




"""simple docstring"""




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM


@require_torch
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->str:





           '''simple docstring'''

           A__							=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           A__							=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(_a)
           A__							=   -1

           A__							=   ids_tensor((1, 5) ,			vocab_size=model.config.vocab_size).to(_a)
           A__							=   model.generate(_a ,			max_new_tokens=10 ,			do_sample=_a)
           A__							=   tokenizer.decode(greedy_ids[0])

           with CaptureStdout() as cs:
                  A__							=   TextStreamer(_a)
                  model.generate(_a ,			max_new_tokens=10 ,			do_sample=_a ,			streamer=_a)
           # The greedy text should be printed to stdout, except for the final "\n" in the streamer
           A__							=   cs.out[:-1]

           self.assertEqual(_a ,			_a)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Any:





           '''simple docstring'''

           A__							=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           A__							=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(_a)
           A__							=   -1

           A__							=   ids_tensor((1, 5) ,			vocab_size=model.config.vocab_size).to(_a)
           A__							=   model.generate(_a ,			max_new_tokens=10 ,			do_sample=_a)
           A__							=   tokenizer.decode(greedy_ids[0])

           A__							=   TextIteratorStreamer(_a)
           A__							=   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
           A__							=   Thread(target=model.generate ,			kwargs=_a)
           thread.start()
           A__							=   ""
           for new_text in streamer:
                  streamer_text += new_text

           self.assertEqual(_a ,			_a)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__							=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           A__							=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(_a)
           A__							=   -1

           A__							=   ids_tensor((1, 5) ,			vocab_size=model.config.vocab_size).to(_a)
           A__							=   model.generate(_a ,			max_new_tokens=10 ,			do_sample=_a)
           A__							=   greedy_ids[:, input_ids.shape[1] :]
           A__							=   tokenizer.decode(new_greedy_ids[0])

           with CaptureStdout() as cs:
                  A__							=   TextStreamer(_a ,			skip_prompt=_a)
                  model.generate(_a ,			max_new_tokens=10 ,			do_sample=_a ,			streamer=_a)
           # The greedy text should be printed to stdout, except for the final "\n" in the streamer
           A__							=   cs.out[:-1]

           self.assertEqual(_a ,			_a)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Dict:





           '''simple docstring'''

           A__							=   AutoTokenizer.from_pretrained('''distilgpt2''')
           A__							=   AutoModelForCausalLM.from_pretrained('''distilgpt2''').to(_a)
           A__							=   -1

           A__							=   torch.ones((1, 5) ,			device=_a).long() * model.config.bos_token_id
           with CaptureStdout() as cs:
                  A__							=   TextStreamer(_a ,			skip_special_tokens=_a)
                  model.generate(_a ,			max_new_tokens=1 ,			do_sample=_a ,			streamer=_a)

           # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
           # re-tokenized, must only contain one token
           A__							=   cs.out[:-1]  # Remove the final "\n"
           A__							=   tokenizer(_a ,			return_tensors='''pt''')
           self.assertEqual(streamer_text_tokenized.input_ids.shape ,			(1, 1))





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict:





           '''simple docstring'''

           A__							=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           A__							=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(_a)
           A__							=   -1

           A__							=   ids_tensor((1, 5) ,			vocab_size=model.config.vocab_size).to(_a)
           A__							=   TextIteratorStreamer(_a ,			timeout=0.001)
           A__							=   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
           A__							=   Thread(target=model.generate ,			kwargs=_a)
           thread.start()

           # The streamer will timeout after 0.001 seconds, so an exception will be raised
           with self.assertRaises(_a):
                  A__							=   ""
                  for new_text in streamer:
                         streamer_text += new_text








from __future__ import annotations

import queue


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict)						->Any:





           '''simple docstring'''

           A__							=   data
           A__							=   None
           A__							=   None







def SCREAMING_SNAKE_CASE    (	)       ->	TreeNode:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   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    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   []
              while not q.empty():
                     A__							=   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(lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   []
       A__							=   node
       while n or stack:
              while n:
                     stack.append(lowercase_						)
                     A__							=   n.left
              A__							=   stack.pop()
              print(n.data			,    end=''','''						)
              A__							=   n.right







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__			,							A__							=   [], []
       A__							=   node
       stacka.append(lowercase_						)
       while stacka:  # to find the reversed order of post order, store it in stack2
              A__							=   stacka.pop()
              if n.left:
                     stacka.append(n.left						)
              if n.right:
                     stacka.append(n.right						)
              stacka.append(lowercase_						)
       while stacka:  # pop up from stack2 will be the post order
              print(stacka.pop().data			,    end=''','''						)







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""			,    lowercase_=50			,    lowercase_="*"						)       ->	str:



       """simple docstring"""

       if not s:
              return "\n" + width * char
       A__			,							A__							=   divmod(width - len(lowercase_						) - 2			,    2						)
       return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
  import doctest

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

  _lowerCamelCase     :		TreeNode								=       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())


