


from __future__ import annotations

import queue

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    data
         snake_case					=    None
         snake_case					=    None






def       UpperCAmelCase__						():



     """simple docstring"""






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



     """simple docstring"""






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



     """simple docstring"""






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



     """simple docstring"""






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



     """simple docstring"""






     if not isinstance(UpperCamelCase_							,UpperCamelCase_    ) or not node:
          return
     snake_case					=    queue.Queue()
     q.put(UpperCamelCase_    )
     while not q.empty():
          snake_case					=    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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






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



     """simple docstring"""






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



     """simple docstring"""






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



     """simple docstring"""






     if not isinstance(UpperCamelCase_							,UpperCamelCase_    ) or not node:
          return
     snake_case			,  snake_case					=    [], []
     snake_case					=    node
     stacka.append(UpperCamelCase_    )
     while stacka:  # to find the reversed order of post order, store it in stack2
          snake_case					=    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       UpperCAmelCase__						(UpperCamelCase_ = ""							,UpperCamelCase_=50							,UpperCamelCase_="*"    ):



     """simple docstring"""






     if not s:
          return "\n" + width * char
     snake_case			,  snake_case					=    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"))

 _SCREAMING_SNAKE_CASE      :			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())








import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort

class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
    def 		a_      (	self      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, 3, 1_2_8, 1_2_8)      , rng=random.Random(__snake_case   )   )
         snake_case					=    np.random.RandomState(__snake_case   )
         snake_case					=    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.75,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]   )
         assert np.abs(image_slice - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    PNDMScheduler.from_config(pipe.scheduler.config      , skip_prk_steps=__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         # warmup pass to apply optimizations
         snake_case					=    pipe(**self.get_dummy_inputs()   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1




    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1






@nightly
@require_onnxruntime
@require_torch_gpu
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @property
    def 		a_      (	self   ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def 		a_      (	self   ):
         snake_case					=    ort.SessionOptions()
         snake_case					=    False
         return options
    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         # using the PNDM scheduler by default
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''      , revision='''onnx'''      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=1_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2




    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         snake_case					=    LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , subfolder='''scheduler'''      , revision='''onnx'''   )
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , revision='''onnx'''      , scheduler=__snake_case      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=2_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2




