


'''simple docstring'''







import unittest

from transformers import SqueezeBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SqueezeBertForMaskedLM,
		    SqueezeBertForMultipleChoice,
		    SqueezeBertForQuestionAnswering,
		    SqueezeBertForSequenceClassification,
		    SqueezeBertForTokenClassification,
		    SqueezeBertModel,
		)






class 		_snake_case     (					snake_case_		):






						'''simple docstring'''







						def __init__(	self:						Union[str, Any]   ,				__UpperCamelCase:						Any   ,				__UpperCamelCase:						Any=13   ,				__UpperCamelCase:						Optional[int]=7   ,				__UpperCamelCase:						Optional[Any]=True   ,				__UpperCamelCase:						str=True   ,				__UpperCamelCase:						int=False   ,				__UpperCamelCase:						int=True   ,				__UpperCamelCase:						Any=99   ,				__UpperCamelCase:						List[Any]=32   ,				__UpperCamelCase:						Dict=5   ,				__UpperCamelCase:						Union[str, Any]=4   ,				__UpperCamelCase:						Optional[int]=64   ,				__UpperCamelCase:						Any="gelu"   ,				__UpperCamelCase:						Tuple=0.1   ,				__UpperCamelCase:						str=0.1   ,				__UpperCamelCase:						Optional[int]=512   ,				__UpperCamelCase:						Dict=16   ,				__UpperCamelCase:						Optional[int]=2   ,				__UpperCamelCase:						List[Any]=0.0_2   ,				__UpperCamelCase:						int=3   ,				__UpperCamelCase:						Optional[int]=4   ,				__UpperCamelCase:						str=None   ,				__UpperCamelCase:						Any=2   ,				__UpperCamelCase:						Union[str, Any]=2   ,				__UpperCamelCase:						List[str]=2   ,				__UpperCamelCase:						Tuple=2   ,				__UpperCamelCase:						Dict=4   ,				__UpperCamelCase:						Optional[Any]=1   ,				)				->						str:
										__magic_name__					:       Any    							=							parent
										__magic_name__					:       Dict    							=							batch_size
										__magic_name__					:       Optional[Any]    							=							seq_length
										__magic_name__					:       Dict    							=							is_training
										__magic_name__					:       Optional[Any]    							=							use_input_mask
										__magic_name__					:       Dict    							=							use_token_type_ids
										__magic_name__					:       Any    							=							use_labels
										__magic_name__					:       List[str]    							=							vocab_size
										__magic_name__					:       str    							=							hidden_size
										__magic_name__					:       Union[str, Any]    							=							num_hidden_layers
										__magic_name__					:       int    							=							num_attention_heads
										__magic_name__					:       Tuple    							=							intermediate_size
										__magic_name__					:       List[Any]    							=							hidden_act
										__magic_name__					:       Any    							=							hidden_dropout_prob
										__magic_name__					:       int    							=							attention_probs_dropout_prob
										__magic_name__					:       Any    							=							max_position_embeddings
										__magic_name__					:       Optional[Any]    							=							type_vocab_size
										__magic_name__					:       Dict    							=							type_sequence_label_size
										__magic_name__					:       Optional[int]    							=							initializer_range
										__magic_name__					:       Union[str, Any]    							=							num_labels
										__magic_name__					:       Optional[Any]    							=							num_choices
										__magic_name__					:       List[Any]    							=							scope
										__magic_name__					:       Optional[Any]    							=							q_groups
										__magic_name__					:       Optional[Any]    							=							k_groups
										__magic_name__					:       Tuple    							=							v_groups
										__magic_name__					:       List[Any]    							=							post_attention_groups
										__magic_name__					:       Tuple    							=							intermediate_groups
										__magic_name__					:       Union[str, Any]    							=							output_groups







						def lowerCAmelCase__			(	self:						int  )				->						str:
										__magic_name__					:       Union[str, Any]    							=							ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size  )

