



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_    :  int						  =		{'vocab_file': 'spiece.model'}

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





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



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






           '''simple docstring'''



           lowerCamelCase             =		AddedToken(__a					,      lstrip=__a					,      rstrip=__a     ) if isinstance(__a					,      __a     ) else mask_token

           lowerCamelCase             =		{} if sp_model_kwargs is None else sp_model_kwargs

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

           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(__a     )

           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     _a   (self     ):






           '''simple docstring'''



           return len(self.sp_model     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		{self.convert_ids_to_tokens(__a     ): 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					,      __a     ):






           '''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     _a   (self					,      __a     ):






           '''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"					,      __a     )
                 lowerCamelCase             =		"".join([c for c in outputs if not unicodedata.combining(__a     )]     )
           if self.do_lower_case:
                 lowerCamelCase             =		outputs.lower()

           return outputs







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.preprocess_text(__a     )
           lowerCamelCase             =		self.sp_model.encode(__a					,      out_type=__a     )
           lowerCamelCase             =		[]
           for piece in pieces:
                 if len(__a     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
                       lowerCamelCase             =		self.sp_model.EncodeAsPieces(piece[:-1].replace(__a					,      ""     )     )
                       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(__a     )
                 else:
                       new_pieces.append(__a     )

           return new_pieces







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           return self.sp_model.PieceToId(__a     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           return self.sp_model.IdToPiece(__a     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		"".join(__a     ).replace(__a					,      " "     ).strip()
           return out_string







     def     _a   (self					,      __a					,      __a = 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     _a   (self					,      __a					,      __a = None					,      __a = False     ):






           '''simple docstring'''



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

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







     def     _a   (self					,      __a					,      __a = 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     _a   (self					,      __a					,      __a = None     ):






           '''simple docstring'''



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

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

           return (out_vocab_file,)







     def     _a   (self					,      *__a					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		super()._decode(*__a					,      **__a     )
           lowerCamelCase             =		text.replace(" "					,      ""     ).replace("\u2582"					,      " "     ).replace("\u2583"					,      "\n"     )
           return text




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
     import tensorflow as tf


a_    :  Any						  =		logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      [
         'no_inference',
         'no_cuda',
         'no_tpu',
         'no_speed',
         'no_memory',
         'no_env_print',
         'no_multi_process',
     ]







     def __init__(self					,      **__a     ):






           '''simple docstring'''



           for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                       lowerCamelCase             =		deprecated_arg[3:]
                       lowerCamelCase             =		not kwargs.pop(__a     )
                       logger.warning(
                           F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
                           F""" {positive_arg}={kwargs[positive_arg]}"""     )
           lowerCamelCase             =		kwargs.pop("tpu_name"					,      self.tpu_name     )
           lowerCamelCase             =		kwargs.pop("device_idx"					,      self.device_idx     )
           lowerCamelCase             =		kwargs.pop("eager_mode"					,      self.eager_mode     )
           lowerCamelCase             =		kwargs.pop("use_xla"					,      self.use_xla     )
           super().__init__(**__a     )

     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Name of TPU'}							,     )
     _A      =      field(
         default=0							,     metadata={'help': 'CPU / GPU device index. Defaults to 0.'}							,     )
     _A      =      field(default=UpperCAmelCase_							,     metadata={'help': 'Benchmark models in eager model.'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={
             'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
         }							,     )







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           lowerCamelCase             =		None
           if self.tpu:
                 try:
                       if self.tpu_name:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
                       else:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                       lowerCamelCase             =		None
           return tpu







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.is_tpu:
                 tf.config.experimental_connect_to_cluster(self._setup_tpu     )
                 tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

                 lowerCamelCase             =		tf.distribute.TPUStrategy(self._setup_tpu     )
           else:
                 # currently no multi gpu is allowed
                 if self.is_gpu:
                       # TODO: Currently only single GPU is supported
                       tf.config.set_visible_devices(self.gpu_list[self.device_idx]					,      "GPU"     )
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""     )
                 else:
                       tf.config.set_visible_devices([]					,      "GPU"     )  # disable GPU
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""     )

           return strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_tpu is not None







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return tf.config.list_physical_devices("GPU"     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.cuda:
                 return len(self.gpu_list     )
           return 0







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.n_gpu > 0