dict=5_12					,       _snake_case       :							List[str]=2					,       _snake_case       :							Any=0.02					,       _snake_case       :							str=1E-12					,       _snake_case       :							Union[str, Any]=0					,       _snake_case       :							int="absolute"					,       _snake_case       :							str=None					,       _snake_case       :							Union[str, Any]=4					,       _snake_case       :							List[Any]=10_24					,       **_snake_case       :							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





"""simple docstring"""


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class 							a					:


    def __init__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str=2 ,					__lowerCAmelCase						:	List[Any]=8 ,					__lowerCAmelCase						:	Dict=True ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	str=True ,					__lowerCAmelCase						:	Optional[Any]=99 ,					__lowerCAmelCase						:	Optional[Any]=16 ,					__lowerCAmelCase						:	Optional[int]=5 ,					__lowerCAmelCase						:	Optional[int]=2 ,					__lowerCAmelCase						:	Optional[int]=36 ,					__lowerCAmelCase						:	Any="gelu" ,					__lowerCAmelCase						:	Any=0.0 ,					__lowerCAmelCase						:	int=0.0 ,					__lowerCAmelCase						:	List[str]=512 ,					__lowerCAmelCase						:	List[str]=16 ,					__lowerCAmelCase						:	int=2 ,					__lowerCAmelCase						:	Any=0.02 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	str=4 ,					__lowerCAmelCase						:	Dict=None ,					):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   seq_length
      _UpperCAmelCase    			=   is_training
      _UpperCAmelCase    			=   use_input_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_labels
      _UpperCAmelCase    			=   num_choices
      _UpperCAmelCase    			=   scope


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size       )

      _UpperCAmelCase    			=   None
      if self.use_input_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    			=   None
      _UpperCAmelCase    			=   None
      _UpperCAmelCase    			=   None
      if self.use_labels:
        _UpperCAmelCase    			=   ids_tensor([self.batch_size] ,					self.type_sequence_label_size       )
        _UpperCAmelCase    			=   ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels       )
        _UpperCAmelCase    			=   ids_tensor([self.batch_size] ,					self.num_choices       )

      _UpperCAmelCase    			=   self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


    def   lowerCAmelCase_	(       self						:	Any       ):
      return MraConfig(
          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 ,					)


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   self.get_config()
      _UpperCAmelCase    			=   300
      return config


    def   lowerCAmelCase_	(       self						:	Any       ):
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   self.prepare_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,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Tuple       ):
      _UpperCAmelCase    			=   MraModel(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					):
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   MraModel(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					encoder_attention_mask=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)       )


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   MraForQuestionAnswering(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					start_positions=__lowerCAmelCase ,					end_positions=__lowerCAmelCase ,					)
      self.parent.assertEqual(result.start_logits.shape ,					(self.batch_size, self.seq_length)       )
      self.parent.assertEqual(result.end_logits.shape ,					(self.batch_size, self.seq_length)       )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForSequenceClassification(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForTokenClassification(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)       )


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.num_choices
      _UpperCAmelCase    			=   MraForMultipleChoice(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   input_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   token_type_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   input_mask.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase ,					)
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   config_and_inputs
      _UpperCAmelCase    			=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	str  				=				(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _snake_case  :	Dict  				=				False
    _snake_case  :	Any  				=				False
    _snake_case  :	List[str]  				=				False
    _snake_case  :	Tuple  				=				False

    _snake_case  :	int  				=				()


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   MraModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(self ,					config_class=__lowerCAmelCase ,					hidden_size=37       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      self.config_tester.run_common_tests()


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        _UpperCAmelCase    			=   type
        self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase       )


    @slow
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase    			=   MraModel.from_pretrained(__lowerCAmelCase       )
        self.assertIsNotNone(__lowerCAmelCase       )


    @unittest.skip(reason="""MRA does not output attentions"""       )
    def   lowerCAmelCase_	(       self						:	str       ):
      return



@require_torch
class 							a					(		unittest.TestCase ):


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraModel.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   torch.Size((1, 256, 768)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )


    @slow
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 256, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""       )
      _UpperCAmelCase    			=   torch.arange(4096       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 4096, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )
