


'''simple docstring'''







import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A				:					int    						=			logging.get_logger(__name__)

A				:					int    						=			'''▁'''

A				:					int    						=			{'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

A				:					List[str]    						=			{
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

A				:					str    						=			{
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

A				:					List[str]    						=			{
    '''ernie-m-base''': 514,
    '''ernie-m-large''': 514,
}

A				:					Optional[Any]    						=			{
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   ["input_ids"]

    a													=   VOCAB_FILES_NAMES
    a													=   PRETRAINED_INIT_CONFIGURATION
    a													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=   PRETRAINED_VOCAB_FILES_MAP
    a													=   RESOURCE_FILES_NAMES


    def __init__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=False				,							SCREAMING_SNAKE_CASE      :							Any="utf8"				,							SCREAMING_SNAKE_CASE      :							List[str]="[UNK]"				,							SCREAMING_SNAKE_CASE      :							Tuple="[SEP]"				,							SCREAMING_SNAKE_CASE      :							int="[PAD]"				,							SCREAMING_SNAKE_CASE      :							Tuple="[CLS]"				,							SCREAMING_SNAKE_CASE      :							Optional[int]="[MASK]"				,							SCREAMING_SNAKE_CASE      :							Optional[Dict[str, Any]] = None				,							**SCREAMING_SNAKE_CASE      :							Tuple				,							):
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.

           _A					:     Tuple     = {} if sp_model_kwargs is None else sp_model_kwargs
           super().__init__(
               do_lower_case=SCREAMING_SNAKE_CASE				,							unk_token=SCREAMING_SNAKE_CASE				,							sep_token=SCREAMING_SNAKE_CASE				,							pad_token=SCREAMING_SNAKE_CASE				,							cls_token=SCREAMING_SNAKE_CASE				,							mask_token=SCREAMING_SNAKE_CASE				,							vocab_file=SCREAMING_SNAKE_CASE				,							encoding=SCREAMING_SNAKE_CASE				,							sp_model_kwargs=self.sp_model_kwargs				,							**SCREAMING_SNAKE_CASE				,							)
           _A					:     Union[str, Any]     = do_lower_case
           _A					:     List[str]     = sentencepiece_model_ckpt
           _A					:     Tuple     = spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(SCREAMING_SNAKE_CASE)

           # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
           if vocab_file is not None:
                  _A					:     str     = self.load_vocab(filepath=SCREAMING_SNAKE_CASE)
           else:
                  _A					:     int     = {self.sp_model.id_to_piece(SCREAMING_SNAKE_CASE): id for id in range(self.sp_model.get_piece_size())}
           _A					:     Optional[int]     = {v: k for k, v in self.vocab.items()}


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           if text is None:
                  return None

           _A					:     Any     = self.tokenize(SCREAMING_SNAKE_CASE)
           _A  ,			_A					:     Union[str, Any]     = '', []

           for i, ch in enumerate(SCREAMING_SNAKE_CASE):
                  if ch in self.SP_CHAR_MAPPING:
                         _A					:     int     = self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE)
                  else:
                         _A					:     Optional[Any]     = unicodedata.normalize('NFKC'				,							SCREAMING_SNAKE_CASE)
                  if self.is_whitespace(SCREAMING_SNAKE_CASE):
                         continue
                  normalized_text += ch
                  char_mapping.extend([i] * len(SCREAMING_SNAKE_CASE))

           _A  ,			_A  ,			_A					:     int     = normalized_text, [], 0

           if self.do_lower_case:
                  _A					:     int     = text.lower()

           for token in split_tokens:
                  if token[:1] == "▁":
                         _A					:     Union[str, Any]     = token[1:]
                  _A					:     str     = text[offset:].index(SCREAMING_SNAKE_CASE) + offset
                  _A					:     int     = start + len(SCREAMING_SNAKE_CASE)

                  token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
                  _A					:     List[str]     = end
           return token_mapping


    @property
    def        A   (  self      :							List[Any]):
           return len(self.vocab)


    def        A   (  self      :							List[str]):
           return dict(self.vocab				,							**self.added_tokens_encoder)


    def __getstate__(  self      :							Dict):
           _A					:     Tuple     = self.__dict__.copy()
           _A					:     Any     = None
           return state


    def __setstate__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     Optional[int]     = d

           # for backward compatibility
           if not hasattr(self				,							'sp_model_kwargs'):
                  _A					:     str     = {}

           _A					:     int     = spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.sentencepiece_model_ckpt)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict):
           return "".join((self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for c in text))


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							List[Any]=False				,							SCREAMING_SNAKE_CASE      :							Any=64				,							SCREAMING_SNAKE_CASE      :							List[str]=0.1):

           if self.sp_model_kwargs.get('enable_sampling') is True:
                  _A					:     str     = True
           if self.sp_model_kwargs.get('alpha') is not None:
                  _A					:     Tuple     = self.sp_model_kwargs.get('alpha')
           if self.sp_model_kwargs.get('nbest_size') is not None:
                  _A					:     Any     = self.sp_model_kwargs.get('nbest_size')

           if not enable_sampling:
                  _A					:     Dict     = self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     List[str]     = self.sp_model.SampleEncodeAsPieces(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           _A					:     Any     = []
           for pi, piece in enumerate(SCREAMING_SNAKE_CASE):
                  if piece == SPIECE_UNDERLINE:
                         if not pieces[pi + 1].startswith(SCREAMING_SNAKE_CASE) and pi != 0:
                                new_pieces.append(SCREAMING_SNAKE_CASE)
                                continue
                         else:
                                continue
                  _A					:     Union[str, Any]     = 0
                  for i, chunk in enumerate(SCREAMING_SNAKE_CASE):
                         if chunk == SPIECE_UNDERLINE:
                                continue
                         if self.is_ch_char(SCREAMING_SNAKE_CASE) or self.is_punct(SCREAMING_SNAKE_CASE):
                                if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                       new_pieces.append(piece[lst_i:i])
                                new_pieces.append(SCREAMING_SNAKE_CASE)
                                _A					:     Dict     = i + 1
                         elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                                if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                       new_pieces.append(piece[lst_i:i])
                                _A					:     Dict     = i
                         elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                                if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                       new_pieces.append(piece[lst_i:i])
                                _A					:     List[str]     = i
                  if len(SCREAMING_SNAKE_CASE) > lst_i:
                         new_pieces.append(piece[lst_i:])
           return new_pieces


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Any     = ''.join(SCREAMING_SNAKE_CASE).replace(SCREAMING_SNAKE_CASE				,							' ').strip()
           return out_string


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Any     = self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = ''.join(SCREAMING_SNAKE_CASE).replace(SCREAMING_SNAKE_CASE				,							' ').strip()
           return out_string


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							List[str]):
           return self.vocab.get(SCREAMING_SNAKE_CASE				,							self.vocab.get(self.unk_token))


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return self.reverse_vocab.get(SCREAMING_SNAKE_CASE				,							self.unk_token)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=None):
           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _A					:     List[str]     = [self.cls_token_id]
           _A					:     Tuple     = [self.sep_token_id]
           return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict=None):
           if offset_mapping_a is None:
                  return [(0, 0)] + offset_mapping_a + [(0, 0)]

           return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None				,							SCREAMING_SNAKE_CASE      :							List[Any]=False):

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             'You should not supply a second sequence if the provided sequence of '
                             'ids is already formatted with special tokens for the model.')
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None):
           # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
           if token_ids_a is None:
                  # [CLS] X [SEP]
                  return (len(SCREAMING_SNAKE_CASE) + 2) * [0]

           # [CLS] A [SEP] [SEP] B [SEP]
           return [0] * (len(SCREAMING_SNAKE_CASE) + 1) + [1] * (len(SCREAMING_SNAKE_CASE) + 3)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           if "\u4e00" <= char <= "\u9fff":
                  return True
           return False


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Dict):
           if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                  return True
           return False


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Tuple):
           if char in ",;:.?!~，；：。？！《》【】":
                  return True
           return False


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict):
           if char == " " or char == "\t" or char == "\n" or char == "\r":
                  return True
           if len(SCREAMING_SNAKE_CASE) == 1:
                  _A					:     Optional[int]     = unicodedata.category(SCREAMING_SNAKE_CASE)
                  if cat == "Zs":
                         return True
           return False


