


"""simple docstring"""





import gc
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
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 (
	    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    CTRLForSequenceClassification,
	    CTRLLMHeadModel,
	    CTRLModel,
	)



class 		snake_case_:






					def __init__(	self  :					List[str]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Tuple=1_4					,				UpperCamelCase_  :					Optional[Any]=7					,				UpperCamelCase_  :					List[str]=True					,				UpperCamelCase_  :					Union[str, Any]=True					,				UpperCamelCase_  :					List[str]=True					,				UpperCamelCase_  :					Any=True					,				UpperCamelCase_  :					Optional[Any]=True					,				UpperCamelCase_  :					Union[str, Any]=9_9					,				UpperCamelCase_  :					Tuple=3_2					,				UpperCamelCase_  :					List[Any]=5					,				UpperCamelCase_  :					str=4					,				UpperCamelCase_  :					List[str]=3_7					,				UpperCamelCase_  :					List[str]="gelu"					,				UpperCamelCase_  :					Optional[int]=0.1					,				UpperCamelCase_  :					List[Any]=0.1					,				UpperCamelCase_  :					int=5_1_2					,				UpperCamelCase_  :					Optional[Any]=1_6					,				UpperCamelCase_  :					Union[str, Any]=2					,				UpperCamelCase_  :					int=0.02					,				UpperCamelCase_  :					List[str]=3					,				UpperCamelCase_  :					int=4					,				UpperCamelCase_  :					Union[str, Any]=None					,				):
											lowerCAmelCase					:	str       =   parent
											lowerCAmelCase					:	Optional[Any]       =   batch_size
											lowerCAmelCase					:	Optional[int]       =   seq_length
											lowerCAmelCase					:	Any       =   is_training
											lowerCAmelCase					:	List[str]       =   use_token_type_ids
											lowerCAmelCase					:	Any       =   use_input_mask
											lowerCAmelCase					:	Any       =   use_labels
											lowerCAmelCase					:	Tuple       =   use_mc_token_ids
											lowerCAmelCase					:	Union[str, Any]       =   vocab_size
											lowerCAmelCase					:	int       =   hidden_size
											lowerCAmelCase					:	Any       =   num_hidden_layers
											lowerCAmelCase					:	Union[str, Any]       =   num_attention_heads
											lowerCAmelCase					:	List[Any]       =   intermediate_size
											lowerCAmelCase					:	List[str]       =   hidden_act
											lowerCAmelCase					:	Tuple       =   hidden_dropout_prob
											lowerCAmelCase					:	str       =   attention_probs_dropout_prob
											lowerCAmelCase					:	str       =   max_position_embeddings
											lowerCAmelCase					:	int       =   type_vocab_size
											lowerCAmelCase					:	int       =   type_sequence_label_size
											lowerCAmelCase					:	Optional[Any]       =   initializer_range
											lowerCAmelCase					:	Optional[int]       =   num_labels
											lowerCAmelCase					:	List[Any]       =   num_choices
											lowerCAmelCase					:	List[Any]       =   scope
											lowerCAmelCase					:	int       =   self.vocab_size - 1






					def      lowerCamelCase__     (	self  :					int				):
											lowerCAmelCase					:	Any       =   ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size				)

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

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

											lowerCAmelCase					:	Any       =   None
											if self.use_mc_token_ids:
																	lowerCAmelCase					:	Tuple       =   ids_tensor([self.batch_size, self.num_choices]					,				self.seq_length				)

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

											lowerCAmelCase					:	Tuple       =   self.get_config()

											lowerCAmelCase					:	Union[str, Any]       =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,				2				)

											return (
											    config,
											    input_ids,
											    input_mask,
											    head_mask,
											    token_type_ids,
											    mc_token_ids,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											)






					def      lowerCamelCase__     (	self  :					Dict				):
											return CTRLConfig(
											    vocab_size=self.vocab_size					,				n_embd=self.hidden_size					,				n_layer=self.num_hidden_layers					,				n_head=self.num_attention_heads					,				n_positions=self.max_position_embeddings					,				pad_token_id=self.pad_token_id					,				)






					def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Optional[int]					,				*UpperCamelCase_  :					str				):
											lowerCAmelCase					:	List[str]       =   CTRLModel(config=lowercase_				)
											model.to(lowercase_				)
											model.eval()

											model(lowercase_					,				token_type_ids=lowercase_					,				head_mask=lowercase_				)
											model(lowercase_					,				token_type_ids=lowercase_				)
											lowerCAmelCase					:	Any       =   model(lowercase_				)
											self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)				)
											self.parent.assertEqual(len(result.past_key_values				)					,				config.n_layer				)






					def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Dict					,				*UpperCamelCase_  :					int				):
											lowerCAmelCase					:	Tuple       =   CTRLLMHeadModel(lowercase_				)
											model.to(lowercase_				)
											model.eval()

											lowerCAmelCase					:	int       =   model(lowercase_					,				token_type_ids=lowercase_					,				labels=lowercase_				)
											self.parent.assertEqual(result.loss.shape					,				()				)
											self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)				)






					def      lowerCamelCase__     (	self  :					Optional[int]				):
											lowerCAmelCase					:	Optional[Any]       =   self.prepare_config_and_inputs()

