





'''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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{"vocab_file": "spiece.model"}

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

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase="<s>"      ,  _lowerCAmelCase="</s>"      ,  _lowerCAmelCase="<unk>"      ,  _lowerCAmelCase="<sep>"      ,  _lowerCAmelCase="<pad>"      ,  _lowerCAmelCase="<cls>"      ,  _lowerCAmelCase="<mask>"      ,  _lowerCAmelCase=["<eop>", "<eod>"]      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				None:
     _lowerCAmelCase						      =   AddedToken(_lowerCAmelCase      ,  lstrip=_lowerCAmelCase      ,  rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		) else mask_token

     _lowerCAmelCase						      =   {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         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      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCAmelCase      ,  )

     _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(_lowerCAmelCase		)

     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		)     ->				Optional[int]:
     return len(self.sp_model		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




 def __getstate__(						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.__dict__.copy()
     _lowerCAmelCase						      =   None
     return state




 def __setstate__(						self      ,  _lowerCAmelCase		)     ->				Dict:
     _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      ,  _lowerCAmelCase		)     ->				str:
     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"      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   "".join([c for c in outputs if not unicodedata.combining(_lowerCAmelCase		)]		)
     if self.do_lower_case:
         _lowerCAmelCase						      =   outputs.lower()

     return outputs




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

     return new_pieces




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     return self.sp_model.PieceToId(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     return self.sp_model.IdToPiece(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   "".join(_lowerCAmelCase		).replace(_lowerCAmelCase      ,  " "		).strip()
     return out_string




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _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      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False		)     ->				List[int]:

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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _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      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase						      =   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		) and os.path.isfile(self.vocab_file		):
         copyfile(self.vocab_file      ,  _lowerCAmelCase		)
     elif not os.path.isfile(self.vocab_file		):
         with open(_lowerCAmelCase      ,  "wb"		) as fi:
             _lowerCAmelCase						      =   self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase		)

     return (out_vocab_file,)





 def 				_snake_case (						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   super()._decode(*_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   text.replace(" "      ,  ""		).replace("\u2582"      ,  " "		).replace("\u2583"      ,  "\n"		)
     return text








from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
						from ...onnx.config import PatchingSpec
						from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE:     str									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="longformer"



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[List[int], int] = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   int = 1					,							snake_case__	:   int = 0					,							snake_case__	:   int = 2					,							snake_case__	:   int = 3_05_22					,							snake_case__	:   int = 7_68					,							snake_case__	:   int = 12					,							snake_case__	:   int = 12					,							snake_case__	:   int = 30_72					,							snake_case__	:   str = "gelu"					,							snake_case__	:   float = 0.1					,							snake_case__	:   float = 0.1					,							snake_case__	:   int = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   float = 0.02					,							snake_case__	:   float = 1e-12					,							snake_case__	:   bool = False					,							**snake_case__	:   Tuple					,							):

								"""simple docstring"""

								super().__init__(pad_token_id=snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							attention_window
								SCREAMING_SNAKE_CASE_					      =							sep_token_id
								SCREAMING_SNAKE_CASE_					      =							bos_token_id
								SCREAMING_SNAKE_CASE_					      =							eos_token_id
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							hidden_size
								SCREAMING_SNAKE_CASE_					      =							num_hidden_layers
								SCREAMING_SNAKE_CASE_					      =							num_attention_heads
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							intermediate_size
								SCREAMING_SNAKE_CASE_					      =							hidden_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							type_vocab_size
								SCREAMING_SNAKE_CASE_					      =							initializer_range
								SCREAMING_SNAKE_CASE_					      =							layer_norm_eps
								SCREAMING_SNAKE_CASE_					      =							onnx_export



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Dict					,							snake_case__	:   "PretrainedConfig"					,							snake_case__	:   str = "default"					,							snake_case__	:   "List[PatchingSpec]" = None  ):

								"""simple docstring"""

								super().__init__(snake_case__					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							True



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								if self.task == "multiple-choice":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								        ('global_attention_mask', dynamic_axis),
								    ]  )



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().outputs
								if self.task == "default":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
								return outputs



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return 1e-4



						@property
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								return max(super().default_onnx_opset					,							14  )






						def 	__a					(			self	:   List[str]					,							snake_case__	:   "PreTrainedTokenizerBase"					,							snake_case__	:   int = -1					,							snake_case__	:   int = -1					,							snake_case__	:   bool = False					,							snake_case__	:   Optional[TensorType] = None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().generate_dummy_inputs(
								    preprocessor=snake_case__					,							batch_size=snake_case__					,							seq_length=snake_case__					,							is_pair=snake_case__					,							framework=snake_case__  )
								import torch

								# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
								# makes the export fail randomly
								SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(inputs['input_ids']  )
								# make every second token global
								SCREAMING_SNAKE_CASE_					      =							1

								return inputs