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     Optional[Any]     = {}
           with io.open(SCREAMING_SNAKE_CASE				,							'r'				,							encoding='utf-8') as f:
                  for index, line in enumerate(SCREAMING_SNAKE_CASE):
                         _A					:     Any     = line.rstrip('\n')
                         _A					:     str     = int(SCREAMING_SNAKE_CASE)

           return token_to_idx


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[str] = None):
           _A					:     List[str]     = 0
           if os.path.isdir(SCREAMING_SNAKE_CASE):
                  _A					:     Any     = os.path.join(
                      SCREAMING_SNAKE_CASE				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
           else:
                  _A					:     Union[str, Any]     = (filename_prefix + '-' if filename_prefix else '') + save_directory
           with open(SCREAMING_SNAKE_CASE				,							'w'				,							encoding='utf-8') as writer:
                  for token, token_index in sorted(self.vocab.items()				,							key=lambda SCREAMING_SNAKE_CASE: kv[1]):
                         if index != token_index:
                                logger.warning(
                                    F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
                                    ' Please check that the vocabulary is not corrupted!')
                                _A					:     Dict     = token_index
                         writer.write(token + '\n')
                         index += 1

           _A					:     List[Any]     = os.path.join(SCREAMING_SNAKE_CASE				,							'sentencepiece.bpe.model')
           with open(SCREAMING_SNAKE_CASE				,							'wb') as fi:
                  _A					:     List[str]     = self.sp_model.serialized_model_proto()
                  fi.write(SCREAMING_SNAKE_CASE)

           return (vocab_file,)










'''simple docstring'''







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


A				:					Tuple    						=			logging.get_logger(__name__)

A				:					Tuple    						=			{
    '''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 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "longformer"


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[List[int], int] = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 30522				,							SCREAMING_SNAKE_CASE      :							int = 768				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 3072				,							SCREAMING_SNAKE_CASE      :							str = "gelu"				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							float = 0.02				,							SCREAMING_SNAKE_CASE      :							float = 1e-12				,							SCREAMING_SNAKE_CASE      :							bool = False				,							**SCREAMING_SNAKE_CASE      :							List[Any]				,							):
           super().__init__(pad_token_id=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = attention_window
           _A					:     int     = sep_token_id
           _A					:     Tuple     = bos_token_id
           _A					:     Any     = eos_token_id
           _A					:     List[str]     = vocab_size
           _A					:     Any     = hidden_size
           _A					:     Optional[int]     = num_hidden_layers
           _A					:     int     = num_attention_heads
           _A					:     Dict     = hidden_act
           _A					:     List[Any]     = intermediate_size
           _A					:     int     = hidden_dropout_prob
           _A					:     Optional[int]     = attention_probs_dropout_prob
           _A					:     int     = max_position_embeddings
           _A					:     Any     = type_vocab_size
           _A					:     Dict     = initializer_range
           _A					:     Any     = layer_norm_eps
           _A					:     List[Any]     = onnx_export




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							"PretrainedConfig"				,							SCREAMING_SNAKE_CASE      :							str = "default"				,							SCREAMING_SNAKE_CASE      :							"List[PatchingSpec]" = None):
           super().__init__(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = True


    @property
    def        A   (  self      :							List[str]):
           if self.task == "multiple-choice":
                  _A					:     Tuple     = {0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                  _A					:     List[Any]     = {0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
                   ('global_attention_mask', dynamic_axis),
               ])


    @property
    def        A   (  self      :							str):
           _A					:     int     = super().outputs
           if self.task == "default":
                  _A					:     str     = {0: 'batch'}
           return outputs


    @property
    def        A   (  self      :							List[Any]):
           return 1e-4


    @property
    def        A   (  self      :							Dict):
           # needs to be >= 14 to support tril operator
           return max(super().default_onnx_opset				,							14)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							"PreTrainedTokenizerBase"				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							Optional[TensorType] = None				,							):
           _A					:     Union[str, Any]     = super().generate_dummy_inputs(
               preprocessor=SCREAMING_SNAKE_CASE				,							batch_size=SCREAMING_SNAKE_CASE				,							seq_length=SCREAMING_SNAKE_CASE				,							is_pair=SCREAMING_SNAKE_CASE				,							framework=SCREAMING_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
           _A					:     Tuple     = torch.zeros_like(inputs['input_ids'])
           # make every second token global
           _A					:     Dict     = 1

           return inputs






