



import unittest

from transformers import AutoTokenizer, NystromformerConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    NystromformerForMaskedLM,
						    NystromformerForMultipleChoice,
						    NystromformerForQuestionAnswering,
						    NystromformerForSequenceClassification,
						    NystromformerForTokenClassification,
						    NystromformerModel,
						)
						from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=13 ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=99 ,					__UpperCAmelCase=32 ,					__UpperCAmelCase=5 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=37 ,					__UpperCAmelCase="gelu" ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=512 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= parent
											a__	:							Optional[int]      						= batch_size
											a__	:							Optional[int]      						= seq_length
											a__	:							List[str]      						= is_training
											a__	:							Any      						= use_input_mask
											a__	:							str      						= use_token_type_ids
											a__	:							List[Any]      						= use_labels
											a__	:							List[Any]      						= vocab_size
											a__	:							Union[str, Any]      						= hidden_size
											a__	:							Any      						= num_hidden_layers
											a__	:							Any      						= num_attention_heads
											a__	:							List[str]      						= intermediate_size
											a__	:							Union[str, Any]      						= hidden_act
											a__	:							Any      						= hidden_dropout_prob
											a__	:							Any      						= attention_probs_dropout_prob
											a__	:							List[Any]      						= max_position_embeddings
											a__	:							Optional[int]      						= type_vocab_size
											a__	:							Dict      						= type_sequence_label_size
											a__	:							int      						= initializer_range
											a__	:							int      						= num_labels
											a__	:							Any      						= num_choices
											a__	:							List[str]      						= scope



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size				)

											a__	:							Union[str, Any]      						= None
											if self.use_input_mask:
															a__	:							Optional[int]      						= random_attention_mask([self.batch_size, self.seq_length]				)

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

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

											a__	:							Optional[Any]      						= self.get_config()

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



							def 		_A	(							self				):







											"""simple docstring"""



											return NystromformerConfig(
											    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=__UpperCAmelCase ,					initializer_range=self.initializer_range ,					)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= NystromformerModel(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							List[Any]      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase				)
											a__	:							List[str]      						= model(__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase				)
											a__	:							Tuple      						= model(__UpperCAmelCase				)
											self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= NystromformerForMaskedLM(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							List[Any]      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= NystromformerForQuestionAnswering(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Any      						= model(
											    __UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					start_positions=__UpperCAmelCase ,					end_positions=__UpperCAmelCase ,					)
											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 ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							str      						= self.num_labels
											a__	:							int      						= NystromformerForSequenceClassification(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Optional[Any]      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Dict      						= self.num_labels
											a__	:							Union[str, Any]      						= NystromformerForTokenClassification(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							int      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= self.num_choices
											a__	:							List[str]      						= NystromformerForMultipleChoice(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Optional[int]      						= input_ids.unsqueeze(1				).expand(-1 ,					self.num_choices ,					-1				).contiguous()
											a__	:							Optional[int]      						= token_type_ids.unsqueeze(1				).expand(-1 ,					self.num_choices ,					-1				).contiguous()
											a__	:							int      						= input_mask.unsqueeze(1				).expand(-1 ,					self.num_choices ,					-1				).contiguous()
											a__	:							List[str]      						= model(
											    __UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase ,					)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.prepare_config_and_inputs()
											(
											    (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   
											)	:							List[str]      						= config_and_inputs
											a__	:							Optional[int]      						= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
											return config, inputs_dict





@require_torch
class 				_a						(       SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Optional[Any] =       (
							    (
							        NystromformerModel,
							        NystromformerForMaskedLM,
							        NystromformerForMultipleChoice,
							        NystromformerForQuestionAnswering,
							        NystromformerForSequenceClassification,
							        NystromformerForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							A				:Any =       (
							    {
							        "feature-extraction": NystromformerModel,
							        "fill-mask": NystromformerForMaskedLM,
							        "question-answering": NystromformerForQuestionAnswering,
							        "text-classification": NystromformerForSequenceClassification,
							        "token-classification": NystromformerForTokenClassification,
							        "zero-shot": NystromformerForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							A				:Optional[Any] =       False
							A				:Optional[int] =       False



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= NystromformerModelTester(self				)
											a__	:							Any      						= ConfigTester(self ,					config_class=__UpperCAmelCase ,					hidden_size=37				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.config_tester.run_common_tests()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
															a__	:							Dict      						= type
															self.model_tester.create_and_check_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__	:							Any      						= NystromformerModel.from_pretrained(__UpperCAmelCase				)
															self.assertIsNotNone(__UpperCAmelCase				)





