code: import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  UpperCAmelCase					=				logging.get_logger(__name__)
  
  UpperCAmelCase					=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  UpperCAmelCase					=				{
      '''vocab_file''': {
          '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
      },
      '''tokenizer_file''': {
          '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
      },
  }
  
  
  
  
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Union[str, Any]       ,			lowerCamelCase       :		Optional[int]=None       ,			lowerCamelCase       :		Optional[int]=None       ,			lowerCamelCase       :		Union[str, Any]=False       ,			lowerCamelCase       :		Tuple=True       ,			lowerCamelCase       :		Optional[Any]=False       ,			lowerCamelCase       :		Dict="<s>"       ,			lowerCamelCase       :		List[str]="</s>"       ,			lowerCamelCase       :		Tuple="<unk>"       ,			lowerCamelCase       :		int="<sep>"       ,			lowerCamelCase       :		Any="<pad>"       ,			lowerCamelCase       :		Union[str, Any]="<cls>"       ,			lowerCamelCase       :		Tuple="<mask>"       ,			lowerCamelCase       :		str=["<eop>", "<eod>"]       ,			**lowerCamelCase       :		List[Any]       ,			) ->							Any:
          _UpperCamelCase     :       str  						=  AddedToken(lowerCamelCase       ,			lstrip=lowerCamelCase       ,			rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase       ,			lowerCamelCase  ) else mask_token
  
          super().__init__(
              vocab_file=lowerCamelCase       ,			tokenizer_file=lowerCamelCase       ,			do_lower_case=lowerCamelCase       ,			remove_space=lowerCamelCase       ,			keep_accents=lowerCamelCase       ,			bos_token=lowerCamelCase       ,			eos_token=lowerCamelCase       ,			unk_token=lowerCamelCase       ,			sep_token=lowerCamelCase       ,			pad_token=lowerCamelCase       ,			cls_token=lowerCamelCase       ,			mask_token=lowerCamelCase       ,			additional_special_tokens=lowerCamelCase       ,			**lowerCamelCase       ,			)
  
          _UpperCamelCase     :       str  						=  3
          _UpperCamelCase     :       List[str]  						=  do_lower_case
          _UpperCamelCase     :       int  						=  remove_space
          _UpperCamelCase     :       str  						=  keep_accents
          _UpperCamelCase     :       str  						=  vocab_file
          _UpperCamelCase     :       Any  						=  False if not self.vocab_file else True
  
          try:
                 import jieba
          except ModuleNotFoundError as error:
                 raise error.__class__(
                     'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
                     'See https://pypi.org/project/jieba/ for installation.'  )
          _UpperCamelCase     :       Union[str, Any]  						=  jieba
          _UpperCamelCase     :       int  						=  str.maketrans(' \n'       ,			'\u2582\u2583'  )
  
   def 					__snake_case					(			self       :		Optional[int]       ,			lowerCamelCase       :		List[int]       ,			lowerCamelCase       :		Optional[List[int]] = None  ) ->							List[int]:
          _UpperCamelCase     :       Tuple  						=  [self.sep_token_id]
          _UpperCamelCase     :       Tuple  						=  [self.cls_token_id]
          if token_ids_a is None:
                 return token_ids_a + sep + cls
          return token_ids_a + sep + token_ids_a + sep + cls
  
   def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		List[int]       ,			lowerCamelCase       :		Optional[List[int]] = None  ) ->							List[int]:
          _UpperCamelCase     :       Optional[Any]  						=  [self.sep_token_id]
          _UpperCamelCase     :       Optional[Any]  						=  [2]
  
          if token_ids_a is None:
                 return len(token_ids_a + sep  ) * [0] + cls_segment_id
          return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id
  
   def 					__snake_case					(			self       :		str       ,			lowerCamelCase       :		str       ,			lowerCamelCase       :		Optional[str] = None  ) ->							Tuple[str]:
          if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                     'tokenizer.'  )
  
          if not os.path.isdir(lowerCamelCase  ):
                 logger.error(f"Vocabulary path ({save_directory}) should be a directory"  )
                 return
          _UpperCamelCase     :       Any  						=  os.path.join(
              lowerCamelCase       ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
  
          if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowerCamelCase  ):
                 copyfile(self.vocab_file       ,			lowerCamelCase  )
  
          return (out_vocab_file,)
  
   def 					__snake_case					(			self       :		List[Any]       ,			lowerCamelCase       :		Dict       ,			*lowerCamelCase       :		List[str]       ,			**lowerCamelCase       :		Union[str, Any]  ) ->							List[Any]:
          _UpperCamelCase     :       List[str]  						=  [
              ' '.join([x.translate(self.translator  ) for x in self.jieba.cut(lowerCamelCase       ,			cut_all=lowerCamelCase  )]  )
              for text in batch_text_or_text_pairs
          ]
          return super()._batch_encode_plus(lowerCamelCase       ,			*lowerCamelCase       ,			**lowerCamelCase  )
  
   def 					__snake_case					(			self       :		Tuple       ,			*lowerCamelCase       :		Tuple       ,			**lowerCamelCase       :		List[str]  ) ->							Union[str, Any]:
          _UpperCamelCase     :       Optional[int]  						=  super()._decode(*lowerCamelCase       ,			**lowerCamelCase  )
          _UpperCamelCase     :       Optional[int]  						=  text.replace(' '       ,			''  ).replace('\u2582'       ,			' '  ).replace('\u2583'       ,			'\n'  )
          return text
  
