


'''simple docstring'''







import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


A__:       Optional[int]          =			logging.get_logger(__name__)

A__:       Tuple          =			{'''vocab_file''': '''spiece.model'''}

A__:       Optional[Any]          =			{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}







class A__			(		UpperCAmelCase__ ):






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




             '''simple docstring'''
             _a       :					Dict	   =AddedToken(_lowerCAmelCase	,		lstrip=_lowerCAmelCase	,		rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase	,		_lowerCAmelCase      ) else mask_token

             _a       :					List[str]	   ={} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 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	,		sp_model_kwargs=self.sp_model_kwargs	,		**_lowerCAmelCase	,		)

             _a       :					List[str]	   =3

             _a       :					Dict	   =do_lower_case
             _a       :					Union[str, Any]	   =remove_space
             _a       :					str	   =keep_accents
             _a       :					List[Any]	   =vocab_file

             _a       :					Dict	   =spm.SentencePieceProcessor(**self.sp_model_kwargs      )
             self.sp_model.Load(_lowerCAmelCase      )

             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."""      )
             _a       :					Tuple	   =jieba
             _a       :					Dict	   =str.maketrans(""" \n"""	,		"""\u2582\u2583"""      )






      @property
      # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
      def __UpperCAmelCase			(					self     :List[str]      )			->   Optional[int]:




             '''simple docstring'''
             return len(self.sp_model      )






      def __UpperCAmelCase			(					self     :Optional[Any]      )			->   Optional[int]:




             '''simple docstring'''
             _a       :					Tuple	   ={self.convert_ids_to_tokens(_lowerCAmelCase      ): i for i in range(self.vocab_size      )}
             vocab.update(self.added_tokens_encoder      )
             return vocab






      def __getstate__(					self     :str      )			->   Tuple:




             '''simple docstring'''
             _a       :					List[str]	   =self.__dict__.copy()
             _a       :					Dict	   =None
             return state






      def __setstate__(					self     :Optional[Any]	,		SCREAMING_SNAKE_CASE     :int      )			->   Dict:




             '''simple docstring'''
             _a       :					Tuple	   =d

             # for backward compatibility
             if not hasattr(self	,		"""sp_model_kwargs"""      ):
                    _a       :					Dict	   ={}

             _a       :					Union[str, Any]	   =spm.SentencePieceProcessor(**self.sp_model_kwargs      )
             self.sp_model.Load(self.vocab_file      )






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




             '''simple docstring'''
             if self.remove_space:
                    _a       :					Optional[Any]	   =""" """.join(inputs.strip().split()      )
             else:
                    _a       :					Dict	   =inputs
             _a       :					Dict	   =outputs.replace("""``"""	,		"""\""""      ).replace("""''"""	,		"""\""""      )

             if not self.keep_accents:
                    _a       :					Optional[Any]	   =unicodedata.normalize("""NFKD"""	,		_lowerCAmelCase      )
                    _a       :					Tuple	   ="""""".join([c for c in outputs if not unicodedata.combining(_lowerCAmelCase      )]      )
             if self.do_lower_case:
                    _a       :					Dict	   =outputs.lower()

             return outputs






      def __UpperCAmelCase			(					self     :List[str]	,		SCREAMING_SNAKE_CASE     :str      )			->   List[str]:




             '''simple docstring'''
             _a       :					int	   =self.preprocess_text(_lowerCAmelCase      )
             _a       :					Union[str, Any]	   =self.sp_model.encode(_lowerCAmelCase	,		out_type=_lowerCAmelCase      )
             _a       :					Optional[int]	   =[]
             for piece in pieces:
                    if len(_lowerCAmelCase      ) > 1 and piece[-1] == str(""","""      ) and piece[-2].isdigit():
                           _a       :					List[str]	   =self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCAmelCase	,		""""""      )      )
                           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                  if len(cur_pieces[0]      ) == 1:
                                         _a       :					str	   =cur_pieces[1:]
                                  else:
                                         _a       :					int	   =cur_pieces[0][1:]
                           cur_pieces.append(piece[-1]      )
                           new_pieces.extend(_lowerCAmelCase      )
                    else:
                           new_pieces.append(_lowerCAmelCase      )

             return new_pieces






      def __UpperCAmelCase			(					self     :int	,		SCREAMING_SNAKE_CASE     :Tuple      )			->   str:




             '''simple docstring'''
             return self.sp_model.PieceToId(_lowerCAmelCase      )






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




             '''simple docstring'''
             return self.sp_model.IdToPiece(_lowerCAmelCase      )






      def __UpperCAmelCase			(					self     :Tuple	,		SCREAMING_SNAKE_CASE     :str      )			->   Optional[Any]:




             '''simple docstring'''
             _a       :					Tuple	   ="""""".join(_lowerCAmelCase      ).replace(_lowerCAmelCase	,		""" """      ).strip()
             return out_string






      def __UpperCAmelCase			(					self     :List[Any]	,		SCREAMING_SNAKE_CASE     :List[str]	,		SCREAMING_SNAKE_CASE     :Tuple = None      )			->   List[int]:




