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

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


__lowerCamelCase								=					logging.get_logger(__name__)

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

__lowerCamelCase								=					{
    '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    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):

						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	,						):
													_UpperCAmelCase  :			Optional[int]	       =		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	,						)

													_UpperCAmelCase  :			Union[str, Any]	       =		3
													_UpperCAmelCase  :			int	       =		do_lower_case
													_UpperCAmelCase  :			str	       =		remove_space
													_UpperCAmelCase  :			Optional[int]	       =		keep_accents
													_UpperCAmelCase  :			int	       =		vocab_file
													_UpperCAmelCase  :			List[Any]	       =		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."			)
													_UpperCAmelCase  :			Dict	       =		jieba
													_UpperCAmelCase  :			List[Any]	       =		str.maketrans(" \n"	,						"\u2582\u2583"			)

						def 						a__ (					self		:			Optional[int]	,						_lowerCamelCase		:			List[int]	,						_lowerCamelCase		:			Optional[List[int]] = None			):
													_UpperCAmelCase  :			Any	       =		[self.sep_token_id]
													_UpperCAmelCase  :			Tuple	       =		[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 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			List[int]	,						_lowerCamelCase		:			Optional[List[int]] = None			):
													_UpperCAmelCase  :			Union[str, Any]	       =		[self.sep_token_id]
													_UpperCAmelCase  :			Optional[int]	       =		[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 						a__ (					self		:			List[Any]	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			Optional[str] = None			):
													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
													_UpperCAmelCase  :			Optional[int]	       =		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 						a__ (					self		:			List[Any]	,						_lowerCamelCase		:			int	,						*_lowerCamelCase		:			Dict	,						**_lowerCamelCase		:			Optional[int]			):
													_UpperCAmelCase  :			List[str]	       =		[
													    " ".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 						a__ (					self		:			int	,						*_lowerCamelCase		:			Optional[Any]	,						**_lowerCamelCase		:			str			):
													_UpperCAmelCase  :			Optional[int]	       =		super()._decode(*_lowerCamelCase	,						**_lowerCamelCase			)
													_UpperCAmelCase  :			Tuple	       =		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"			)




