



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 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=8,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=99,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=5,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=36,     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=512,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_=None,     )	->						Optional[Any]:
          UpperCamelCase       :    Tuple		      =      parent
          UpperCamelCase       :    Dict		      =      batch_size
          UpperCamelCase       :    str		      =      seq_length
          UpperCamelCase       :    List[Any]		      =      is_training
          UpperCamelCase       :    Tuple		      =      use_input_mask
          UpperCamelCase       :    Union[str, Any]		      =      use_token_type_ids
          UpperCamelCase       :    Optional[Any]		      =      use_labels
          UpperCamelCase       :    Optional[Any]		      =      vocab_size
          UpperCamelCase       :    Dict		      =      hidden_size
          UpperCamelCase       :    Union[str, Any]		      =      num_hidden_layers
          UpperCamelCase       :    Optional[int]		      =      num_attention_heads
          UpperCamelCase       :    Tuple		      =      intermediate_size
          UpperCamelCase       :    List[Any]		      =      hidden_act
          UpperCamelCase       :    Dict		      =      hidden_dropout_prob
          UpperCamelCase       :    int		      =      attention_probs_dropout_prob
          UpperCamelCase       :    Dict		      =      max_position_embeddings
          UpperCamelCase       :    List[Any]		      =      type_vocab_size
          UpperCamelCase       :    Tuple		      =      type_sequence_label_size
          UpperCamelCase       :    Tuple		      =      initializer_range
          UpperCamelCase       :    int		      =      num_labels
          UpperCamelCase       :    Any		      =      num_choices
          UpperCamelCase       :    str		      =      scope
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    Any		      =      ids_tensor([self.batch_size, self.seq_length],     self.vocab_size			)

          UpperCamelCase       :    Tuple		      =      None
          if self.use_input_mask:
             UpperCamelCase       :    List[str]		      =      random_attention_mask([self.batch_size, self.seq_length]			)

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

          UpperCamelCase       :    Optional[int]		      =      None
          UpperCamelCase       :    Any		      =      None
          UpperCamelCase       :    Optional[int]		      =      None
          if self.use_labels:
             UpperCamelCase       :    Dict		      =      ids_tensor([self.batch_size],     self.type_sequence_label_size			)
             UpperCamelCase       :    str		      =      ids_tensor([self.batch_size, self.seq_length],     self.num_labels			)
             UpperCamelCase       :    List[Any]		      =      ids_tensor([self.batch_size],     self.num_choices			)

          UpperCamelCase       :    int		      =      self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def    snake_case_			(     self			)	->						str:
          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    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    Tuple		      =      self.get_config()
          UpperCamelCase       :    Optional[int]		      =      300
          return config
       def    snake_case_			(     self			)	->						str:
          (
              UpperCamelCase						
          )       :    Union[str, Any]		      =      self.prepare_config_and_inputs()

          UpperCamelCase       :    List[str]		      =      True
          UpperCamelCase       :    Dict		      =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
          UpperCamelCase       :    Optional[Any]		      =      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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    Dict		      =      MraModel(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Optional[Any]		      =      model(__lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase			)
          UpperCamelCase       :    List[str]		      =      model(__lowerCamelCase,     token_type_ids=__lowerCamelCase			)
          UpperCamelCase       :    Optional[int]		      =      model(__lowerCamelCase			)
          self.parent.assertEqual(result.last_hidden_state.shape,     (self.batch_size, self.seq_length, self.hidden_size)			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     )	->						List[Any]:
          UpperCamelCase       :    Dict		      =      True
          UpperCamelCase       :    int		      =      MraModel(__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Any		      =      model(
              __lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase,     encoder_hidden_states=__lowerCamelCase,     encoder_attention_mask=__lowerCamelCase,     )
          UpperCamelCase       :    Tuple		      =      model(
              __lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase,     encoder_hidden_states=__lowerCamelCase,     )
          UpperCamelCase       :    Tuple		      =      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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    Tuple		      =      MraForMaskedLM(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Dict		      =      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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    Union[str, Any]		      =      MraForQuestionAnswering(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Union[str, Any]		      =      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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          UpperCamelCase       :    Optional[Any]		      =      self.num_labels
          UpperCamelCase       :    Optional[Any]		      =      MraForSequenceClassification(__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Union[str, Any]		      =      model(__lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase,     labels=__lowerCamelCase			)
          self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels)			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          UpperCamelCase       :    List[str]		      =      self.num_labels
          UpperCamelCase       :    List[Any]		      =      MraForTokenClassification(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    List[Any]		      =      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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						int:
          UpperCamelCase       :    str		      =      self.num_choices
          UpperCamelCase       :    Union[str, Any]		      =      MraForMultipleChoice(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Optional[Any]		      =      input_ids.unsqueeze(1			).expand(-1,     self.num_choices,     -1			).contiguous()
          UpperCamelCase       :    Any		      =      token_type_ids.unsqueeze(1			).expand(-1,     self.num_choices,     -1			).contiguous()
          UpperCamelCase       :    Any		      =      input_mask.unsqueeze(1			).expand(-1,     self.num_choices,     -1			).contiguous()
          UpperCamelCase       :    Union[str, Any]		      =      model(
              __lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase,     labels=__lowerCamelCase,     )
          self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_choices)			)






       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    Any		      =      self.prepare_config_and_inputs()
          (
              UpperCamelCase						
          )       :    List[Any]		      =      config_and_inputs
          UpperCamelCase       :    Union[str, Any]		      =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict






