

"""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


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

snake_case_   :   int        =       {"""vocab_file""": """spiece.model"""}

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







class       snake_case__  ( lowerCAmelCase_     ):







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





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				AddedToken(lowercase			,  lstrip=lowercase			,  rstrip=lowercase  ) if isinstance(lowercase			,  lowercase  ) else mask_token

								UpperCAmelCase			:   Tuple      =				{} if sp_model_kwargs is None else sp_model_kwargs

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

								UpperCAmelCase			:   List[Any]      =				3

								UpperCAmelCase			:   Optional[Any]      =				do_lower_case
								UpperCAmelCase			:   List[str]      =				remove_space
								UpperCAmelCase			:   Optional[Any]      =				keep_accents
								UpperCAmelCase			:   List[Any]      =				vocab_file

								UpperCAmelCase			:   Optional[int]      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(lowercase  )

								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			:   Optional[Any]      =				jieba
								UpperCAmelCase			:   List[Any]      =				str.maketrans(" \n"			,  "\u2582\u2583"  )







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





								'''simple docstring'''



								return len(self.sp_model  )







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				{self.convert_ids_to_tokens(lowercase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







			def __getstate__(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self.__dict__.copy()
								UpperCAmelCase			:   Optional[int]      =				None
								return state







			def __setstate__(			self			: Tuple			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				d

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

								UpperCAmelCase			:   str      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.vocab_file  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Optional[int]  ):





								'''simple docstring'''



								if self.remove_space:
													UpperCAmelCase			:   Tuple      =				" ".join(inputs.strip().split()  )
								else:
													UpperCAmelCase			:   int      =				inputs
								UpperCAmelCase			:   Optional[int]      =				outputs.replace("``"			,  "\""  ).replace("''"			,  "\""  )

								if not self.keep_accents:
													UpperCAmelCase			:   List[str]      =				unicodedata.normalize("NFKD"			,  lowercase  )
													UpperCAmelCase			:   Optional[int]      =				"".join([c for c in outputs if not unicodedata.combining(lowercase  )]  )
								if self.do_lower_case:
													UpperCAmelCase			:   List[Any]      =				outputs.lower()

								return outputs







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: str  ):





								'''simple docstring'''



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

								return new_pieces







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								return self.sp_model.PieceToId(lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: str  ):





								'''simple docstring'''



								return self.sp_model.IdToPiece(lowercase  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				"".join(lowercase  ).replace(lowercase			,  " "  ).strip()
								return out_string







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				[self.sep_token_id]
								UpperCAmelCase			:   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 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None			,  lowercase			: bool = False  ):





								'''simple docstring'''



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

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







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				[self.sep_token_id]
								UpperCAmelCase			:   Tuple      =				[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 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: str			,  lowercase			: Optional[str] = None  ):





								'''simple docstring'''



								if not os.path.isdir(lowercase  ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
													return
								UpperCAmelCase			:   List[str]      =				os.path.join(
								    lowercase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase  ) and os.path.isfile(self.vocab_file  ):
													copyfile(self.vocab_file			,  lowercase  )
								elif not os.path.isfile(self.vocab_file  ):
													with open(lowercase			,  "wb"  ) as fi:
																		UpperCAmelCase			:   List[Any]      =				self.sp_model.serialized_model_proto()
																		fi.write(lowercase  )

								return (out_vocab_file,)







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  *lowercase			: Tuple			,  **lowercase			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				super()._decode(*lowercase			,  **lowercase  )
								UpperCAmelCase			:   str      =				text.replace(" "			,  ""  ).replace("\u2582"			,  " "  ).replace("\u2583"			,  "\n"  )
								return text





"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput







@dataclass
class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						1







			@register_to_config
			def __init__(			self			: Optional[int]			,  lowercase			: int = 20_00			,  lowercase			: float = 0.1_5			,  lowercase			: float = 0.0_1			,  lowercase			: float = 1_3_4_8.0			,  lowercase			: float = 1E-5			,  lowercase			: int = 1			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_max

								# setable values
								UpperCAmelCase			:   Any      =				None

								self.set_sigmas(lowercase			,  lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[int] = None  ):





								'''simple docstring'''



								return sample







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: Union[str, torch.device] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

								UpperCAmelCase			:   Tuple      =				torch.linspace(1			,  lowercase			,  lowercase			,  device=lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: float = None			,  lowercase			: float = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_min if sigma_min is not None else self.config.sigma_min
								UpperCAmelCase			:   List[str]      =				sigma_max if sigma_max is not None else self.config.sigma_max
								UpperCAmelCase			:   Union[str, Any]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
													self.set_timesteps(lowercase			,  lowercase  )

								UpperCAmelCase			:   List[Any]      =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								UpperCAmelCase			:   Any      =				torch.exp(torch.linspace(math.log(lowercase  )			,  math.log(lowercase  )			,  lowercase  )  )
								UpperCAmelCase			:   str      =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Dict			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								return torch.where(
								    timesteps == 0			,  torch.zeros_like(t.to(timesteps.device  )  )			,  self.discrete_sigmas[timesteps - 1].to(timesteps.device  )			,  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: int			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

								UpperCAmelCase			:   Any      =				timestep * torch.ones(
								    sample.shape[0]			,  device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
								UpperCAmelCase			:   Dict      =				(timestep * (len(self.timesteps  ) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								UpperCAmelCase			:   List[str]      =				timesteps.to(self.discrete_sigmas.device  )

								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas[timesteps].to(sample.device  )
								UpperCAmelCase			:   Tuple      =				self.get_adjacent_sigma(lowercase			,  lowercase  ).to(sample.device  )
								UpperCAmelCase			:   Optional[int]      =				torch.zeros_like(lowercase  )
								UpperCAmelCase			:   Union[str, Any]      =				(sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								UpperCAmelCase			:   List[Any]      =				diffusion.flatten()
								while len(diffusion.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Optional[int]      =				diffusion.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								UpperCAmelCase			:   List[Any]      =				randn_tensor(
								    sample.shape			,  layout=sample.layout			,  generator=lowercase			,  device=sample.device			,  dtype=sample.dtype  )
								UpperCAmelCase			:   int      =				sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								UpperCAmelCase			:   Tuple      =				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
													return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowercase			,  prev_sample_mean=lowercase  )







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								UpperCAmelCase			:   str      =				randn_tensor(sample.shape			,  layout=sample.layout			,  generator=lowercase  ).to(sample.device  )

								# compute step size from the model_output, the noise, and the snr
								UpperCAmelCase			:   Tuple      =				torch.norm(model_output.reshape(model_output.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   List[Any]      =				torch.norm(noise.reshape(noise.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   Tuple      =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
								UpperCAmelCase			:   int      =				step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								UpperCAmelCase			:   int      =				step_size.flatten()
								while len(step_size.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Union[str, Any]      =				step_size.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				sample + step_size * model_output
								UpperCAmelCase			:   Union[str, Any]      =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
													return (prev_sample,)

								return SchedulerOutput(prev_sample=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				timesteps.to(original_samples.device  )
								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas.to(original_samples.device  )[timesteps]
								UpperCAmelCase			:   List[str]      =				(
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowercase  ) * sigmas[:, None, None, None]
								)
								UpperCAmelCase			:   Optional[Any]      =				noise + original_samples
								return noisy_samples







			def __len__(			self			: List[str]  ):





								'''simple docstring'''



								return self.config.num_train_timesteps