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

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

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

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







						def lowerCAmelCase__			(	self:						Any  )				->						Optional[Any]:
										return SqueezeBertConfig(
										    embedding_size=self.hidden_size   ,				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   ,				attention_probs_dropout_prob=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				initializer_range=self.initializer_range   ,				q_groups=self.q_groups   ,				k_groups=self.k_groups   ,				v_groups=self.v_groups   ,				post_attention_groups=self.post_attention_groups   ,				intermediate_groups=self.intermediate_groups   ,				output_groups=self.output_groups   ,				)







						def lowerCAmelCase__			(	self:						Tuple   ,				__UpperCamelCase:						Tuple   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						Any   ,				__UpperCamelCase:						List[Any]   ,				__UpperCamelCase:						List[Any]   ,				__UpperCamelCase:						Dict  )				->						Optional[int]:
										__magic_name__					:       Optional[int]    							=							SqueezeBertModel(config=__snake_case  )
										model.to(__snake_case  )
										model.eval()
										__magic_name__					:       str    							=							model(__snake_case   ,				__snake_case  )
										__magic_name__					:       str    							=							model(__snake_case  )
										self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)  )







						def lowerCAmelCase__			(	self:						Union[str, Any]   ,				__UpperCamelCase:						Optional[Any]   ,				__UpperCamelCase:						Dict   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						int   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						List[Any]  )				->						List[Any]:
										__magic_name__					:       Optional[Any]    							=							SqueezeBertForMaskedLM(config=__snake_case  )
										model.to(__snake_case  )
										model.eval()
										__magic_name__					:       str    							=							model(__snake_case   ,				attention_mask=__snake_case   ,				labels=__snake_case  )
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)  )







						def lowerCAmelCase__			(	self:						Dict   ,				__UpperCamelCase:						Tuple   ,				__UpperCamelCase:						Any   ,				__UpperCamelCase:						Dict   ,				__UpperCamelCase:						Optional[Any]   ,				__UpperCamelCase:						Optional[int]   ,				__UpperCamelCase:						int  )				->						Optional[int]:
										__magic_name__					:       Optional[int]    							=							SqueezeBertForQuestionAnswering(config=__snake_case  )
										model.to(__snake_case  )
										model.eval()
										__magic_name__					:       Any    							=							model(
										    __snake_case   ,				attention_mask=__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 lowerCAmelCase__			(	self:						Tuple   ,				__UpperCamelCase:						int   ,				__UpperCamelCase:						Tuple   ,				__UpperCamelCase:						Optional[int]   ,				__UpperCamelCase:						Dict   ,				__UpperCamelCase:						Tuple   ,				__UpperCamelCase:						Union[str, Any]  )				->						Union[str, Any]:
										__magic_name__					:       List[str]    							=							self.num_labels
										__magic_name__					:       Dict    							=							SqueezeBertForSequenceClassification(__snake_case  )
										model.to(__snake_case  )
										model.eval()
										__magic_name__					:       Union[str, Any]    							=							model(__snake_case   ,				attention_mask=__snake_case   ,				labels=__snake_case  )
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)  )







						def lowerCAmelCase__			(	self:						int   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						Optional[Any]   ,				__UpperCamelCase:						Dict   ,				__UpperCamelCase:						Any   ,				__UpperCamelCase:						Optional[int]  )				->						int:
										__magic_name__					:       Any    							=							self.num_labels
										__magic_name__					:       List[str]    							=							SqueezeBertForTokenClassification(config=__snake_case  )
										model.to(__snake_case  )
										model.eval()

										__magic_name__					:       int    							=							model(__snake_case   ,				attention_mask=__snake_case   ,				labels=__snake_case  )
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)  )







						def lowerCAmelCase__			(	self:						Optional[int]   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						Optional[Any]   ,				__UpperCamelCase:						Union[str, Any]   ,				__UpperCamelCase:						Any   ,				__UpperCamelCase:						str   ,				__UpperCamelCase:						List[Any]  )				->						str:
										__magic_name__					:       List[Any]    							=							self.num_choices
										__magic_name__					:       str    							=							SqueezeBertForMultipleChoice(config=__snake_case  )
										model.to(__snake_case  )
										model.eval()
										__magic_name__					:       Any    							=							input_ids.unsqueeze(1  ).expand(-1   ,				self.num_choices   ,				-1  ).contiguous()
										__magic_name__					:       Optional[Any]    							=							input_mask.unsqueeze(1  ).expand(-1   ,				self.num_choices   ,				-1  ).contiguous()
										__magic_name__					:       Any    							=							model(
										    __snake_case   ,				attention_mask=__snake_case   ,				labels=__snake_case   ,				)
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)  )







						def lowerCAmelCase__			(	self:						Any  )				->						Tuple:
										__magic_name__					:       Dict    							=							self.prepare_config_and_inputs()
										((__magic_name__)							,  (__magic_name__)							,  (__magic_name__)							,  (__magic_name__)							,  (__magic_name__)							,  (__magic_name__))					:       List[Any]    							=							config_and_inputs
										__magic_name__					:       Optional[Any]    							=							{"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_torch
class 		_snake_case     (					snake_case_	,	snake_case_	,	unittest.TestCase		):






