



"""simple docstring"""

from collections.abc import Iterable
from typing import Any




class SCREAMING_SNAKE_CASE__					:



						def __init__(self						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       value
													__a		:		Optional[Any]									=       None  # Added in order to delete a node easier
													__a		:		List[str]									=       None
													__a		:		Dict									=       None





						def __repr__(self							):





													'''simple docstring'''

													from pprint import pformat

													if self.left is None and self.right is None:
																				return str(self.value							)
													return pformat({F'''{self.value}''': (self.left, self.right)}						,     indent=1							)







class SCREAMING_SNAKE_CASE__					:



						def __init__(self						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		Any									=       root



						def __str__(self							):





													'''simple docstring'''

													return str(self.root							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													if new_children is not None:  # reset its kids
																				__a		:		Union[str, Any]									=       node.parent
													if node.parent is not None:  # reset its parent
																				if self.is_right(snake_case_							):  # If it is the right children
																											__a		:		Tuple									=       new_children
																				else:
																											__a		:		Optional[Any]									=       new_children
													else:
																				__a		:		str									=       new_children



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													if node.parent and node.parent.right:
																				return node == node.parent.right
													return False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.root is None



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       Node(snake_case_							)  # create a new Node
													if self.empty():  # if Tree is empty
																				__a		:		Dict									=       new_node  # set its root
													else:  # Tree is not empty
																				__a		:		Optional[int]									=       self.root  # from root
																				if parent_node is None:
																											return
																				while True:  # While we don't get to a leaf
																											if value < parent_node.value:  # We go left
																																		if parent_node.left is None:
																																									__a		:		Any									=       new_node  # We insert the new node in a leaf
																																									break
																																		else:
																																									__a		:		Tuple									=       parent_node.left
																											else:
																																		if parent_node.right is None:
																																									__a		:		Union[str, Any]									=       new_node
																																									break
																																		else:
																																									__a		:		Optional[int]									=       parent_node.right
																				__a		:		Tuple									=       parent_node



						def     lowerCAmelCase__(self						,     *_lowercase							):





													'''simple docstring'''

													for value in values:
																				self.__insert(snake_case_							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													if self.empty():
																				raise IndexError("""Warning: Tree is empty! please use another."""							)
													else:
																				__a		:		Any									=       self.root
																				# use lazy evaluation here to avoid NoneType Attribute error
																				while node is not None and node.value is not value:
																											__a		:		Tuple									=       node.left if value < node.value else node.right
																				return node



						def     lowerCAmelCase__(self						,     _lowercase = None							):





													'''simple docstring'''

													if node is None:
																				if self.root is None:
																											return None
																				__a		:		List[Any]									=       self.root

													if not self.empty():
																				while node.right is not None:
																											__a		:		Optional[Any]									=       node.right
													return node



						def     lowerCAmelCase__(self						,     _lowercase = None							):





													'''simple docstring'''

													if node is None:
																				__a		:		Optional[int]									=       self.root
													if self.root is None:
																				return None
													if not self.empty():
																				__a		:		str									=       self.root
																				while node.left is not None:
																											__a		:		str									=       node.left
													return node



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.search(snake_case_							)  # Look for the node with that label
													if node is not None:
																				if node.left is None and node.right is None:  # If it has no children
																											self.__reassign_nodes(snake_case_						,     snake_case_							)
																				elif node.left is None:  # Has only right children
																											self.__reassign_nodes(snake_case_						,     node.right							)
																				elif node.right is None:  # Has only left children
																											self.__reassign_nodes(snake_case_						,     node.left							)
																				else:
																											__a		:		int									=       self.get_max(
																											    node.left							)  # Gets the max value of the left branch
																											self.remove(tmp_node.value							)  # type: ignore
																											__a		:		List[str]									=       (
																											    tmp_node.value  # type: ignore
																											)  # Assigns the value to the node to delete and keep tree structure



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													if node is not None:
																				yield node  # Preorder Traversal
																				yield from self.preorder_traverse(node.left							)
																				yield from self.preorder_traverse(node.right							)



						def     lowerCAmelCase__(self						,     _lowercase=None							):





													'''simple docstring'''

													if traversal_function is None:
																				return self.preorder_traverse(self.root							)
													else:
																				return traversal_function(self.root							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													if node:
																				self.inorder(snake_case_						,     node.left							)
																				arr.append(node.value							)
																				self.inorder(snake_case_						,     node.right							)





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       []
													self.inorder(snake_case_						,     snake_case_							)  # append all values to list using inorder traversal
													return arr[k - 1]







def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
							__a		:		Optional[Any]									=       []
							if curr_node is not None:
														__a		:		Optional[Any]									=       postorder(curr_node.left					) + postorder(curr_node.right					) + [curr_node]
							return node_list





def       __magic_name__    (   ):
							__a		:		Any									=       (8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
							__a		:		List[str]									=       BinarySearchTree()
							for i in testlist:
														t.insert(_lowerCamelCase					)