											(
											    lowerCAmelCase 
											)					:	Optional[Any]       =   config_and_inputs

											lowerCAmelCase					:	Tuple       =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

											return config, inputs_dict






					def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Dict					,				*UpperCamelCase_  :					Dict				):
											lowerCAmelCase					:	int       =   self.num_labels
											lowerCAmelCase					:	Optional[int]       =   CTRLForSequenceClassification(lowercase_				)
											model.to(lowercase_				)
											model.eval()
											lowerCAmelCase					:	int       =   ids_tensor([self.batch_size]					,				self.type_sequence_label_size				)
											lowerCAmelCase					:	Optional[Any]       =   model(lowercase_					,				token_type_ids=lowercase_					,				labels=lowercase_				)
											self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)				)



@require_torch
class 		snake_case_(       snake_case__							,					snake_case__							,					snake_case__							,					unittest.TestCase			):
					__UpperCamelCase  =					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
					__UpperCamelCase  =					(CTRLLMHeadModel,) if is_torch_available() else ()
					__UpperCamelCase  =					(
					    {
					        '''feature-extraction''': CTRLModel,
					        '''text-classification''': CTRLForSequenceClassification,
					        '''text-generation''': CTRLLMHeadModel,
					        '''zero-shot''': CTRLForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__UpperCamelCase  =					True
					__UpperCamelCase  =					False
					__UpperCamelCase  =					False






					def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
											if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																	# config could not be created.
																	return True

											return False






					def      lowerCamelCase__     (	self  :					str				):
											lowerCAmelCase					:	List[str]       =   CTRLModelTester(self				)
											lowerCAmelCase					:	str       =   ConfigTester(self					,				config_class=lowercase_					,				n_embd=3_7				)






					def      lowerCamelCase__     (	self  :					List[str]				):
											super().tearDown()
											# clean-up as much as possible GPU memory occupied by PyTorch
											gc.collect()
											torch.cuda.empty_cache()






					def      lowerCamelCase__     (	self  :					Union[str, Any]				):
											self.config_tester.run_common_tests()






					def      lowerCamelCase__     (	self  :					Dict				):
											lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_ctrl_model(*lowercase_				)






					def      lowerCamelCase__     (	self  :					Optional[int]				):
											lowerCAmelCase					:	Any       =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_lm_head_model(*lowercase_				)






					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
					def      lowerCamelCase__     (	self  :					Union[str, Any]				):
											pass






					@slow
					def      lowerCamelCase__     (	self  :					List[Any]				):
											for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCAmelCase					:	Dict       =   CTRLModel.from_pretrained(lowercase_				)
																	self.assertIsNotNone(lowercase_				)






					@unittest.skip('''The model doesn\'t support left padding'''				)  # and it's not used enough to be worth fixing :)
					def      lowerCamelCase__     (	self  :					Optional[Any]				):
											pass



@require_torch
class 		snake_case_(       unittest.TestCase			):






					def      lowerCamelCase__     (	self  :					Dict				):
											super().tearDown()
											# clean-up as much as possible GPU memory occupied by PyTorch
											gc.collect()
											torch.cuda.empty_cache()






					@slow
					def      lowerCamelCase__     (	self  :					Union[str, Any]				):
											lowerCAmelCase					:	str       =   CTRLLMHeadModel.from_pretrained('''ctrl'''				)
											model.to(lowercase_				)
											lowerCAmelCase					:	Union[str, Any]       =   torch.tensor(
											    [[1_1_8_5_9, 0, 1_6_1_1, 8]]					,				dtype=torch.long					,				device=lowercase_				)  # Legal the president is
											lowerCAmelCase					:	Tuple       =   [
											    1_1_8_5_9,
											    0,
											    1_6_1_1,
											    8,
											    5,
											    1_5_0,
											    2_6_4_4_9,
											    2,
											    1_9,
											    3_4_8,
											    4_6_9,
											    3,
											    2_5_9_5,
											    4_8,
											    2_0_7_4_0,
											    2_4_6_5_3_3,
											    2_4_6_5_3_3,
											    1_9,
											    3_0,
											    5,
											]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

											lowerCAmelCase					:	Optional[Any]       =   model.generate(lowercase_					,				do_sample=lowercase_				)
											self.assertListEqual(output_ids[0].tolist()					,				lowercase_				)






"""simple docstring"""





class 		snake_case_:






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Dict       =   val
           lowerCAmelCase					:	str       =   None
           lowerCAmelCase					:	Dict       =   None






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           if self.val:
                 if val < self.val:
                       if self.left is None:
                             lowerCAmelCase					:	int       =   Node(UpperCamelCase_				)
                       else:
                             self.left.insert(UpperCamelCase_				)
                 elif val > self.val:
                       if self.right is None:
                             lowerCAmelCase					:	Any       =   Node(UpperCamelCase_				)
                       else:
                             self.right.insert(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[Any]       =   val



def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			str			):
      # Recursive traversal
      if root:
            inorder(root.left ,   _snake_case			)
            res.append(root.val			)
            inorder(root.right ,   _snake_case			)


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


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


