
'''simple docstring'''





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

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


A							     = logging.get_logger(__name__)

A							     = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A							     = {
    "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__ (							UpperCamelCase							):



  '''simple docstring'''






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

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

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			List[int]						,			lowerCAmelCase_	:			Optional[List[int]] = None     ) ->							Optional[Any]:
        snake_case_    							=				[self.sep_token_id]
        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 		_lowerCamelCase						(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[int]						,			lowerCAmelCase_	:			Optional[List[int]] = None     ) ->							List[Any]:
        snake_case_    							=				[self.sep_token_id]
        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 		_lowerCamelCase						(self	:			List[Any]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Optional[str] = None     ) ->							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
        snake_case_    							=				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 		_lowerCamelCase						(self	:			List[Any]						,			lowerCAmelCase_	:			int						,			*lowerCAmelCase_	:			Dict						,			**lowerCAmelCase_	:			Optional[int]     ) ->							Dict:
        snake_case_    							=				[
            " ".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 		_lowerCamelCase						(self	:			int						,			*lowerCAmelCase_	:			Optional[Any]						,			**lowerCAmelCase_	:			str     ) ->							Union[str, Any]:
        snake_case_    							=				super()._decode(*_lowerCamelCase						,			**_lowerCamelCase     )
        snake_case_    							=				text.replace(" "						,			""     ).replace("\u2582"						,			" "     ).replace("\u2583"						,			"\n"     )
        return text



import torch

from accelerate import Accelerator, DistributedType
class    _UpperCamelCase:

						def __enter__(					self		:			str			):
													if self.enabled:
																				_UpperCAmelCase  :			int	       =		self.model.no_sync()
																				self.model_sync_obj.__enter__()

													return self

						def __exit__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Optional[Any]			):
													if self.enabled:
																				# Average all models on exit
																				self._sync_and_avg_model_params()
																				self.model_sync_obj.__exit__(_lowerCamelCase	,						_lowerCamelCase	,						_lowerCamelCase			)

						def __init__(					self		:			str	,						_lowerCamelCase		:			Accelerator	,						_lowerCamelCase		:			torch.nn.Module	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			bool = True			):
													if accelerator.distributed_type not in [
													    DistributedType.NO,
													    DistributedType.MULTI_CPU,
													    DistributedType.MULTI_GPU,
													]:
																				raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"			)
													_UpperCAmelCase  :			Any	       =		enabled and accelerator.distributed_type != DistributedType.NO
													_UpperCAmelCase  :			str	       =		0
													if self.enabled:
																				_UpperCAmelCase  :			Tuple	       =		accelerator
																				_UpperCAmelCase  :			List[str]	       =		model
																				_UpperCAmelCase  :			Dict	       =		local_sgd_steps

						def 						a__ (					self		:			Optional[int]			):
													self.num_steps += 1
													if not self.enabled:
																				return

													if self.num_steps % self.local_sgd_steps == 0:
																				self._sync_and_avg_model_params()






						def 						a__ (					self		:			Tuple			):
													self.accelerator.wait_for_everyone()
													with self.accelerator.autocast():
																				for param in self.model.parameters():
																											_UpperCAmelCase  :			Optional[Any]	       =		self.accelerator.reduce(param.data	,						reduction="mean"			)




