





'''simple docstring'''






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			Union[str, Any]						     =   (CMStochasticIterativeScheduler,)
      _lowercase:			Optional[Any]						     =   10




      def 				lowercase__    (			self			: Union[str, Any]			,			**__snake_case			: Any  )				->      Any:
            _lowerCAmelCase							     =							{
                """num_train_timesteps""": 2_01,
                """sigma_min""": 0.0_02,
                """sigma_max""": 80.0,
            }

            config.update(**__snake_case  )
            return config




      def 				lowercase__    (			self			: Union[str, Any]  )				->      int:
            _lowerCAmelCase							     =							10

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

            scheduler.set_timesteps(__snake_case  )

            _lowerCAmelCase							     =							scheduler.timesteps[0]
            _lowerCAmelCase							     =							scheduler.timesteps[1]

            _lowerCAmelCase							     =							self.dummy_sample
            _lowerCAmelCase							     =							0.1 * sample

            _lowerCAmelCase							     =							scheduler.step(__snake_case			,			__snake_case			,			__snake_case  ).prev_sample
            _lowerCAmelCase							     =							scheduler.step(__snake_case			,			__snake_case			,			__snake_case  ).prev_sample

            self.assertEqual(output_a.shape			,			sample.shape  )
            self.assertEqual(output_a.shape			,			output_a.shape  )




      def 				lowercase__    (			self			: List[str]  )				->      Union[str, Any]:
            for timesteps in [10, 50, 1_00, 10_00]:
                  self.check_over_configs(num_train_timesteps=__snake_case  )




      def 				lowercase__    (			self			: List[str]  )				->      int:
            for clip_denoised in [True, False]:
                  self.check_over_configs(clip_denoised=__snake_case  )




      def 				lowercase__    (			self			: Any  )				->      List[Any]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__snake_case  )

            _lowerCAmelCase							     =							1
            scheduler.set_timesteps(__snake_case  )
            _lowerCAmelCase							     =							scheduler.timesteps

            _lowerCAmelCase							     =							torch.manual_seed(0  )

            _lowerCAmelCase							     =							self.dummy_model()
            _lowerCAmelCase							     =							self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(__snake_case  ):
                  # 1. scale model input
                  _lowerCAmelCase							     =							scheduler.scale_model_input(__snake_case			,			__snake_case  )

                  # 2. predict noise residual
                  _lowerCAmelCase							     =							model(__snake_case			,			__snake_case  )

                  # 3. predict previous sample x_t-1
                  _lowerCAmelCase							     =							scheduler.step(__snake_case			,			__snake_case			,			__snake_case			,			generator=__snake_case  ).prev_sample

                  _lowerCAmelCase							     =							pred_prev_sample

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

            assert abs(result_sum.item() - 1_92.76_14  ) < 1E-2
            assert abs(result_mean.item() - 0.25_10  ) < 1E-3




      def 				lowercase__    (			self			: Optional[Any]  )				->      List[Any]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__snake_case  )

            _lowerCAmelCase							     =							[1_06, 0]
            scheduler.set_timesteps(timesteps=__snake_case  )
            _lowerCAmelCase							     =							scheduler.timesteps

            _lowerCAmelCase							     =							torch.manual_seed(0  )

            _lowerCAmelCase							     =							self.dummy_model()
            _lowerCAmelCase							     =							self.dummy_sample_deter * scheduler.init_noise_sigma

            for t in timesteps:
                  # 1. scale model input
                  _lowerCAmelCase							     =							scheduler.scale_model_input(__snake_case			,			__snake_case  )

                  # 2. predict noise residual
                  _lowerCAmelCase							     =							model(__snake_case			,			__snake_case  )

                  # 3. predict previous sample x_t-1
                  _lowerCAmelCase							     =							scheduler.step(__snake_case			,			__snake_case			,			__snake_case			,			generator=__snake_case  ).prev_sample

                  _lowerCAmelCase							     =							pred_prev_sample

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

            assert abs(result_sum.item() - 3_47.63_57  ) < 1E-2
            assert abs(result_mean.item() - 0.45_27  ) < 1E-3




      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__snake_case  )

            _lowerCAmelCase							     =							[39, 30, 12, 15, 0]

            with self.assertRaises(__snake_case			,			msg="""`timesteps` must be in descending order."""  ):
                  scheduler.set_timesteps(timesteps=__snake_case  )




      def 				lowercase__    (			self			: int  )				->      int:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__snake_case  )