							# Prints all the elements of the list in order traversal
							print(_lowerCamelCase					)

							if t.search(6					) is not None:
														print("""The value 6 exists"""					)
							else:
														print("""The value 6 doesn\'t exist"""					)

							if t.search(-1					) is not None:
														print("""The value -1 exists"""					)
							else:
														print("""The value -1 doesn\'t exist"""					)

							if not t.empty():
														print("""Max Value: """  ,       t.get_max().value					)  # type: ignore
														print("""Min Value: """  ,       t.get_min().value					)  # type: ignore

							for i in testlist:
														t.remove(_lowerCamelCase					)
														print(_lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)











"""simple docstring"""

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowercase__			   =  {"LayoutLMv2Config", "LayoutLMv3Config"}




@is_pipeline_test
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):
						_lowerCAmelCase							=					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						_lowerCAmelCase							=					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

						if model_mapping is not None:
												_lowerCAmelCase							=					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
						if tf_model_mapping is not None:
												_lowerCAmelCase							=					{
												    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
												}



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       pipeline(
													    task="""text-classification"""						,     model="""hf-internal-testing/tiny-random-distilbert"""						,     framework="""pt"""							)

													__a		:		Tuple									=       text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													__a		:		Optional[Any]									=       text_classifier("""This is great !"""						,     top_k=2							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]							)

													__a		:		int									=       text_classifier(["""This is great !""", """This is bad"""]						,     top_k=2							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													    ]						,     )

													__a		:		List[str]									=       text_classifier("""This is great !"""						,     top_k=1							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													# Legacy behavior
													__a		:		Optional[int]									=       text_classifier("""This is great !"""						,     return_all_scores=_lowercase							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													__a		:		Tuple									=       text_classifier("""This is great !"""						,     return_all_scores=_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]]							)

													__a		:		Any									=       text_classifier(["""This is great !""", """Something else"""]						,     return_all_scores=_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													    ]						,     )

													__a		:		Union[str, Any]									=       text_classifier(["""This is great !""", """Something else"""]						,     return_all_scores=_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [
													        {"""label""": """LABEL_0""", """score""": 0.504},
													        {"""label""": """LABEL_0""", """score""": 0.504},
													    ]						,     )



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													import torch

													__a		:		Any									=       pipeline(
													    task="""text-classification"""						,     model="""hf-internal-testing/tiny-random-distilbert"""						,     framework="""pt"""						,     device=torch.device("""cpu"""							)						,     )

													__a		:		Optional[int]									=       text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}]							)



						@require_tf
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       pipeline(
													    task="""text-classification"""						,     model="""hf-internal-testing/tiny-random-distilbert"""						,     framework="""tf"""							)

													__a		:		List[str]									=       text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}]							)



						@slow
						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       pipeline("""text-classification"""							)

													__a		:		Tuple									=       text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """POSITIVE""", """score""": 1.0}]							)
													__a		:		Optional[int]									=       text_classifier("""This is bad !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """NEGATIVE""", """score""": 1.0}]							)
													__a		:		Union[str, Any]									=       text_classifier("""Birds are a type of animal"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """POSITIVE""", """score""": 0.988}]							)



						@slow
						@require_tf
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       pipeline("""text-classification"""						,     framework="""tf"""							)

													__a		:		str									=       text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """POSITIVE""", """score""": 1.0}]							)
													__a		:		Tuple									=       text_classifier("""This is bad !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """NEGATIVE""", """score""": 1.0}]							)
													__a		:		str									=       text_classifier("""Birds are a type of animal"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """POSITIVE""", """score""": 0.988}]							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Dict									=       TextClassificationPipeline(model=_lowercase						,     tokenizer=_lowercase							)
													return text_classifier, ["HuggingFace is in", "This is another test"]





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		List[str]									=       text_classifier.model
													# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
													__a		:		Union[str, Any]									=       """HuggingFace is in"""
													__a		:		List[str]									=       text_classifier(_lowercase							)

													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}]							)
													self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()							)

													__a		:		Optional[int]									=       ["""HuggingFace is in """, """Paris is in France"""]
													__a		:		Dict									=       text_classifier(_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}, {"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}]						,     )
													self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()							)
													self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values()							)

													# Forcing to get all results with `top_k=None`
													# This is NOT the legacy format
													__a		:		Dict									=       text_classifier(_lowercase						,     top_k=_lowercase							)
													__a		:		Dict									=       len(model.config.idalabel.values()							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [[{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}] * N, [{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}] * N]						,     )

													__a		:		Dict									=       {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
													__a		:		Any									=       text_classifier(_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     {"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}						,     )
													self.assertTrue(outputs["""label"""] in model.config.idalabel.values()							)

													# This might be used a text pair, but tokenizer + pipe interaction
													# makes it hard to understand that it's not using the pair properly
													# https://github.com/huggingface/transformers/issues/17305
													# We disabled this usage instead as it was outputting wrong outputs.
													__a		:		Dict									=       [["""HuggingFace is in """, """Paris is in France"""]]
													with self.assertRaises(_lowercase							):
																				text_classifier(_lowercase							)

													# This used to be valid for doing text pairs
													# We're keeping it working because of backward compatibility
													__a		:		Optional[int]									=       text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]]							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}]						,     )
													self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()							)

