
"""simple docstring"""






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 				__a    (   unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: Any					      =      """hf-internal-testing/tiny-stable-diffusion-pipe"""
							_lowerCamelCase				: Tuple					      =      1
							_lowerCamelCase				: List[Any]					      =      77
							_lowerCamelCase				: List[Any]					      =      32
							_lowerCamelCase				: int					      =      4
							_lowerCamelCase				: List[Any]					      =      64
							_lowerCamelCase				: List[str]					      =      torch.manual_seed(0					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase=0.9999					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					UNetaDConditionModel.from_pretrained(self.model_id     ,						subfolder="unet"					)
										__lowercase						      =					unet.to(_lowerCamelCase					)
										__lowercase						      =					EMAModel(unet.parameters()     ,						decay=_lowerCamelCase     ,						model_cls=_lowerCamelCase     ,						model_config=unet.config					)
										return unet, ema_unet
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					torch.randn(
										    self.batch_size     ,						self.num_in_channels     ,						self.latent_height     ,						self.latent_width     ,						generator=self.generator					).to(_lowerCamelCase					)
										__lowercase						      =					torch.randint(0     ,						1_000     ,						size=(self.batch_size,)     ,						generator=self.generator					).to(_lowerCamelCase					)
										__lowercase						      =					torch.randn(
										    self.batch_size     ,						self.prompt_length     ,						self.text_encoder_hidden_dim     ,						generator=self.generator					).to(_lowerCamelCase					)
										return noisy_latents, timesteps, encoder_hidden_states
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					{}
										for k, param in unet.state_dict().items():
													__lowercase						      =					torch.randn_like(_lowerCamelCase					) + (param * torch.randn_like(_lowerCamelCase					))
													updated_state_dict.update({k: updated_param}					)
										unet.load_state_dict(_lowerCamelCase					)
										return unet
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase      ,				__lowercase						      =					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      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase      ,				__lowercase						      =					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()					)
										__lowercase						      =					list(unet.parameters()					)
										for s_param, param in zip(ema_unet.shadow_params     ,						_lowerCamelCase					):
													assert torch.allclose(_lowerCamelCase     ,						_lowerCamelCase					)

										# 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     ,						_lowerCamelCase					):
													assert torch.allclose(_lowerCamelCase     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										__lowercase      ,				__lowercase						      =					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.
										__lowercase						      =					self.simulate_backprop(_lowerCamelCase					)

										# 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.
										__lowercase						      =					list(unet_pseudo_updated_step_one.parameters()					)
										for s_param, param in zip(ema_unet.shadow_params     ,						_lowerCamelCase					):
													assert ~torch.allclose(_lowerCamelCase     ,						_lowerCamelCase					)

										# 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     ,						_lowerCamelCase					):
													assert ~torch.allclose(_lowerCamelCase     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										# If we call EMA step after a backpropagation consecutively for two times,
										# the shadow params from those two steps should be different.
										__lowercase      ,				__lowercase						      =					self.get_models()

										# First backprop + EMA
										__lowercase						      =					self.simulate_backprop(_lowerCamelCase					)
										ema_unet.step(unet_step_one.parameters()					)
										__lowercase						      =					ema_unet.shadow_params

										# Second backprop + EMA
										__lowercase						      =					self.simulate_backprop(_lowerCamelCase					)
										ema_unet.step(unet_step_two.parameters()					)
										__lowercase						      =					ema_unet.shadow_params

										for step_one, step_two in zip(_lowerCamelCase     ,						_lowerCamelCase					):
													assert ~torch.allclose(_lowerCamelCase     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										# If there's no decay even if there are backprops, EMA steps
										# won't take any effect i.e., the shadow params would remain the
										# same.
										__lowercase      ,				__lowercase						      =					self.get_models(decay=0.0					)
										__lowercase						      =					self.simulate_backprop(_lowerCamelCase					)
										ema_unet.step(unet_step_one.parameters()					)
										__lowercase						      =					ema_unet.shadow_params