            _lowerCAmelCase							     =							[39, 30, 12, 1, 0]
            _lowerCAmelCase							     =							len(__snake_case  )

            with self.assertRaises(__snake_case			,			msg="""Can only pass one of `num_inference_steps` or `timesteps`."""  ):
                  scheduler.set_timesteps(num_inference_steps=__snake_case			,			timesteps=__snake_case  )




      def 				lowercase__    (			self			: Optional[int]  )				->      str:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__snake_case  )

            _lowerCAmelCase							     =							[scheduler.config.num_train_timesteps]

            with self.assertRaises(
                __snake_case			,			msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""			,			):
                  scheduler.set_timesteps(timesteps=__snake_case  )










'''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_  :				Any						= logging.get_logger(__name__)

a_  :				Optional[Any]						= {"""vocab_file""": """spiece.model"""}

a_  :				Tuple						= {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=True			,  UpperCamelCase=False			,  UpperCamelCase="<s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase="<sep>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="<cls>"			,  UpperCamelCase="<mask>"			,  UpperCamelCase=["<eop>", "<eod>"]			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  AddedToken(UpperCamelCase			,  lstrip=UpperCamelCase			,  rstrip=UpperCamelCase     ) if isinstance(UpperCamelCase			,  UpperCamelCase     ) else mask_token

   lowerCamelCase_      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCamelCase			,  remove_space=UpperCamelCase			,  keep_accents=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  unk_token=UpperCamelCase			,  sep_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  cls_token=UpperCamelCase			,  mask_token=UpperCamelCase			,  additional_special_tokens=UpperCamelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  3

   lowerCamelCase_      =  do_lower_case
   lowerCamelCase_      =  remove_space
   lowerCamelCase_      =  keep_accents
   lowerCamelCase_      =  vocab_file

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(UpperCamelCase     )

   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."     )
   lowerCamelCase_      =  jieba
   lowerCamelCase_      =  str.maketrans(" \n"			,  "\u2582\u2583"     )






  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def   snake_case		(    self     ):







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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {self.convert_ids_to_tokens(UpperCamelCase     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def __getstate__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d

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

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(self.vocab_file     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if self.remove_space:
    lowerCamelCase_      =  " ".join(inputs.strip().split()     )
   else:
    lowerCamelCase_      =  inputs
   lowerCamelCase_      =  outputs.replace("``"			,  "\""     ).replace("''"			,  "\""     )

   if not self.keep_accents:
    lowerCamelCase_      =  unicodedata.normalize("NFKD"			,  UpperCamelCase     )
    lowerCamelCase_      =  "".join([c for c in outputs if not unicodedata.combining(UpperCamelCase     )]     )
   if self.do_lower_case:
    lowerCamelCase_      =  outputs.lower()

   return outputs






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.preprocess_text(UpperCamelCase     )
   lowerCamelCase_      =  self.sp_model.encode(UpperCamelCase			,  out_type=UpperCamelCase     )
   lowerCamelCase_      =  []
   for piece in pieces:
    if len(UpperCamelCase     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
     lowerCamelCase_      =  self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase			,  ""     )     )
     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
      if len(cur_pieces[0]     ) == 1:
       lowerCamelCase_      =  cur_pieces[1:]
      else:
       lowerCamelCase_      =  cur_pieces[0][1:]
     cur_pieces.append(piece[-1]     )
     new_pieces.extend(UpperCamelCase     )
    else:
     new_pieces.append(UpperCamelCase     )

   return new_pieces






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.PieceToId(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.IdToPiece(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "".join(UpperCamelCase     ).replace(UpperCamelCase			,  " "     ).strip()
   return out_string






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [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			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = False     ):







   """simple docstring"""

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

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






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [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			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   if not os.path.isdir(UpperCamelCase     ):
    logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
    return
   lowerCamelCase_      =  os.path.join(
       UpperCamelCase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file			,  UpperCamelCase     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(UpperCamelCase			,  "wb"     ) as fi:
     lowerCamelCase_      =  self.sp_model.serialized_model_proto()
     fi.write(UpperCamelCase     )

   return (out_vocab_file,)







  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._decode(*UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  text.replace(" "			,  ""     ).replace("\u2582"			,  " "     ).replace("\u2583"			,  "\n"     )
   return text



