
'''simple docstring'''

import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:






		class  A  :





			@staticmethod
			def 				__SCREAMING_SNAKE_CASE	(       *__magic_name__      :							List[Any]   ,				**__magic_name__      :							Dict				):


						"""simple docstring"""

						pass






@is_pipeline_test
@require_vision
class  A  (						unittest.TestCase     ):





	@require_torch
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				pipeline(
				    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"   ,				)
				lowerCAmelCase__										=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
				lowerCAmelCase__										=				image_classifier(__magic_name__   ,				candidate_labels=["a", "b", "c"]				)

				# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
				# python and torch versions.
				self.assertIn(
				    nested_simplify(__magic_name__				)   ,				[
				        [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
				        [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
				    ]   ,				)

				lowerCAmelCase__										=				image_classifier([image] * 5   ,				candidate_labels=["A", "B", "C"]   ,				batch_size=2				)
				self.assertEqual(
				    nested_simplify(__magic_name__				)   ,				[
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				    ]   ,				)





	@require_tf
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				pipeline(
				    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"   ,				framework="tf"				)
				lowerCAmelCase__										=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
				lowerCAmelCase__										=				image_classifier(__magic_name__   ,				candidate_labels=["a", "b", "c"]				)

				self.assertEqual(
				    nested_simplify(__magic_name__				)   ,				[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}]   ,				)

				lowerCAmelCase__										=				image_classifier([image] * 5   ,				candidate_labels=["A", "B", "C"]   ,				batch_size=2				)
				self.assertEqual(
				    nested_simplify(__magic_name__				)   ,				[
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				        [
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				            {"score": 0.333, "label": ANY(__magic_name__				)},
				        ],
				    ]   ,				)





	@slow
	@require_torch
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				pipeline(
				    task="zero-shot-image-classification"   ,				model="openai/clip-vit-base-patch32"   ,				)
				# This is an image of 2 cats with remotes and no planes
				lowerCAmelCase__										=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
				lowerCAmelCase__										=				image_classifier(__magic_name__   ,				candidate_labels=["cat", "plane", "remote"]				)

				self.assertEqual(
				    nested_simplify(__magic_name__				)   ,				[
				        {"score": 0.511, "label": "remote"},
				        {"score": 0.485, "label": "cat"},
				        {"score": 0.004, "label": "plane"},
				    ]   ,				)

				lowerCAmelCase__										=				image_classifier([image] * 5   ,				candidate_labels=["cat", "plane", "remote"]   ,				batch_size=2				)
				self.assertEqual(
				    nested_simplify(__magic_name__				)   ,				[
				        [
				            {"score": 0.511, "label": "remote"},
				            {"score": 0.485, "label": "cat"},
				            {"score": 0.004, "label": "plane"},
				        ],
				    ]
				    * 5   ,				)







	@slow
	@require_tf
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				pipeline(
				    task="zero-shot-image-classification"   ,				model="openai/clip-vit-base-patch32"   ,				framework="tf"				)
				# This is an image of 2 cats with remotes and no planes
				lowerCAmelCase__										=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
				lowerCAmelCase__										=				image_classifier(__magic_name__   ,				candidate_labels=["cat", "plane", "remote"]				)
				self.assertEqual(
				    nested_simplify(__magic_name__				)   ,				[
				        {"score": 0.511, "label": "remote"},
				        {"score": 0.485, "label": "cat"},
				        {"score": 0.004, "label": "plane"},
				    ]   ,				)

				lowerCAmelCase__										=				image_classifier([image] * 5   ,				candidate_labels=["cat", "plane", "remote"]   ,				batch_size=2				)
				self.assertEqual(
				    nested_simplify(__magic_name__				)   ,				[
				        [
				            {"score": 0.511, "label": "remote"},
				            {"score": 0.485, "label": "cat"},
				            {"score": 0.004, "label": "plane"},
				        ],
				    ]
				    * 5   ,				)











'''simple docstring'''



from __future__ import annotations

import queue






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						data
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						None
             SCREAMING_SNAKE_CASE   :      List[str]    					=						None




def   lowercase__(			):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      print(node.data      ,end=','    )
      pre_order(node.left    )
      pre_order(node.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      in_order(node.left    )
      print(node.data      ,end=','    )
      in_order(node.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      post_order(node.left    )
      post_order(node.right    )
      print(node.data      ,end=','    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
            while not q.empty():
                  SCREAMING_SNAKE_CASE   :      List[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   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      list[TreeNode]    					=						[]
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						node
      while n or stack:
            while n:  # start from root node, find its left child
                  print(n.data      ,end=','    )
                  stack.append(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      Any    					=						n.left
            # end of while means current node doesn't have left child
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						stack.pop()
            # start to traverse its right child
            SCREAMING_SNAKE_CASE   :      Any    					=						n.right




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      list[TreeNode]    					=						[]
      SCREAMING_SNAKE_CASE   :      int    					=						node
      while n or stack:
            while n:
                  stack.append(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						n.left
            SCREAMING_SNAKE_CASE   :      Tuple    					=						stack.pop()
            print(n.data      ,end=','    )
            SCREAMING_SNAKE_CASE   :      str    					=						n.right




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						[], []
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						node
      stacka.append(__UpperCamelCase    )
      while stacka:  # to find the reversed order of post order, store it in stack2
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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   lowercase__(			__UpperCamelCase:					str = ""      ,__UpperCamelCase:					Dict=50      ,__UpperCamelCase:					Optional[int]="*"    ):

      """simple docstring"""






      if not s:
            return "\n" + width * char
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[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"))

      UpperCamelCase_    	=				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("*" * 5_0 + "\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())