										__lowercase						      =					self.simulate_backprop(_lowerCamelCase					)
										ema_unet.step(unet_step_two.parameters()					)
										__lowercase						      =					ema_unet.shadow_params

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





							@skip_mps
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase      ,				__lowercase						      =					self.get_models()
										__lowercase      ,				__lowercase      ,				__lowercase						      =					self.get_dummy_inputs()

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

										# Since no EMA step has been performed the outputs should match.
										__lowercase						      =					unet(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					).sample
										__lowercase						      =					loaded_unet(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					).sample

										assert torch.allclose(_lowerCamelCase     ,						_lowerCamelCase     ,						atol=1e-4					)







"""simple docstring"""






class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					data
										__lowercase						      =					None
										__lowercase						      =					None





							def __str__(    self					) ->       str:




										'''simple docstring'''
										return f'''{self.data}'''





class 				__a    :



							'''simple docstring'''







							def __init__(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					None
										__lowercase						      =					None
							def __iter__(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.head
										while node:
													yield node.data
													__lowercase						      =					node.next
							def __str__(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										return "->".join([str(_lowerCamelCase					) for item in self]					)
							def __len__(    self					) ->       Dict:




										'''simple docstring'''
										return sum(1 for _ in self					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Union[str, Any]:




										'''simple docstring'''
										self.insert_at_nth(0     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       List[str]:




										'''simple docstring'''
										self.insert_at_nth(len(self					)     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length:
													raise IndexError("list index out of range"					)
										__lowercase						      =					Node(_lowerCamelCase					)
										if self.head is None:
													__lowercase						      =					__lowercase						      =					new_node
										elif index == 0:
													__lowercase						      =					new_node
													__lowercase						      =					self.head
													__lowercase						      =					new_node
										elif index == length:
													__lowercase						      =					new_node
													__lowercase						      =					self.tail
													__lowercase						      =					new_node
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					new_node
													__lowercase						      =					temp.previous
													__lowercase						      =					temp
													__lowercase						      =					new_node
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										return self.delete_at_nth(0					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										return self.delete_at_nth(len(self					) - 1					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length - 1:
													raise IndexError("list index out of range"					)
										__lowercase						      =					self.head  # default first node
										if length == 1:
													__lowercase						      =					__lowercase						      =					None
										elif index == 0:
													__lowercase						      =					self.head.next
													__lowercase						      =					None
										elif index == length - 1:
													__lowercase						      =					self.tail
													__lowercase						      =					self.tail.previous
													__lowercase						      =					None
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					temp
													__lowercase						      =					temp.previous
													__lowercase						      =					temp.next
										return delete_node.data
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.head

										while current.data != data:  # Find the position to delete
													if current.next:
																__lowercase						      =					current.next
													else:  # We have reached the end an no value matches
																raise ValueError("No data matching given value"					)

										if current == self.head:
													self.delete_head()

										elif current == self.tail:
													self.delete_tail()

										else:  # Before: 1 <--> 2(current) <--> 3
													__lowercase						      =					current.next  # 1 --> 3
													__lowercase						      =					current.previous  # 1 <--> 3
										return data





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




										'''simple docstring'''
										return len(self					) == 0





def     lowerCAmelCase__ (		)	->None:
			__lowercase						      =					DoublyLinkedList()
			assert linked_list.is_empty() is True
			assert str(__magic_name__			) == ""

			try:
						linked_list.delete_head()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			try:
						linked_list.delete_tail()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			for i in range(1_0			):
						assert len(__magic_name__			) == i
						linked_list.insert_at_nth(__magic_name__ ,      i + 1			)
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(1 ,      1_1			)			)

			linked_list.insert_at_head(0			)
			linked_list.insert_at_tail(1_1			)
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(0 ,      1_2			)			)

			assert linked_list.delete_head() == 0
			assert linked_list.delete_at_nth(9			) == 1_0
			assert linked_list.delete_tail() == 1_1
			assert len(__magic_name__			) == 9
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(1 ,      1_0			)			)


if __name__ == "__main__":
					from doctest import testmod

					testmod()





