



'''simple docstring'''






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    DebertaForMaskedLM,
						    DebertaForQuestionAnswering,
						    DebertaForSequenceClassification,
						    DebertaForTokenClassification,
						    DebertaModel,
						)
						from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def __init__(						self			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Tuple=13		,			__snake_case			:     Optional[Any]=7		,			__snake_case			:     Optional[int]=True		,			__snake_case			:     int=True		,			__snake_case			:     Optional[int]=True		,			__snake_case			:     str=True		,			__snake_case			:     str=99		,			__snake_case			:     Any=32		,			__snake_case			:     Union[str, Any]=5		,			__snake_case			:     Optional[int]=4		,			__snake_case			:     List[str]=37		,			__snake_case			:     Any="gelu"		,			__snake_case			:     Optional[int]=0.1		,			__snake_case			:     Dict=0.1		,			__snake_case			:     Dict=512		,			__snake_case			:     List[str]=16		,			__snake_case			:     Any=2		,			__snake_case			:     List[str]=0.02		,			__snake_case			:     str=False		,			__snake_case			:     str=True		,			__snake_case			:     List[str]="None"		,			__snake_case			:     List[Any]=3		,			__snake_case			:     Optional[Any]=4		,			__snake_case			:     Tuple=None		,			)			->		List[str]:
								UpperCAmelCase :       int 							=		parent
								UpperCAmelCase :       List[Any] 							=		batch_size
								UpperCAmelCase :       Tuple 							=		seq_length
								UpperCAmelCase :       int 							=		is_training
								UpperCAmelCase :       Union[str, Any] 							=		use_input_mask
								UpperCAmelCase :       List[str] 							=		use_token_type_ids
								UpperCAmelCase :       List[str] 							=		use_labels
								UpperCAmelCase :       Union[str, Any] 							=		vocab_size
								UpperCAmelCase :       int 							=		hidden_size
								UpperCAmelCase :       int 							=		num_hidden_layers
								UpperCAmelCase :       str 							=		num_attention_heads
								UpperCAmelCase :       Union[str, Any] 							=		intermediate_size
								UpperCAmelCase :       Optional[Any] 							=		hidden_act
								UpperCAmelCase :       Dict 							=		hidden_dropout_prob
								UpperCAmelCase :       int 							=		attention_probs_dropout_prob
								UpperCAmelCase :       Union[str, Any] 							=		max_position_embeddings
								UpperCAmelCase :       Union[str, Any] 							=		type_vocab_size
								UpperCAmelCase :       int 							=		type_sequence_label_size
								UpperCAmelCase :       List[Any] 							=		initializer_range
								UpperCAmelCase :       Optional[Any] 							=		num_labels
								UpperCAmelCase :       int 							=		num_choices
								UpperCAmelCase :       List[Any] 							=		relative_attention
								UpperCAmelCase :       Any 							=		position_biased_input
								UpperCAmelCase :       Any 							=		pos_att_type
								UpperCAmelCase :       Optional[int] 							=		scope







	def      A	(						self			:     Optional[int]		)			->		List[str]:
								UpperCAmelCase :       str 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

								UpperCAmelCase :       Any 							=		None
								if self.use_input_mask:
															UpperCAmelCase :       Union[str, Any] 							=		ids_tensor([self.batch_size, self.seq_length]		,			vocab_size=2		)

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

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

								UpperCAmelCase :       Optional[int] 							=		self.get_config()

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







	def      A	(						self			:     List[Any]		)			->		Union[str, Any]:
								return DebertaConfig(
								    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		,			initializer_range=self.initializer_range		,			relative_attention=self.relative_attention		,			position_biased_input=self.position_biased_input		,			pos_att_type=self.pos_att_type		,			)







	def      A	(						self			:     int		)			->		List[str]:
								UpperCAmelCase :       Dict 							=		self.get_config()
								UpperCAmelCase :       int 							=		300
								return config







