



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__			= logging.get_logger(__name__)

enable_full_determinism()






class    SCREAMING_SNAKE_CASE			(							__UpperCAmelCase			,      __UpperCAmelCase			,      unittest.TestCase					):
							__lowerCamelCase	:	Dict    =UNetaDModel
							__lowerCamelCase	:	Union[str, Any]    ="sample"





							@property
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		4
											__a       =		3
											__a       =		(32, 32)

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

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





							@property
							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											return (3, 32, 32)





							@property
							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											return (3, 32, 32)







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''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    SCREAMING_SNAKE_CASE			(							__UpperCAmelCase			,      __UpperCAmelCase			,      unittest.TestCase					):
							__lowerCamelCase	:	Dict    =UNetaDModel
							__lowerCamelCase	:	str    ="sample"





							@property
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		4
											__a       =		4
											__a       =		(32, 32)

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

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





							@property
							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											return (4, 32, 32)





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											return (4, 32, 32)





							def 						UpperCamelCase_					(   self		:		int	):

											'''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 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a     ,					__a       =		UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,				output_loading_info=_lowerCamelCase	)

											self.assertIsNotNone(_lowerCamelCase	)
											self.assertEqual(len(loading_info["""missing_keys"""]	)      ,				0	)

											model.to(_lowerCamelCase	)
											__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 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a     ,					__a       =		UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,				output_loading_info=_lowerCamelCase	)
											model.to(_lowerCamelCase	)
											__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 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
											__a     ,					__a       =		UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,				output_loading_info=_lowerCamelCase	)
											model_accelerate.to(_lowerCamelCase	)
											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(_lowerCamelCase	)
											__a       =		torch.tensor([10] * noise.shape[0]	).to(_lowerCamelCase	)

											__a       =		model_accelerate(_lowerCamelCase      ,				_lowerCamelCase	)["""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     ,					__a       =		UNetaDModel.from_pretrained(
											    """fusing/unet-ldm-dummy-update"""      ,				output_loading_info=_lowerCamelCase      ,				low_cpu_mem_usage=_lowerCamelCase	)
											model_normal_load.to(_lowerCamelCase	)
											model_normal_load.eval()
											__a       =		model_normal_load(_lowerCamelCase      ,				_lowerCamelCase	)["""sample"""]

											assert torch_all_close(_lowerCamelCase      ,				_lowerCamelCase      ,				rtol=1E-3	)







							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""	)
											model.eval()
											model.to(_lowerCamelCase	)

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

											with torch.no_grad():
															__a       =		model(_lowerCamelCase      ,				_lowerCamelCase	).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(_lowerCamelCase      ,				_lowerCamelCase      ,				rtol=1E-3	)	)






class    SCREAMING_SNAKE_CASE			(							__UpperCAmelCase			,      __UpperCAmelCase			,      unittest.TestCase					):
							__lowerCamelCase	:	List[Any]    =UNetaDModel
							__lowerCamelCase	:	str    ="sample"





							@property
							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Union[str, Any]=(32, 32)	):

											'''simple docstring'''
											__a       =		4
											__a       =		3

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

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





							@property
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											return (3, 32, 32)





							@property
							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											return (3, 32, 32)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''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 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a     ,					__a       =		UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""      ,				output_loading_info=_lowerCamelCase	)
											self.assertIsNotNone(_lowerCamelCase	)
											self.assertEqual(len(loading_info["""missing_keys"""]	)      ,				0	)

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

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





							@slow
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""	)
											model.to(_lowerCamelCase	)

											__a       =		4
											__a       =		3
											__a       =		(256, 256)

											__a       =		torch.ones((batch_size, num_channels) + sizes	).to(_lowerCamelCase	)
											__a       =		torch.tensor(batch_size * [1E-4]	).to(_lowerCamelCase	)

											with torch.no_grad():
															__a       =		model(_lowerCamelCase      ,				_lowerCamelCase	).sample

											__a       =		output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											__a       =		torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]	)
											# fmt: on

											self.assertTrue(torch_all_close(_lowerCamelCase      ,				_lowerCamelCase      ,				rtol=1E-2	)	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""	)
											model.to(_lowerCamelCase	)

											__a       =		4
											__a       =		3
											__a       =		(32, 32)

											__a       =		torch.ones((batch_size, num_channels) + sizes	).to(_lowerCamelCase	)
											__a       =		torch.tensor(batch_size * [1E-4]	).to(_lowerCamelCase	)

											with torch.no_grad():
															__a       =		model(_lowerCamelCase      ,				_lowerCamelCase	).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(_lowerCamelCase      ,				_lowerCamelCase      ,				rtol=1E-2	)	)







							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											# not required for this model
											pass





from __future__ import annotations

from collections import Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
