
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 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		"""hf-internal-testing/tiny-stable-diffusion-pipe"""
  lowerCAmelCase_								=		1
  lowerCAmelCase_								=		77
  lowerCAmelCase_								=		32
  lowerCAmelCase_								=		4
  lowerCAmelCase_								=		64
  lowerCAmelCase_								=		torch.manual_seed(0					)

  def 					lowercase_						(					self						,    __UpperCamelCase=0.9999					):
      A_								=					UNetaDConditionModel.from_pretrained(self.model_id						,    subfolder="unet"					)
      A_								=					unet.to(__UpperCamelCase					)
      A_								=					EMAModel(unet.parameters()						,    decay=__UpperCamelCase						,    model_cls=__UpperCamelCase						,    model_config=unet.config					)
      return unet, ema_unet

  def 					lowercase_						(					self					):
      A_								=					torch.randn(
          self.batch_size						,    self.num_in_channels						,    self.latent_height						,    self.latent_width						,    generator=self.generator					).to(__UpperCamelCase					)
      A_								=					torch.randint(0						,    1000						,    size=(self.batch_size,)						,    generator=self.generator					).to(__UpperCamelCase					)
      A_								=					torch.randn(
          self.batch_size						,    self.prompt_length						,    self.text_encoder_hidden_dim						,    generator=self.generator					).to(__UpperCamelCase					)
      return noisy_latents, timesteps, encoder_hidden_states

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					{}
      for k, param in unet.state_dict().items():
          A_								=					torch.randn_like(__UpperCamelCase					) + (param * torch.randn_like(__UpperCamelCase					))
          updated_state_dict.update({k: updated_param}					)
      unet.load_state_dict(__UpperCamelCase					)
      return unet

  def 					lowercase_						(					self					):
      A_	,	A_								=					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 					lowercase_						(					self					):
      A_	,	A_								=					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()					)
      A_								=					list(unet.parameters()					)
      for s_param, param in zip(ema_unet.shadow_params						,    __UpperCamelCase					):
          assert torch.allclose(__UpperCamelCase						,    __UpperCamelCase					)

      # 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						,    __UpperCamelCase					):
          assert torch.allclose(__UpperCamelCase						,    __UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_	,	A_								=					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.
      A_								=					self.simulate_backprop(__UpperCamelCase					)

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

      # 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						,    __UpperCamelCase					):
          assert ~torch.allclose(__UpperCamelCase						,    __UpperCamelCase					)

  def 					lowercase_						(					self					):
      # If we call EMA step after a backpropagation consecutively for two times,
      # the shadow params from those two steps should be different.
      A_	,	A_								=					self.get_models()

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

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

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

  def 					lowercase_						(					self					):
      # 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.
      A_	,	A_								=					self.get_models(decay=0.0					)
      A_								=					self.simulate_backprop(__UpperCamelCase					)
      ema_unet.step(unet_step_one.parameters()					)
      A_								=					ema_unet.shadow_params

      A_								=					self.simulate_backprop(__UpperCamelCase					)
      ema_unet.step(unet_step_two.parameters()					)
      A_								=					ema_unet.shadow_params

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

  @skip_mps
  def 					lowercase_						(					self					):
      A_	,	A_								=					self.get_models()
      A_	,	A_	,	A_								=					self.get_dummy_inputs()

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

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

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








'''simple docstring'''







class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase=None   ):
								_SCREAMING_SNAKE_CASE					=			data
								_SCREAMING_SNAKE_CASE					=			None




		def __repr__(					self   ):
								_SCREAMING_SNAKE_CASE					=			""
								_SCREAMING_SNAKE_CASE					=			self
								while temp:
														string_rep += F'<{temp.data}> ---> '
														_SCREAMING_SNAKE_CASE					=			temp.next
								string_rep += "<END>"
								return string_rep



def       _a		(						_SCREAMING_SNAKE_CASE		: Any	):

						# if elements_list is empty
						if not elements_list:
												raise Exception("The Elements List is empty"	)

						# Set first element as Head
						_SCREAMING_SNAKE_CASE					=			Node(elements_list[0]	)
						_SCREAMING_SNAKE_CASE					=			head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_SCREAMING_SNAKE_CASE					=			Node(_SCREAMING_SNAKE_CASE	)
												_SCREAMING_SNAKE_CASE					=			current.next
						return head


_snake_case					: List[Any]     			=					[1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
_snake_case					: List[Any]     			=					make_linked_list(list_data)
print("""Linked List:""")




print(linked_list)