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		)			->		List[str]:
								self.parent.assertListEqual(list(result.loss.size()		)		,			[]		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     Dict		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     int		,			__snake_case			:     List[str]		,			__snake_case			:     Dict		,			__snake_case			:     str		)			->		List[Any]:
								UpperCAmelCase :       Optional[int] 							=		DebertaModel(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[Any] 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		)[0]
								UpperCAmelCase :       Union[str, Any] 							=		model(__snake_case		,			token_type_ids=__snake_case		)[0]
								UpperCAmelCase :       List[Any] 							=		model(__snake_case		)[0]

								self.parent.assertListEqual(list(sequence_output.size()		)		,			[self.batch_size, self.seq_length, self.hidden_size]		)







	def      A	(						self			:     Dict		,			__snake_case			:     Dict		,			__snake_case			:     str		,			__snake_case			:     List[Any]		,			__snake_case			:     str		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Tuple		,			__snake_case			:     List[Any]		)			->		str:
								UpperCAmelCase :       Tuple 							=		DebertaForMaskedLM(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Dict 							=		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      A	(						self			:     Tuple		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     int		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[int]		)			->		List[Any]:
								UpperCAmelCase :       Any 							=		self.num_labels
								UpperCAmelCase :       Optional[int] 							=		DebertaForSequenceClassification(__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		)
								self.parent.assertListEqual(list(result.logits.size()		)		,			[self.batch_size, self.num_labels]		)
								self.check_loss_output(__snake_case		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Tuple		,			__snake_case			:     List[str]		,			__snake_case			:     int		,			__snake_case			:     int		,			__snake_case			:     Optional[int]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Any		)			->		Tuple:
								UpperCAmelCase :       int 							=		self.num_labels
								UpperCAmelCase :       Tuple 							=		DebertaForTokenClassification(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Dict 							=		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      A	(						self			:     Union[str, Any]		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     List[str]		,			__snake_case			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Optional[int]		)			->		List[Any]:
								UpperCAmelCase :       int 							=		DebertaForQuestionAnswering(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[str] 							=		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      A	(						self			:     Any		)			->		Union[str, Any]:
								UpperCAmelCase :       str 							=		self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		
								) :       Union[str, 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 				SCREAMING_SNAKE_CASE(							A__							,		A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       (
	    (
	        DebertaModel,
	        DebertaForMaskedLM,
	        DebertaForSequenceClassification,
	        DebertaForTokenClassification,
	        DebertaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase__			 =       (
	    {
	        """feature-extraction""": DebertaModel,
	        """fill-mask""": DebertaForMaskedLM,
	        """question-answering""": DebertaForQuestionAnswering,
	        """text-classification""": DebertaForSequenceClassification,
	        """token-classification""": DebertaForTokenClassification,
	        """zero-shot""": DebertaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	lowerCamelCase__			 =       True
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False







	def      A	(						self			:     Optional[int]		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		DebertaModelTester(self		)
								UpperCAmelCase :       Optional[Any] 							=		ConfigTester(self		,			config_class=__snake_case		,			hidden_size=37		)







	def      A	(						self			:     Dict		)			->		Any:
								self.config_tester.run_common_tests()







	def      A	(						self			:     Optional[int]		)			->		Tuple:
								UpperCAmelCase :       Optional[Any] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_model(*__snake_case		)







	def      A	(						self			:     Union[str, Any]		)			->		Dict:
								UpperCAmelCase :       Union[str, Any] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_sequence_classification(*__snake_case		)







	def      A	(						self			:     Tuple		)			->		int:
								UpperCAmelCase :       Any 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_masked_lm(*__snake_case		)







	def      A	(						self			:     List[Any]		)			->		Dict:
								UpperCAmelCase :       Tuple 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_question_answering(*__snake_case		)







	def      A	(						self			:     List[str]		)			->		List[str]:
								UpperCAmelCase :       List[str] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_token_classification(*__snake_case		)







	@slow
	def      A	(						self			:     str		)			->		Optional[Any]:
								for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase :       int 							=		DebertaModel.from_pretrained(__snake_case		)
															self.assertIsNotNone(__snake_case		)

@require_torch
@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@unittest.skip(reason='''Model not available yet'''		)
	def      A	(						self			:     Tuple		)			->		str:
								pass







	@slow
	def      A	(						self			:     Any		)			->		str:
								UpperCAmelCase :       Tuple 							=		DebertaModel.from_pretrained('''microsoft/deberta-base'''		)

								UpperCAmelCase :       Optional[Any] 							=		torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
								UpperCAmelCase :       Any 							=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
								with torch.no_grad():
															UpperCAmelCase :       Any 							=		model(__snake_case		,			attention_mask=__snake_case		)[0]
								# compare the actual values for a slice.
								UpperCAmelCase :       Optional[int] 							=		torch.tensor(
								    [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]		)
								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,			__snake_case		,			atol=1E-4		)		,			F"""{output[:, 1:4, 1:4]}"""		)









