


"""simple docstring"""







import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 						_A  (  _a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	VideoToVideoSDPipeline
    UpperCAmelCase	:							List[str]     =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}  ) - {"""image""", """width""", """height"""}
    UpperCAmelCase	:							Union[str, Any]     =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}  ) - {"""image"""}
    UpperCAmelCase	:							Dict     =	PipelineTesterMixin.required_optional_params - {"""latents"""}
    UpperCAmelCase	:							Tuple     =	False

    # No `output_type`.
    UpperCAmelCase	:							Tuple     =	frozenset(
        [
            """num_inference_steps""",
            """generator""",
            """latents""",
            """return_dict""",
            """callback""",
            """callback_steps""",
        ]  )






    def      __snake_case							( self    :   str):
      torch.manual_seed(0)
      a						:		Tuple											=      UNetaDConditionModel(
          block_out_channels=(32, 64, 64, 64)    ,			layers_per_block=2    ,			sample_size=32    ,			in_channels=4    ,			out_channels=4    ,			down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")    ,			up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")    ,			cross_attention_dim=32    ,			attention_head_dim=4    ,			)
      a						:		Optional[int]											=      DDIMScheduler(
          beta_start=0.00_085    ,			beta_end=0.012    ,			beta_schedule="scaled_linear"    ,			clip_sample=__UpperCAmelCase    ,			set_alpha_to_one=__UpperCAmelCase    ,			)
      torch.manual_seed(0)
      a						:		Tuple											=      AutoencoderKL(
          block_out_channels=[32, 64]    ,			in_channels=3    ,			out_channels=3    ,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,			latent_channels=4    ,			sample_size=128    ,			)
      torch.manual_seed(0)
      a						:		Optional[Any]											=      CLIPTextConfig(
          bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=32    ,			intermediate_size=37    ,			layer_norm_eps=1e-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=1000    ,			hidden_act="gelu"    ,			projection_dim=512    ,			)
      a						:		Optional[int]											=      CLIPTextModel(__UpperCAmelCase)
      a						:		List[Any]											=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      a						:		int											=      {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
      }
      return components






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Tuple=0):
      # 3 frames
      a						:		Optional[Any]											=      floats_tensor((1, 3, 3, 32, 32)    ,			rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		List[str]											=      torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		List[str]											=      torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		Tuple											=      {
          "prompt": "A painting of a squirrel eating a burger",
          "video": video,
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "pt",
      }
      return inputs






    def      __snake_case							( self    :   Optional[int]):
      a						:		List[Any]											=      "cpu"  # ensure determinism for the device-dependent torch.Generator
      a						:		Tuple											=      self.get_dummy_components()
      a						:		Tuple											=      VideoToVideoSDPipeline(**__UpperCAmelCase)
      a						:		Optional[int]											=      sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[int]											=      self.get_dummy_inputs(__UpperCAmelCase)
      a						:		Optional[Any]											=      "np"
      a						:		Optional[Any]											=      sd_pipe(**__UpperCAmelCase).frames
      a						:		int											=      frames[0][-3:, -3:, -1]

      assert frames[0].shape == (32, 32, 3)
      a						:		Union[str, Any]											=      np.array([106, 117, 113, 174, 137, 112, 148, 151, 131])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,			reason="XFormers attention is only available with CUDA and `xformers` installed"    ,			)
    def      __snake_case							( self    :   List[Any]):
      self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCAmelCase    ,			expected_max_diff=5e-3)






    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
    def      __snake_case							( self    :   List[Any]):
      pass






    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
    def      __snake_case							( self    :   Tuple):
      pass






    @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline.")
    def      __snake_case							( self    :   Dict):
      pass






    def      __snake_case							( self    :   Optional[Any]):
      return super().test_progress_bar()






@slow
@skip_mps
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def      __snake_case							( self    :   Optional[Any]):
      a						:		Optional[int]											=      VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"    ,			torch_dtype=torch.floataa)
      pipe.enable_model_cpu_offload()

      # 10 frames
      a						:		Union[str, Any]											=      torch.Generator(device="cpu").manual_seed(0)
      a						:		Optional[Any]											=      torch.randn((1, 10, 3, 1024, 576)    ,			generator=__UpperCAmelCase)
      a						:		Optional[int]											=      video.to("cuda")

      a						:		int											=      "Spiderman is surfing"

      a						:		Optional[Any]											=      pipe(__UpperCAmelCase    ,			video=__UpperCAmelCase    ,			generator=__UpperCAmelCase    ,			num_inference_steps=3    ,			output_type="pt").frames

      a						:		Union[str, Any]											=      np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656])
      assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array).sum() < 1e-2










"""simple docstring"""







from __future__ import annotations

import queue






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[Any]											=      data
      a						:		Optional[int]											=      None
      a						:		Union[str, Any]											=      None






def 					lowercase      (    )->				TreeNode:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		str											=      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      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		Union[str, Any]											=      node
  while n or stack:
    while n:
      stack.append(A_	)
      a						:		int											=      n.left
    a						:		str											=      stack.pop()
    print(n.data  ,       end=","	)
    a						:		Optional[int]											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a							,		a						:		Tuple											=      [], []
  a						:		Tuple											=      node
  stacka.append(A_	)
  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(A_	)
  while stacka:  # pop up from stack2 will be the post order
    print(stacka.pop().data  ,       end=","	)


def 					lowercase      (    A_ = ""  ,       A_=50  ,       A_="*"	)->				str:
  '''simple docstring'''







  if not s:
    return "\n" + width * char
  a							,		a						:		Dict											=      divmod(width - len(A_	) - 2  ,       2	)
  return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
 import doctest

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

 __lowercase    =				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())






