




"""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      UpperCAmelCase_  :




    def __init__(   self						:	Tuple     ,			snake_case_						:	Dict     ,			snake_case_						:	Dict=2     ,			snake_case_						:	Union[str, Any]=8     ,			snake_case_						:	Union[str, Any]=True     ,			snake_case_						:	Any=True     ,			snake_case_						:	Union[str, Any]=True     ,			snake_case_						:	List[str]=True     ,			snake_case_						:	Union[str, Any]=99     ,			snake_case_						:	Dict=16     ,			snake_case_						:	Union[str, Any]=5     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[Any]=36     ,			snake_case_						:	Any="gelu"     ,			snake_case_						:	Any=0.0     ,			snake_case_						:	Any=0.0     ,			snake_case_						:	int=512     ,			snake_case_						:	Optional[int]=16     ,			snake_case_						:	str=2     ,			snake_case_						:	Dict=0.02     ,			snake_case_						:	int=3     ,			snake_case_						:	Optional[int]=4     ,			snake_case_						:	Union[str, Any]=None     ,			)				->							Any:




     '''simple docstring'''

     A__	=    parent
     A__	=    batch_size
     A__	=    seq_length
     A__	=    is_training
     A__	=    use_input_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_labels
     A__	=    num_choices
     A__	=    scope




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




     '''simple docstring'''

     A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size				)

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

     A__	=    self.get_config()

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




    def 							__magic_name__							(   self						:	int				)				->							Any:




     '''simple docstring'''

     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=snake_case_     ,			initializer_range=self.initializer_range     ,			)




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




     '''simple docstring'''

     A__	=    self.get_config()
     A__	=    300
     return config




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     (
         (
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),								
     )	=    self.prepare_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,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	List[str]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]				)				->							List[str]:




     '''simple docstring'''

     A__	=    MraModel(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    model(snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    model(snake_case_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Tuple     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Any     ,			snake_case_						:	str     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Any     ,			)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    True
     A__	=    MraModel(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			encoder_hidden_states=snake_case_     ,			encoder_attention_mask=snake_case_     ,			)
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			encoder_hidden_states=snake_case_     ,			)
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Dict     ,			snake_case_						:	Any     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	str				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    MraForMaskedLM(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	List[str]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    MraForQuestionAnswering(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			start_positions=snake_case_     ,			end_positions=snake_case_     ,			)
     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 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	Dict     ,			snake_case_						:	List[str]				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    MraForSequenceClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[str]     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	str     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Dict				)				->							str:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    MraForTokenClassification(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)				)




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	int     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]				)				->							Any:




     '''simple docstring'''

     A__	=    self.num_choices
     A__	=    MraForMultipleChoice(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    input_ids.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    token_type_ids.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    input_mask.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)				)




    def 							__magic_name__							(   self						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     (
         (
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),								
     )	=    config_and_inputs
     A__	=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase_  (						A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False

    lowercase__						 =				()




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




     '''simple docstring'''

     A__	=    MraModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			hidden_size=37				)




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




     '''simple docstring'''

     self.config_tester.run_common_tests()




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




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      A__	=    type
      self.model_tester.create_and_check_model(*snake_case_				)




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




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case_				)




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




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case_				)




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




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case_				)




    def 							__magic_name__							(   self						:	str				)				->							int:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case_				)




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




     '''simple docstring'''

     for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    MraModel.from_pretrained(snake_case_				)
      self.assertIsNotNone(snake_case_				)




    @unittest.skip(reason="MRA does not output attentions"				)
    def 							__magic_name__							(   self						:	int				)				->							Any:




     '''simple docstring'''

     return






@require_torch
class      UpperCAmelCase_  (						unittest.TestCase							):




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




     '''simple docstring'''

     A__	=    MraModel.from_pretrained("uw-madison/mra-base-512-4"				)
     A__	=    torch.arange(256				).unsqueeze(0				)

     with torch.no_grad():
      A__	=    model(snake_case_				)[0]

     A__	=    torch.Size((1, 256, 768)				)
     self.assertEqual(output.shape     ,			snake_case_				)

     A__	=    torch.tensor(
         [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]				)

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




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




     '''simple docstring'''

     A__	=    MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"				)
     A__	=    torch.arange(256				).unsqueeze(0				)

     with torch.no_grad():
      A__	=    model(snake_case_				)[0]

     A__	=    50_265

     A__	=    torch.Size((1, 256, vocab_size)				)
     self.assertEqual(output.shape     ,			snake_case_				)

     A__	=    torch.tensor(
         [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]				)

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




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




     '''simple docstring'''

     A__	=    MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"				)
     A__	=    torch.arange(4_096				).unsqueeze(0				)

     with torch.no_grad():
      A__	=    model(snake_case_				)[0]

     A__	=    50_265

     A__	=    torch.Size((1, 4_096, vocab_size)				)
     self.assertEqual(output.shape     ,			snake_case_				)

     A__	=    torch.tensor(
         [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]				)

     self.assertTrue(torch.allclose(output[:, :3, :3]     ,			snake_case_     ,			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





