




import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_lowerCamelCase	:		str							       =   logging.get_logger(__name__)

enable_full_determinism()


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      UNetaDModel
    UpperCAmelCase__							  =      'sample'


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





           '''simple docstring'''

           A__       =    4
           A__       =    3
           A__       =    (32, 32)

           A__       =    floats_tensor((batch_size, num_channels) + sizes).to(_SCREAMING_SNAKE_CASE)
           A__       =    torch.tensor([10]).to(_SCREAMING_SNAKE_CASE)

           return {"sample": noise, "timestep": time_step}


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





           '''simple docstring'''

           return (3, 32, 32)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[Any]:





           '''simple docstring'''

           return (3, 32, 32)





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





           '''simple docstring'''

           A__       =    {
               """block_out_channels""": (32, 64),
               """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
               """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
               """attention_head_dim""": 3,
               """out_channels""": 3,
               """in_channels""": 3,
               """layers_per_block""": 2,
               """sample_size""": 32,
           }
           A__       =    self.dummy_input
           return init_dict, inputs_dict


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      UNetaDModel
    UpperCAmelCase__							  =      'sample'


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->str:





           '''simple docstring'''

           A__       =    4
           A__       =    4
           A__       =    (32, 32)

           A__       =    floats_tensor((batch_size, num_channels) + sizes).to(_SCREAMING_SNAKE_CASE)
           A__       =    torch.tensor([10]).to(_SCREAMING_SNAKE_CASE)

           return {"sample": noise, "timestep": time_step}


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           return (4, 32, 32)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           return (4, 32, 32)


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





           '''simple docstring'''

           A__       =    {
               """sample_size""": 32,
               """in_channels""": 4,
               """out_channels""": 4,
               """layers_per_block""": 2,
               """block_out_channels""": (32, 64),
               """attention_head_dim""": 32,
               """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
               """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
           }
           A__       =    self.dummy_input
           return init_dict, inputs_dict


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Any:





           '''simple docstring'''

           A__       =    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''					, output_loading_info=_SCREAMING_SNAKE_CASE)

           self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
           self.assertEqual(len(loading_info['''missing_keys'''])					, 0)

           model.to(_SCREAMING_SNAKE_CASE)
           A__       =    model(**self.dummy_input).sample

           assert image is not None, "Make sure output is not None"


    @unittest.skipIf(torch_device != '''cuda'''					, '''This test is supposed to run on GPU''')
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->str:





           '''simple docstring'''

           A__       =    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''					, output_loading_info=_SCREAMING_SNAKE_CASE)
           model.to(_SCREAMING_SNAKE_CASE)
           A__       =    model(**self.dummy_input).sample

           assert image is not None, "Make sure output is not None"


    @unittest.skipIf(torch_device != '''cuda'''					, '''This test is supposed to run on GPU''')
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Tuple:





           '''simple docstring'''

           A__       =    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''					, output_loading_info=_SCREAMING_SNAKE_CASE)
           model_accelerate.to(_SCREAMING_SNAKE_CASE)
           model_accelerate.eval()

           A__       =    torch.randn(
               1					, model_accelerate.config.in_channels					, model_accelerate.config.sample_size					, model_accelerate.config.sample_size					, generator=torch.manual_seed(0)					, )
           A__       =    noise.to(_SCREAMING_SNAKE_CASE)
           A__       =    torch.tensor([10] * noise.shape[0]).to(_SCREAMING_SNAKE_CASE)

           A__       =    model_accelerate(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE)["""sample"""]

           # two models don't need to stay in the device at the same time
           del model_accelerate
           torch.cuda.empty_cache()
           gc.collect()

           A__       =    UNetaDModel.from_pretrained(
               '''fusing/unet-ldm-dummy-update'''					, output_loading_info=_SCREAMING_SNAKE_CASE					, low_cpu_mem_usage=_SCREAMING_SNAKE_CASE)
           model_normal_load.to(_SCREAMING_SNAKE_CASE)
           model_normal_load.eval()
           A__       =    model_normal_load(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE)["""sample"""]

           assert torch_all_close(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, rtol=1e-3)





    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Dict:





           '''simple docstring'''

           A__       =    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''')
           model.eval()
           model.to(_SCREAMING_SNAKE_CASE)

           A__       =    torch.randn(
               1					, model.config.in_channels					, model.config.sample_size					, model.config.sample_size					, generator=torch.manual_seed(0)					, )
           A__       =    noise.to(_SCREAMING_SNAKE_CASE)
           A__       =    torch.tensor([10] * noise.shape[0]).to(_SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  A__       =    model(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE).sample

           A__       =    output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           A__       =    torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
           # fmt: on

           self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, rtol=1e-3))


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      UNetaDModel
    UpperCAmelCase__							  =      'sample'


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, UpperCAmelCase__      :	List[str]=(32, 32))						->str:





           '''simple docstring'''