@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= NystromformerModel.from_pretrained("uw-madison/nystromformer-512"				)
											a__	:							List[Any]      						= torch.tensor([[0, 1, 2, 3, 4, 5]]				)

											with torch.no_grad():
															a__	:							int      						= model(__UpperCAmelCase				)[0]

											a__	:							Tuple      						= torch.Size((1, 6, 768)				)
											self.assertEqual(output.shape ,					__UpperCAmelCase				)

											a__	:							Dict      						= torch.tensor(
											    [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]				)

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




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= "the [MASK] of Belgium is Brussels"

											a__	:							List[Any]      						= AutoTokenizer.from_pretrained("uw-madison/nystromformer-512"				)
											a__	:							Tuple      						= NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512"				)

											a__	:							Dict      						= tokenizer(__UpperCAmelCase ,					return_tensors="pt"				)

											with torch.no_grad():
															a__	:							Dict      						= model(encoding.input_ids				).logits

											a__	:							Optional[Any]      						= token_logits[:, 2, :].argmax(-1				)[0]

											self.assertEqual(tokenizer.decode(__UpperCAmelCase				) ,					"capital"				)







from __future__ import annotations

import queue


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= data
											a__	:							Tuple      						= None
											a__	:							Optional[Any]      						= None






def     SCREAMING_SNAKE_CASE(      )	->						TreeNode:
				print("\n********Press N to stop entering at any point of time********\n" )
				a__	:							Tuple      						= input("Enter the value of the root node: " ).strip().lower()
				a__	:							queue.Queue      						= queue.Queue()
				a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							str      						= q.get()
								a__	:							Optional[Any]      						= F'Enter the left node of {node_found.data}: '
								a__	:							List[Any]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							Optional[int]      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							List[str]      						= left_node
								q.put(__UpperCamelCase )
								a__	:							Dict      						= F'Enter the right node of {node_found.data}: '
								a__	:							List[str]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							Any      						= right_node
								q.put(__UpperCamelCase )
				raise






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				print(node.data	,				end="," )
				pre_order(node.left )
				pre_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				in_order(node.left )
				print(node.data	,				end="," )
				in_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				post_order(node.left )
				post_order(node.right )
				print(node.data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							Tuple      						= q.get()
								print(node_dequeued.data	,				end="," )
								if node_dequeued.left:
												q.put(node_dequeued.left )
								if node_dequeued.right:
												q.put(node_dequeued.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							int      						= []
								while not q.empty():
												a__	:							Union[str, Any]      						= q.get()
												print(node_dequeued.data	,				end="," )
												if node_dequeued.left:
																list_.append(node_dequeued.left )
												if node_dequeued.right:
																list_.append(node_dequeued.right )
								print()
								for node in list_:
												q.put(__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Optional[int]      						= node
				while n or stack:
								while n:  # start from root node, find its left child
												print(n.data	,				end="," )
												stack.append(__UpperCamelCase )
												a__	:							int      						= n.left
								# end of while means current node doesn't have left child
								a__	:							Dict      						= stack.pop()
								# start to traverse its right child
								a__	:							str      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Dict      						= node
				while n or stack:
								while n:
												stack.append(__UpperCamelCase )
												a__	:							List[Any]      						= n.left
								a__	:							str      						= stack.pop()
								print(n.data	,				end="," )
								a__	:							Any      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__						,   a__	:							List[str]      						= [], []
				a__	:							List[Any]      						= node
				stacka.append(__UpperCamelCase )
				while stacka:  # to find the reversed order of post order, store it in stack2
								a__	:							Optional[Any]      						= stacka.pop()
								if n.left:
												stacka.append(n.left )
								if n.right:
												stacka.append(n.right )
								stacka.append(__UpperCamelCase )
				while stacka:  # pop up from stack2 will be the post order
								print(stacka.pop().data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = ""	,				__UpperCamelCase=50	,				__UpperCamelCase="*" )	->						str:
				if not s:
								return "\n" + width * char
				a__						,   a__	:							int      						= divmod(width - len(__UpperCamelCase ) - 2	,				2 )
				return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt("""Binary Tree Traversals"""))

						lowerCamelCase		       =      build_tree()
						print(prompt("""Pre Order Traversal"""))
						pre_order(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal"""))
						in_order(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal"""))
						post_order(node)
						print(prompt() + """\n""")

						print(prompt("""Level Order Traversal"""))
						level_order(node)
						print(prompt() + """\n""")

						print(prompt("""Actual Level Order Traversal"""))
						level_order_actual(node)
						print("""*""" * 50 + """\n""")

						print(prompt("""Pre Order Traversal - Iteration Version"""))
						pre_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal - Iteration Version"""))
						in_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal - Iteration Version"""))
						post_order_iter(node)
						print(prompt())


