code: 
  
  """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     __UpperCamelCase				(     unittest.TestCase		):
  		SCREAMING_SNAKE_CASE              =					"hf-internal-testing/tiny-stable-diffusion-pipe"
  		SCREAMING_SNAKE_CASE              =					1
  		SCREAMING_SNAKE_CASE              =					77
  		SCREAMING_SNAKE_CASE              =					32
  		SCREAMING_SNAKE_CASE              =					4
  		SCREAMING_SNAKE_CASE              =					SCREAMING_SNAKE_CASE              =					64
  		SCREAMING_SNAKE_CASE              =					torch.manual_seed(0		)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=0.9_9_9_9):
  							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       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
  							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  ,  1_0_0_0  ,  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       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any):
  							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       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							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       SCREAMING_SNAKE_CASE__     (self				:	int):
  							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       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
  							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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							# 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       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							# 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       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							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)
  
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import os
  
  from ...utils import logging
  from .utils import DataProcessor, InputExample
  
  
  __A  :					List[Any]												=	logging.get_logger(__name__)
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Any=None):
  							A    =      language
  							A    =      train_language
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self.language if self.train_language is None else self.train_language
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      F"""train-{i}"""
  												A    =      line[0]
  												A    =      line[1]
  												A    =      "contradiction" if line[2] == "contradictory" else line[2]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  "XNLI-1.0/xnli.test.tsv"))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      line[0]
  												if language != self.language:
  																	continue
  												A    =      F"""test-{i}"""
  												A    =      line[6]
  												A    =      line[7]
  												A    =      line[1]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							return ["contradiction", "entailment", "neutral"]
  
  __A  :					Optional[int]												=	{
      'xnli': XnliProcessor,
  }
  
  __A  :					Union[str, Any]												=	{
      'xnli': 'classification',
  }
  
  __A  :					Tuple												=	{
      'xnli': 3,
  }
  
  
  
  
  
  