             '''simple docstring'''
             _a       :					int	   =[self.sep_token_id]
             _a       :					List[Any]	   =[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     :str	,		SCREAMING_SNAKE_CASE     :Union[str, Any]	,		SCREAMING_SNAKE_CASE     :Dict = None	,		SCREAMING_SNAKE_CASE     :str = False      )			->   List[int]:




             '''simple docstring'''

             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=_lowerCAmelCase	,		token_ids_a=_lowerCAmelCase	,		already_has_special_tokens=_lowerCAmelCase      )

             if token_ids_a is not None:
                    return ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1, 1]
             return ([0] * len(_lowerCAmelCase      )) + [1, 1]






      def __UpperCAmelCase			(					self     :Tuple	,		SCREAMING_SNAKE_CASE     :str	,		SCREAMING_SNAKE_CASE     :Union[str, Any] = None      )			->   List[int]:




             '''simple docstring'''
             _a       :					str	   =[self.sep_token_id]
             _a       :					str	   =[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     :Dict	,		SCREAMING_SNAKE_CASE     :Any	,		SCREAMING_SNAKE_CASE     :Dict = None      )			->   Tuple[str]:




             '''simple docstring'''
             if not os.path.isdir(_lowerCAmelCase      ):
                    logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
                    return
             _a       :					Union[str, 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      ) and os.path.isfile(self.vocab_file      ):
                    copyfile(self.vocab_file	,		_lowerCAmelCase      )
             elif not os.path.isfile(self.vocab_file      ):
                    with open(_lowerCAmelCase	,		"""wb"""      ) as fi:
                           _a       :					Union[str, Any]	   =self.sp_model.serialized_model_proto()
                           fi.write(_lowerCAmelCase      )

             return (out_vocab_file,)






      def __UpperCAmelCase			(					self     :List[Any]	,		*SCREAMING_SNAKE_CASE     :Tuple	,		**SCREAMING_SNAKE_CASE     :Any      )			->   Union[str, Any]:




             '''simple docstring'''
             _a       :					str	   =super()._decode(*_lowerCAmelCase	,		**_lowerCAmelCase      )
             _a       :					Optional[Any]	   =text.replace(""" """	,		""""""      ).replace("""\u2582"""	,		""" """      ).replace("""\u2583"""	,		"""\n"""      )
             return text









'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       (DPMSolverSinglestepScheduler,)
 __lowerCamelCase    :				int								=       (("num_inference_steps", 25),)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "solver_order": 2,
         "prediction_type": "epsilon",
         "thresholding": False,
         "sample_max_value": 1.0,
         "algorithm_type": "dpmsolver++",
         "solver_type": "midpoint",
         "lambda_min_clipped": -float("inf"		),
         "variance_type": None,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)
         scheduler.set_timesteps(_lowerCAmelCase		)
         # copy over dummy past residuals
         _lowerCAmelCase						      =   dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase		)
             _lowerCAmelCase						      =   scheduler_class.from_pretrained(_lowerCAmelCase		)
             new_scheduler.set_timesteps(_lowerCAmelCase		)
             # copy over dummy past residuals
             _lowerCAmelCase						      =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase      ,      _lowerCAmelCase						      =   sample, sample
         for t in range(_lowerCAmelCase      ,  time_step + scheduler.config.solver_order + 1		):
             _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
             _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"




 def 				_snake_case (						self		)     ->				int:
     pass




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase						      =   self.get_scheduler_config()
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)
         scheduler.set_timesteps(_lowerCAmelCase		)

         # copy over dummy past residuals (must be after setting timesteps)
         _lowerCAmelCase						      =   dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase		)
             _lowerCAmelCase						      =   scheduler_class.from_pretrained(_lowerCAmelCase		)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(_lowerCAmelCase		)

             # copy over dummy past residual (must be after setting timesteps)
             _lowerCAmelCase						      =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
         _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

         assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"




 def 				_snake_case (						self      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase		)     ->				Tuple:
     if scheduler is None:
         _lowerCAmelCase						      =   self.scheduler_classes[0]
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     return sample




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   50
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:]		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2574		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for timesteps in [25, 50, 100, 999, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3

     _lowerCAmelCase						      =   DEISMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   UniPCMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for order in [1, 2, 3]:
         for solver_type in ["midpoint", "heun"]:
             for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  algorithm_type="dpmsolver++"      ,  solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
         for solver_type in ["midpoint", "heun"]:
             for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     _lowerCAmelCase						      =   self.full_loop(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
     self.check_over_configs(lambda_min_clipped=-5.1		)




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(variance_type=_lowerCAmelCase		)
     self.check_over_configs(variance_type="learned_range"		)




 def 				_snake_case (						self		)     ->				int:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
         self.check_over_forward(num_inference_steps=_lowerCAmelCase      ,  time_step=0		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop()
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.full_loop(use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2248		) < 1E-3




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.1453		) < 1E-3




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"      ,  use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.0649		) < 1E-3





 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(thresholding=_lowerCAmelCase      ,  dynamic_thresholding_ratio=0		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter.half()
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     assert sample.dtype == torch.floataa


