



"""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    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   "hf-internal-testing/tiny-stable-diffusion-pipe"
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   1
	SCREAMING_SNAKE_CASE_				:    Union[str, Any]		    =   7_7
	SCREAMING_SNAKE_CASE_				:    Dict		    =   3_2
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   4
	SCREAMING_SNAKE_CASE_				:    Union[str, Any]		    =   6_4
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   torch.manual_seed(0    )






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_=0.9999					):
			_lowercase							:      str			=					UNetaDConditionModel.from_pretrained(self.model_id							,subfolder="""unet"""					)
			_lowercase							:      List[Any]			=					unet.to(UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					EMAModel(unet.parameters()							,decay=UpperCAmelCase_							,model_cls=UpperCAmelCase_							,model_config=unet.config					)
			return unet, ema_unet






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					torch.randn(
			    self.batch_size							,self.num_in_channels							,self.latent_height							,self.latent_width							,generator=self.generator					).to(UpperCAmelCase_					)
			_lowercase							:      Tuple			=					torch.randint(0							,10_00							,size=(self.batch_size,)							,generator=self.generator					).to(UpperCAmelCase_					)
			_lowercase							:      Tuple			=					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 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase							:      int			=					{}
			for k, param in unet.state_dict().items():
					_lowercase							:      Any			=					torch.randn_like(UpperCAmelCase_					) + (param * torch.randn_like(UpperCAmelCase_					))
					updated_state_dict.update({k: updated_param}					)
			unet.load_state_dict(UpperCAmelCase_					)
			return unet






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      List[str]			=					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 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Tuple			=					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							:      Any			=					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 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Tuple			=					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							:      Any			=					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.
			_lowercase							:      Tuple			=					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 					lowerCamelCase__ (			self					):
			# If we call EMA step after a backpropagation consecutively for two times,
			# the shadow params from those two steps should be different.
			_lowercase			,   _lowercase							:      Any			=					self.get_models()

			# First backprop + EMA
			_lowercase							:      Tuple			=					self.simulate_backprop(UpperCAmelCase_					)
			ema_unet.step(unet_step_one.parameters()					)
			_lowercase							:      List[Any]			=					ema_unet.shadow_params

			# Second backprop + EMA
			_lowercase							:      int			=					self.simulate_backprop(UpperCAmelCase_					)
			ema_unet.step(unet_step_two.parameters()					)
			_lowercase							:      Any			=					ema_unet.shadow_params

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






	def 					lowerCamelCase__ (			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.
			_lowercase			,   _lowercase							:      Dict			=					self.get_models(decay=0.0					)
			_lowercase							:      str			=					self.simulate_backprop(UpperCAmelCase_					)
			ema_unet.step(unet_step_one.parameters()					)
			_lowercase							:      int			=					ema_unet.shadow_params

			_lowercase							:      Any			=					self.simulate_backprop(UpperCAmelCase_					)
			ema_unet.step(unet_step_two.parameters()					)
			_lowercase							:      Optional[Any]			=					ema_unet.shadow_params

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






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Union[str, Any]			=					self.get_models()
			_lowercase			,   _lowercase			,   _lowercase							:      Any			=					self.get_dummy_inputs()

			with tempfile.TemporaryDirectory() as tmpdir:
					ema_unet.save_pretrained(UpperCAmelCase_					)
					_lowercase							:      List[str]			=					UNetaDConditionModel.from_pretrained(UpperCAmelCase_							,model_cls=UpperCAmelCase_					)
					_lowercase							:      List[str]			=					loaded_unet.to(unet.device					)

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

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







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_  :	Tuple							  =							logging.get_logger(__name__)

snake_case_  :	int							  =							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :	Dict							  =							{
    '''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   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Optional[Any] ,			_a    :					Tuple=None ,			_a    :					Union[str, Any]=None ,			_a    :					Dict=False ,			_a    :					int=True ,			_a    :					str=False ,			_a    :					int="<s>" ,			_a    :					int="</s>" ,			_a    :					Optional[int]="<unk>" ,			_a    :					Dict="<sep>" ,			_a    :					Optional[int]="<pad>" ,			_a    :					str="<cls>" ,			_a    :					List[Any]="<mask>" ,			_a    :					Optional[Any]=["<eop>", "<eod>"] ,			**_a    :					Any ,			)							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =AddedToken(_a ,			lstrip=_a ,			rstrip=_a     ) if isinstance(_a ,			_a     ) else mask_token

										super().__init__(
										    vocab_file=_a ,			tokenizer_file=_a ,			do_lower_case=_a ,			remove_space=_a ,			keep_accents=_a ,			bos_token=_a ,			eos_token=_a ,			unk_token=_a ,			sep_token=_a ,			pad_token=_a ,			cls_token=_a ,			mask_token=_a ,			additional_special_tokens=_a ,			**_a ,			)

										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =do_lower_case
										_SCREAMING_SNAKE_CASE	     =remove_space
										_SCREAMING_SNAKE_CASE	     =keep_accents
										_SCREAMING_SNAKE_CASE	     =vocab_file
										_SCREAMING_SNAKE_CASE	     =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.'''     )
										_SCREAMING_SNAKE_CASE	     =jieba
										_SCREAMING_SNAKE_CASE	     =str.maketrans(''' \n''' ,			'''\u2582\u2583'''     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[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 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[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 							__UpperCamelCase (				self    :					str ,			_a    :					str ,			_a    :					Optional[str] = None     )							->   Tuple[str]:


										"""simple docstring"""





										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(_a     ):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
														return
										_SCREAMING_SNAKE_CASE	     =os.path.join(
										    _a ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(_a     ):
														copyfile(self.vocab_file ,			_a     )

										return (out_vocab_file,)




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					str ,			*_a    :					Optional[int] ,			**_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[
										    ''' '''.join([x.translate(self.translator     ) for x in self.jieba.cut(_a ,			cut_all=_a     )]     )
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(_a ,			*_a ,			**_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			*_a    :					Union[str, Any] ,			**_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =super()._decode(*_a ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =text.replace(''' ''' ,			''''''     ).replace('''\u2582''' ,			''' '''     ).replace('''\u2583''' ,			'''\n'''     )
										return text