  
  
  
style_context: 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						:	str				    = 'hf-internal-testing/tiny-stable-diffusion-pipe'
   _lowerCamelCase						:	int				    = 1
   _lowerCamelCase						:	Any				    = 77
   _lowerCamelCase						:	List[Any]				    = 32
   _lowerCamelCase						:	List[str]				    = 4
   _lowerCamelCase						:	Dict				    = 64
   _lowerCamelCase						:	List[str]				    = torch.manual_seed(0						)
  
   def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		Union[str, Any]=0.9_9_9_9  ) ->							Dict:
          _UpperCamelCase     :       int  						=  UNetaDConditionModel.from_pretrained(self.model_id       ,			subfolder='unet'  )
          _UpperCamelCase     :       Any  						=  unet.to(lowerCamelCase  )
          _UpperCamelCase     :       Union[str, Any]  						=  EMAModel(unet.parameters()       ,			decay=lowerCamelCase       ,			model_cls=lowerCamelCase       ,			model_config=unet.config  )
          return unet, ema_unet
  
   def 					__snake_case					(			self       :		Tuple  ) ->							Union[str, Any]:
          _UpperCamelCase     :       Tuple  						=  torch.randn(
              self.batch_size       ,			self.num_in_channels       ,			self.latent_height       ,			self.latent_width       ,			generator=self.generator  ).to(lowerCamelCase  )
          _UpperCamelCase     :       Union[str, Any]  						=  torch.randint(0       ,			1_0_0_0       ,			size=(self.batch_size,)       ,			generator=self.generator  ).to(lowerCamelCase  )
          _UpperCamelCase     :       int  						=  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 					__snake_case					(			self       :		str       ,			lowerCamelCase       :		List[Any]  ) ->							List[Any]:
          _UpperCamelCase     :       int  						=  {}
          for k, param in unet.state_dict().items():
                 _UpperCamelCase     :       Optional[Any]  						=  torch.randn_like(lowerCamelCase  ) + (param * torch.randn_like(lowerCamelCase  ))
                 updated_state_dict.update({k: updated_param}  )
          unet.load_state_dict(lowerCamelCase  )
          return unet
  
   def 					__snake_case					(			self       :		List[Any]  ) ->							Tuple:
          _UpperCamelCase						,      _UpperCamelCase     :       Union[str, 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 					__snake_case					(			self       :		int  ) ->							Any:
          _UpperCamelCase						,      _UpperCamelCase     :       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()  )
          _UpperCamelCase     :       List[Any]  						=  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 					__snake_case					(			self       :		Dict  ) ->							Dict:
          _UpperCamelCase						,      _UpperCamelCase     :       Dict  						=  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.
          _UpperCamelCase     :       str  						=  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.
          _UpperCamelCase     :       Optional[Any]  						=  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 					__snake_case					(			self       :		Union[str, Any]  ) ->							Tuple:
          # If we call EMA step after a backpropagation consecutively for two times,
          # the shadow params from those two steps should be different.
          _UpperCamelCase						,      _UpperCamelCase     :       Dict  						=  self.get_models()
  
          # First backprop + EMA
          _UpperCamelCase     :       Optional[Any]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_one.parameters()  )
          _UpperCamelCase     :       Optional[Any]  						=  ema_unet.shadow_params
  
          # Second backprop + EMA
          _UpperCamelCase     :       Optional[int]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_two.parameters()  )
          _UpperCamelCase     :       List[str]  						=  ema_unet.shadow_params
  
          for step_one, step_two in zip(lowerCamelCase       ,			lowerCamelCase  ):
                 assert ~torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
   def 					__snake_case					(			self       :		str  ) ->							int:
          # 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.
          _UpperCamelCase						,      _UpperCamelCase     :       List[str]  						=  self.get_models(decay=0.0  )
          _UpperCamelCase     :       Optional[Any]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_one.parameters()  )
          _UpperCamelCase     :       Dict  						=  ema_unet.shadow_params
  
          _UpperCamelCase     :       List[str]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_two.parameters()  )
          _UpperCamelCase     :       Union[str, Any]  						=  ema_unet.shadow_params
  
          for step_one, step_two in zip(lowerCamelCase       ,			lowerCamelCase  ):
                 assert torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
   @skip_mps
   def 					__snake_case					(			self       :		Dict  ) ->							str:
          _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  self.get_models()
          _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Any  						=  self.get_dummy_inputs()
  
          with tempfile.TemporaryDirectory() as tmpdir:
                 ema_unet.save_pretrained(lowerCamelCase  )
                 _UpperCamelCase     :       List[str]  						=  UNetaDConditionModel.from_pretrained(lowerCamelCase       ,			model_cls=lowerCamelCase  )
                 _UpperCamelCase     :       Dict  						=  loaded_unet.to(unet.device  )
  
          # Since no EMA step has been performed the outputs should match.
          _UpperCamelCase     :       Optional[Any]  						=  unet(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  ).sample
          _UpperCamelCase     :       List[str]  						=  loaded_unet(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  ).sample
  
          assert torch.allclose(lowerCamelCase       ,			lowerCamelCase       ,			atol=1e-4  )
  
  
  
  