						'''simple docstring'''







						__snake_case				=   (
						    (
						        SqueezeBertModel,
						        SqueezeBertForMaskedLM,
						        SqueezeBertForMultipleChoice,
						        SqueezeBertForQuestionAnswering,
						        SqueezeBertForSequenceClassification,
						        SqueezeBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						__snake_case				=   (
						    {
						        '''feature-extraction''': SqueezeBertModel,
						        '''fill-mask''': SqueezeBertForMaskedLM,
						        '''question-answering''': SqueezeBertForQuestionAnswering,
						        '''text-classification''': SqueezeBertForSequenceClassification,
						        '''token-classification''': SqueezeBertForTokenClassification,
						        '''zero-shot''': SqueezeBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__snake_case				=   False
						__snake_case				=   True
						__snake_case				=   False







						def lowerCAmelCase__			(	self:						int  )				->						Dict:
										__magic_name__					:       List[str]    							=							SqueezeBertModelTester(self  )
										__magic_name__					:       Optional[int]    							=							ConfigTester(self   ,				config_class=__snake_case   ,				dim=37  )







						def lowerCAmelCase__			(	self:						List[Any]  )				->						List[Any]:
										self.config_tester.run_common_tests()







						def lowerCAmelCase__			(	self:						List[str]  )				->						Dict:
										__magic_name__					:       Any    							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_model(*__snake_case  )







						def lowerCAmelCase__			(	self:						Dict  )				->						Optional[Any]:
										__magic_name__					:       Union[str, Any]    							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_masked_lm(*__snake_case  )







						def lowerCAmelCase__			(	self:						Optional[Any]  )				->						Union[str, Any]:
										__magic_name__					:       List[str]    							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_question_answering(*__snake_case  )







						def lowerCAmelCase__			(	self:						List[Any]  )				->						List[str]:
										__magic_name__					:       Tuple    							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__snake_case  )







						def lowerCAmelCase__			(	self:						Optional[int]  )				->						Any:
										__magic_name__					:       Any    							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_token_classification(*__snake_case  )







						def lowerCAmelCase__			(	self:						Union[str, Any]  )				->						Tuple:
										__magic_name__					:       str    							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__snake_case  )







						@slow
						def lowerCAmelCase__			(	self:						List[str]  )				->						Tuple:
										for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__					:       List[Any]    							=							SqueezeBertModel.from_pretrained(__snake_case  )
														self.assertIsNotNone(__snake_case  )






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






						'''simple docstring'''







						@slow
						def lowerCAmelCase__			(	self:						Tuple  )				->						Union[str, Any]:
										__magic_name__					:       Any    							=							SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"  )

										__magic_name__					:       Union[str, Any]    							=							torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]  )
										__magic_name__					:       Union[str, Any]    							=							model(__snake_case  )[0]
										__magic_name__					:       Tuple    							=							torch.Size((1, 3)  )
										self.assertEqual(output.shape   ,				__snake_case  )
										__magic_name__					:       Optional[Any]    							=							torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]  )
										self.assertTrue(torch.allclose(__snake_case   ,				__snake_case   ,				atol=1E-4  )  )






'''simple docstring'''






class 					UpperCAmelCase	:




      def __init__(     self     :					List[str]     ,     __snake_case     :					str    )							-> Union[str, Any]:
            _lowerCAmelCase					=				val
            _lowerCAmelCase					=				None
            _lowerCAmelCase					=				None




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					int    )							-> Optional[int]:
            if self.val:
                  if val < self.val:
                        if self.left is None:
                              _lowerCAmelCase					=				Node(__snake_case    )
                        else:
                              self.left.insert(__snake_case    )
                  elif val > self.val:
                        if self.right is None:
                              _lowerCAmelCase					=				Node(__snake_case    )
                        else:
                              self.right.insert(__snake_case    )
            else:
                  _lowerCAmelCase					=				val




def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      if root:
            inorder(root.left				,      lowerCAmelCase    )
            res.append(root.val    )
            inorder(root.right				,      lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if len(lowerCAmelCase    ) == 0:
            return arr
      _lowerCAmelCase					=				Node(arr[0]    )
      for i in range(1				,      len(lowerCAmelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      _lowerCAmelCase					=				[]
      inorder(lowerCAmelCase				,      lowerCAmelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([10, 1, 3, 2, 9, 14, 13]))