           A__       =    4
           A__       =    3

           A__       =    floats_tensor((batch_size, num_channels) + sizes).to(_SCREAMING_SNAKE_CASE)
           A__       =    torch.tensor(batch_size * [10]).to(dtype=torch.intaa					, device=_SCREAMING_SNAKE_CASE)

           return {"sample": noise, "timestep": time_step}


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           return (3, 32, 32)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->str:





           '''simple docstring'''

           return (3, 32, 32)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->int:





           '''simple docstring'''

           A__       =    {
               """block_out_channels""": [32, 64, 64, 64],
               """in_channels""": 3,
               """layers_per_block""": 1,
               """out_channels""": 3,
               """time_embedding_type""": """fourier""",
               """norm_eps""": 1e-6,
               """mid_block_scale_factor""": math.sqrt(2.0),
               """norm_num_groups""": None,
               """down_block_types""": [
                   """SkipDownBlock2D""",
                   """AttnSkipDownBlock2D""",
                   """SkipDownBlock2D""",
                   """SkipDownBlock2D""",
               ],
               """up_block_types""": [
                   """SkipUpBlock2D""",
                   """SkipUpBlock2D""",
                   """AttnSkipUpBlock2D""",
                   """SkipUpBlock2D""",
               ],
           }
           A__       =    self.dummy_input
           return init_dict, inputs_dict


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Any:





           '''simple docstring'''

           A__       =    UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''					, output_loading_info=_SCREAMING_SNAKE_CASE)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
           self.assertEqual(len(loading_info['''missing_keys'''])					, 0)

           model.to(_SCREAMING_SNAKE_CASE)
           A__       =    self.dummy_input
           A__       =    floats_tensor((4, 3) + (256, 256)).to(_SCREAMING_SNAKE_CASE)
           A__       =    noise
           A__       =    model(**_SCREAMING_SNAKE_CASE)

           assert image is not None, "Make sure output is not None"


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Tuple:





           '''simple docstring'''

           A__       =    UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''')
           model.to(_SCREAMING_SNAKE_CASE)

           A__       =    4
           A__       =    3
           A__       =    (256, 256)

           A__       =    torch.ones((batch_size, num_channels) + sizes).to(_SCREAMING_SNAKE_CASE)
           A__       =    torch.tensor(batch_size * [1e-4]).to(_SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  A__       =    model(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE).sample

           A__       =    output[0, -3:, -3:, -1].flatten().cpu()
           # fmt: off
           A__       =    torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10_980.7_129, -20_028.8_535, 8148.2822, 2342.2905, 567.7608])
           # fmt: on

           self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, rtol=1e-2))


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Dict:





           '''simple docstring'''

           A__       =    UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update''')
           model.to(_SCREAMING_SNAKE_CASE)

           A__       =    4
           A__       =    3
           A__       =    (32, 32)

           A__       =    torch.ones((batch_size, num_channels) + sizes).to(_SCREAMING_SNAKE_CASE)
           A__       =    torch.tensor(batch_size * [1e-4]).to(_SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  A__       =    model(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE).sample

           A__       =    output[0, -3:, -3:, -1].flatten().cpu()
           # fmt: off
           A__       =    torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
           # fmt: on

           self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, rtol=1e-2))





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





           '''simple docstring'''

           pass







"""simple docstring"""
from __future__ import annotations



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	data
    UpperCAmelCase	:					Node | None	=	None
    UpperCAmelCase	:					Node | None	=	None

def       _snake_case						(						UpperCamelCase						:		Node | None						):  # In Order traversal of the tree
 if tree:
  display(tree.left						)
  print(tree.data						)
  display(tree.right						)

def       _snake_case						(						UpperCamelCase						:		Node | None						):
 return 1 + max(depth_of_tree(tree.left						)			,		depth_of_tree(tree.right						)						) if tree else 0

def       _snake_case						(						UpperCamelCase						:		Node						):
 if not tree:
  return True
 if tree.left and tree.right:
  return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
 else:
  return not tree.left and not tree.right

def       _snake_case						(						):  # Main function for testing.
 UpperCAmelCase	:					int	=	Node(1						)
 UpperCAmelCase	:					Tuple	=	Node(2						)
 UpperCAmelCase	:					Any	=	Node(3						)
 UpperCAmelCase	:					Optional[int]	=	Node(4						)
 UpperCAmelCase	:					Any	=	Node(5						)
 UpperCAmelCase	:					Optional[int]	=	Node(6						)
 UpperCAmelCase	:					int	=	Node(7						)
 UpperCAmelCase	:					str	=	Node(8						)
 UpperCAmelCase	:					str	=	Node(9						)

 print(is_full_binary_tree(UpperCamelCase						)						)
 print(depth_of_tree(UpperCamelCase						)						)
 print("""Tree is: """						)
 display(UpperCamelCase						)


if __name__ == "__main__":
      main()