"""simple docstring"""







class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__ = ""		,     snake_case__ = False						):






         """simple docstring"""





         lowerCAmelCase     :     dict[str, RadixNode]								=							{}

         # A node will be a leaf if the tree contains its word
         lowerCAmelCase     :     str								=							is_leaf

         lowerCAmelCase     :     str								=							prefix

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							0
         for q, w in zip(self.prefix		,     snake_case__						):
           if q != w:
             break

           x += 1

         return self.prefix[:x], self.prefix[x:], word[x:]

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         for word in words:
           self.insert(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if self.prefix == word:
           lowerCAmelCase     :     Union[str, Any]								=							True

         # Case 2: The node has no edges that have a prefix to the word
         # Solution: We create an edge from the current node to a new one
         # containing the word
         elif word[0] not in self.nodes:
           lowerCAmelCase     :     Optional[Any]								=							RadixNode(prefix=snake_case__		,     is_leaf=snake_case__						)

         else:
           lowerCAmelCase     :     Tuple								=							self.nodes[word[0]]
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Optional[Any]								=							incoming_node.match(
               snake_case__						)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
             self.nodes[matching_string[0]].insert(snake_case__						)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
             lowerCAmelCase     :     Optional[Any]								=							remaining_prefix

             lowerCAmelCase     :     int								=							self.nodes[matching_string[0]]
             lowerCAmelCase     :     List[Any]								=							RadixNode(snake_case__		,     snake_case__						)
             lowerCAmelCase     :     Optional[int]								=							aux_node

             if remaining_word == "":
               lowerCAmelCase     :     Optional[int]								=							True
             else:
               self.nodes[matching_string[0]].insert(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							self.nodes.get(word[0]		,     snake_case__						)
         if not incoming_node:
           return False
         else:
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							incoming_node.match(
               snake_case__						)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
             return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
             return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
             return incoming_node.find(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.nodes.get(word[0]		,     snake_case__						)
         if not incoming_node:
           return False
         else:
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							incoming_node.match(
               snake_case__						)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
             return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
             return incoming_node.delete(snake_case__						)
           else:
             # If it is not a leaf, we don't have to delete
             if not incoming_node.is_leaf:
               return False
             else:
               # We delete the nodes if no edges go from it
               if len(incoming_node.nodes						) == 0:
                 del self.nodes[word[0]]
                 # We merge the current node with its only child
                 if len(self.nodes						) == 1 and not self.is_leaf:
                   lowerCAmelCase     :     List[str]								=							list(self.nodes.values()						)[0]
                   lowerCAmelCase     :     List[str]								=							merging_node.is_leaf
                   self.prefix += merging_node.prefix
                   lowerCAmelCase     :     Optional[int]								=							merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
               elif len(incoming_node.nodes						) > 1:
                 lowerCAmelCase     :     Optional[int]								=							False
               # If there is 1 edge, we merge it with its child
               else:
                 lowerCAmelCase     :     Optional[Any]								=							list(incoming_node.nodes.values()						)[0]
                 lowerCAmelCase     :     int								=							merging_node.is_leaf
                 incoming_node.prefix += merging_node.prefix
                 lowerCAmelCase     :     Tuple								=							merging_node.nodes

               return True







       def        lowercase__							(						self		,     snake_case__ = 0						):






         """simple docstring"""





         if self.prefix != "":
           print("-" * height		,     self.prefix		,     "  (leaf)" if self.is_leaf else ""						)

         for value in self.nodes.values():
           value.print_tree(height + 1						)

def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							"banana bananas bandana band apple all beast".split()
  lowerCAmelCase     :     List[str]								=							RadixNode()
  root.insert_many(SCREAMING_SNAKE_CASE				)

  assert all(root.find(SCREAMING_SNAKE_CASE				) for word in words				)
  assert not root.find("bandanas"				)
  assert not root.find("apps"				)
  root.delete("all"				)
  assert not root.find("all"				)
  root.delete("banana"				)
  assert not root.find("banana"				)
  assert root.find("bananas"				)

  return True
def 					a__			(				):


  '''simple docstring'''







  assert test_trie()
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							RadixNode()
  lowerCAmelCase     :     Optional[Any]								=							"banana bananas bandanas bandana band apple all beast".split()
  root.insert_many(SCREAMING_SNAKE_CASE				)

  print("Words:"				,      SCREAMING_SNAKE_CASE				)
  print("Tree:"				)
  root.print_tree()


if __name__ == "__main__":
     main()





