import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()







class   __lowercase						(unittest.TestCase     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	"""hf-internal-testing/tiny-stable-diffusion-pipe"""
							_UpperCAmelCase						     =	1
							_UpperCAmelCase						     =	7_7
							_UpperCAmelCase						     =	3_2
							_UpperCAmelCase						     =	4
							_UpperCAmelCase						     =	_UpperCAmelCase						     =	6_4
							_UpperCAmelCase						     =	torch.manual_seed(0     )






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__=0.9_999					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				UNetaDConditionModel.from_pretrained(self.model_id ,	subfolder='unet'					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				unet.to(__snake_case					)
										SCREAMING_SNAKE_CASE_		:    str 		=				EMAModel(unet.parameters() ,	decay=__snake_case ,	model_cls=__snake_case ,	model_config=unet.config					)
										return unet, ema_unet






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				torch.randn(
										    self.batch_size ,	self.num_in_channels ,	self.latent_height ,	self.latent_width ,	generator=self.generator					).to(__snake_case					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				torch.randint(0 ,	1_0_0_0 ,	size=(self.batch_size,) ,	generator=self.generator					).to(__snake_case					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				torch.randn(
										    self.batch_size ,	self.prompt_length ,	self.text_encoder_hidden_dim ,	generator=self.generator					).to(__snake_case					)
										return noisy_latents, timesteps, encoder_hidden_states






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				{}
										for k, param in unet.state_dict().items():
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				torch.randn_like(__snake_case					) + (param * torch.randn_like(__snake_case					))
													updated_state_dict.update({k: updated_param}					)
										unet.load_state_dict(__snake_case					)
										return unet






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Any 		=				self.get_models()
										# Take the first (hypothetical) EMA step.
										ema_unet.step(unet.parameters()					)
										assert ema_unet.optimization_step == 1

										# Take two more.
										for _ in range(2					):
													ema_unet.step(unet.parameters()					)
										assert ema_unet.optimization_step == 3






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.get_models()
										# Since the `unet` is not being updated (i.e., backprop'd)
										# there won't be any difference between the `params` of `unet`
										# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
										ema_unet.step(unet.parameters()					)
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				list(unet.parameters()					)
										for s_param, param in zip(ema_unet.shadow_params ,	__snake_case					):
													assert torch.allclose(__snake_case ,	__snake_case					)

										# The above holds true even if we call `ema.step()` multiple times since
										# `unet` params are still not being updated.
										for _ in range(4					):
													ema_unet.step(unet.parameters()					)
										for s_param, param in zip(ema_unet.shadow_params ,	__snake_case					):
													assert torch.allclose(__snake_case ,	__snake_case					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Any 		=				self.get_models()
										# Here we simulate the parameter updates for `unet`. Since there might
										# be some parameters which are initialized to zero we take extra care to
										# initialize their values to something non-zero before the multiplication.
										SCREAMING_SNAKE_CASE_		:    Any 		=				self.simulate_backprop(__snake_case					)

										# Take the EMA step.
										ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

										# Now the EMA'd parameters won't be equal to the original model parameters.
										SCREAMING_SNAKE_CASE_		:    int 		=				list(unet_pseudo_updated_step_one.parameters()					)
										for s_param, param in zip(ema_unet.shadow_params ,	__snake_case					):
													assert ~torch.allclose(__snake_case ,	__snake_case					)

										# Ensure this is the case when we take multiple EMA steps.
										for _ in range(4					):
													ema_unet.step(unet.parameters()					)
										for s_param, param in zip(ema_unet.shadow_params ,	__snake_case					):
													assert ~torch.allclose(__snake_case ,	__snake_case					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.get_models()

										# First backprop + EMA
										SCREAMING_SNAKE_CASE_		:    str 		=				self.simulate_backprop(__snake_case					)
										ema_unet.step(unet_step_one.parameters()					)
										SCREAMING_SNAKE_CASE_		:    str 		=				ema_unet.shadow_params

										# Second backprop + EMA
										SCREAMING_SNAKE_CASE_		:    Any 		=				self.simulate_backprop(__snake_case					)
										ema_unet.step(unet_step_two.parameters()					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				ema_unet.shadow_params

										for step_one, step_two in zip(__snake_case ,	__snake_case					):
													assert ~torch.allclose(__snake_case ,	__snake_case					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.get_models(decay=0.0					)
										SCREAMING_SNAKE_CASE_		:    int 		=				self.simulate_backprop(__snake_case					)
										ema_unet.step(unet_step_one.parameters()					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				ema_unet.shadow_params

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.simulate_backprop(__snake_case					)
										ema_unet.step(unet_step_two.parameters()					)
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				ema_unet.shadow_params

										for step_one, step_two in zip(__snake_case ,	__snake_case					):
													assert torch.allclose(__snake_case ,	__snake_case					)






							@skip_mps
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    str 		=				self.get_models()
										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    int 		=				self.get_dummy_inputs()

										with tempfile.TemporaryDirectory() as tmpdir:
													ema_unet.save_pretrained(__snake_case					)
													SCREAMING_SNAKE_CASE_		:    Dict 		=				UNetaDConditionModel.from_pretrained(__snake_case ,	model_cls=__snake_case					)
													SCREAMING_SNAKE_CASE_		:    Dict 		=				loaded_unet.to(unet.device					)

										# Since no EMA step has been performed the outputs should match.
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				unet(__snake_case ,	__snake_case ,	__snake_case					).sample
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				loaded_unet(__snake_case ,	__snake_case ,	__snake_case					).sample

										assert torch.allclose(__snake_case ,	__snake_case ,	atol=1E-4					)









from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

lowerCAmelCase__ :			str         ={
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def 	a__ (							A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				{start}
			SCREAMING_SNAKE_CASE_		:    int 		=				[start]
			SCREAMING_SNAKE_CASE_		:    Queue 		=				Queue()
			queue.put(A__							)
			while not queue.empty():
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				queue.get()
						for w in graph[v]:
									if w not in explored:
												explored.add(A__							)
												result.append(A__							)
												queue.put(A__							)
			return result






def 	a__ (							A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Any 		=				{start}
			SCREAMING_SNAKE_CASE_		:    str 		=				[start]
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				deque([start]							)
			while queue:
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				queue.popleft()
						for child in graph[v]:
									if child not in visited:
												visited.add(A__							)
												result.append(A__							)
												queue.append(A__							)
			return result






def 	a__ (							A__							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				F'''from __main__ import G, {name}'''
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				1_0_0_0_0
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				timeit(F'''{name}(G, \'A\')''',			setup=A__,			number=A__							)
			print(F'''{name:<35} finished {number} runs in {res:.5f} seconds'''							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				benchmark_function('breadth_first_search')
				benchmark_function('breadth_first_search_with_deque')
				# breadth_first_search                finished 10000 runs in 0.20999 seconds
				# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