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

       UpperCAmelCase__							:   Dict  	=    ()
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    List[Any]		      =      MraModelTester(self			)
          UpperCamelCase       :    List[str]		      =      ConfigTester(self,     config_class=__lowerCamelCase,     hidden_size=37			)
       def    snake_case_			(     self			)	->						Optional[int]:
          self.config_tester.run_common_tests()
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Union[str, Any]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    str		      =      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCamelCase       :    List[str]		      =      type
             self.model_tester.create_and_check_model(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    List[Any]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    int		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    int		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    Union[str, Any]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase			)
       @slow
       def    snake_case_			(     self			)	->						List[Any]:
          for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase       :    Any		      =      MraModel.from_pretrained(__lowerCamelCase			)
             self.assertIsNotNone(__lowerCamelCase			)






       @unittest.skip(reason='MRA does not output attentions'			)
       def    snake_case_			(     self			)	->						int:
          return






@require_torch
class 							lowerCAmelCase_						(    unittest.TestCase				):
       @slow
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    Optional[Any]		      =      MraModel.from_pretrained('uw-madison/mra-base-512-4'			)
          UpperCamelCase       :    List[str]		      =      torch.arange(256			).unsqueeze(0			)

          with torch.no_grad():
             UpperCamelCase       :    Optional[Any]		      =      model(__lowerCamelCase			)[0]

          UpperCamelCase       :    Any		      =      torch.Size((1, 256, 768)			)
          self.assertEqual(output.shape,     __lowerCamelCase			)

          UpperCamelCase       :    Optional[Any]		      =      torch.tensor(
              [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]			)

          self.assertTrue(torch.allclose(output[:, :3, :3],     __lowerCamelCase,     atol=1e-4			)			)
       @slow
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    Tuple		      =      MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'			)
          UpperCamelCase       :    Any		      =      torch.arange(256			).unsqueeze(0			)

          with torch.no_grad():
             UpperCamelCase       :    Optional[int]		      =      model(__lowerCamelCase			)[0]

          UpperCamelCase       :    int		      =      5_0265

          UpperCamelCase       :    Union[str, Any]		      =      torch.Size((1, 256, vocab_size)			)
          self.assertEqual(output.shape,     __lowerCamelCase			)

          UpperCamelCase       :    Optional[Any]		      =      torch.tensor(
              [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]			)

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






       @slow
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    Union[str, Any]		      =      MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'			)
          UpperCamelCase       :    Any		      =      torch.arange(4096			).unsqueeze(0			)

          with torch.no_grad():
             UpperCamelCase       :    Optional[Any]		      =      model(__lowerCamelCase			)[0]

          UpperCamelCase       :    List[str]		      =      5_0265

          UpperCamelCase       :    Union[str, Any]		      =      torch.Size((1, 4096, vocab_size)			)
          self.assertEqual(output.shape,     __lowerCamelCase			)

          UpperCamelCase       :    Optional[int]		      =      torch.tensor(
              [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]			)

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







'''simple docstring'''





class 				UpperCAmelCase			:


 def __init__(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:dict[str, TrieNode]      =      {}  # Mapping from char to TrieNode
    UpperCAmelCase__	:Union[str, Any]      =      False


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	list[str] ):
    for word in words:
       self.insert(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Tuple      =      self
    for char in word:
       if char not in curr.nodes:
          UpperCAmelCase__	:Optional[int]      =      TrieNode()
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    UpperCAmelCase__	:Optional[int]      =      True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:str      =      self
    for char in word:
       if char not in curr.nodes:
          return False
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    return curr.is_leaf





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):

    def _delete(__lowerCamelCase							:	TrieNode			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int ) -> bool:
       if index == len(__lowerCamelCase ):
          # If word does not exist
          if not curr.is_leaf:
             return False
          UpperCAmelCase__	:int      =      False
          return len(curr.nodes ) == 0
       UpperCAmelCase__	:str      =      word[index]
       UpperCAmelCase__	:Optional[Any]      =      curr.nodes.get(__lowerCamelCase )
       # If char not in current trie node
       if not char_node:
          return False
       # Flag to check if node can be deleted
       UpperCAmelCase__	:Any      =      _delete(__lowerCamelCase			,       __lowerCamelCase			,       index + 1 )
       if delete_curr:
          del curr.nodes[char]
          return len(curr.nodes ) == 0
       return delete_curr

    _delete(self			,       __lowerCamelCase			,       0 )


def        a__     (					UpperCamelCase_						:      TrieNode,   UpperCamelCase_						:      str						):
   if node.is_leaf:
      print(UpperCamelCase_,   end=''' '''						)

   for key, value in node.nodes.items():
      print_words(UpperCamelCase_,   word + key						)


def        a__     (					):
   UpperCAmelCase__	:Union[str, Any]      =      '''banana bananas bandana band apple all beast'''.split()
   UpperCAmelCase__	:Union[str, Any]      =      TrieNode()
   root.insert_many(UpperCamelCase_						)
   # print_words(root, "")
   assert all(root.find(UpperCamelCase_						) for word in words						)
   assert root.find('''banana'''						)
   assert not root.find('''bandanas'''						)
   assert not root.find('''apps'''						)
   assert root.find('''apple'''						)
   assert root.find('''all'''						)
   root.delete('''all'''						)
   assert not root.find('''all'''						)
   root.delete('''banana'''						)
   assert not root.find('''banana'''						)
   assert root.find('''bananas'''						)
   return True


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      bool						):
   print(str(UpperCamelCase_						),   '''works!''' if passes else '''doesn\'t work :('''						)


def        a__     (					):
   assert test_trie()


def        a__     (					):
   print_results('''Testing trie functionality''',   test_trie()						)


if __name__ == "__main__":
    main()



