
import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
      import jieba

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


lowercase__         =logging.get_logger(__name__)

lowercase__         ={'vocab_file': 'vocab.txt'}

lowercase__         ={
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

lowercase__         ={
    'openbmb/cpm-ant-10b': 1024,
}




def   __UpperCamelCase	(							lowerCAmelCase__				:							int    ):
 __a					:    Optional[Any]													=    collections.OrderedDict()
 with open(lowerCAmelCase__    ,  '''r'''    ,  encoding='''utf-8'''    ) as reader:
  __a					:    List[Any]													=    reader.readlines()
 for index, token in enumerate(lowerCAmelCase__    ):
  __a					:    Dict													=    token.rstrip('''\n'''    )
  __a					:    str													=    index
 return vocab



class 						UpperCamelCase__    (			snake_case_       ):
   def __init__(self					:   int   ,							snake_case_					:   List[Any]   ,							snake_case_					:   List[Any]="<unk>"   ,							snake_case_					:   Optional[int]=2_0_0  ):
    __a					:    Dict													=    vocab
    __a					:    Union[str, Any]													=    unk_token
    __a					:    int													=    max_input_chars_per_word







   def  lowerCAmelCase     (self					:   Dict   ,							snake_case_					:   int  ):
    __a					:    List[str]													=    list(UpperCamelCase__  )
    if len(UpperCamelCase__  ) > self.max_input_chars_per_word:
     return [self.unk_token]

    __a					:    Optional[Any]													=    0
    __a					:    Tuple													=    []
    while start < len(UpperCamelCase__  ):
     __a					:    Union[str, Any]													=    len(UpperCamelCase__  )
     __a					:    Tuple													=    None
     while start < end:
      __a					:    Optional[int]													=    "".join(chars[start:end]  )
      if substr in self.vocab:
       __a					:    List[str]													=    substr
       break
      end -= 1
     if cur_substr is None:
      sub_tokens.append(self.unk_token  )
      start += 1
     else:
      sub_tokens.append(UpperCamelCase__  )
      __a					:    str													=    end

    return sub_tokens





class 						UpperCamelCase__    (			snake_case_       ):
   _SCREAMING_SNAKE_CASE :				Any      			= VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE :				Optional[int]      			= PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE :				List[Any]      			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE :				Optional[int]      			= ['''input_ids''', '''attention_mask''']
   _SCREAMING_SNAKE_CASE :				List[Any]      			= False
   def __init__(self					:   Dict   ,							snake_case_					:   int   ,							snake_case_					:   Dict="<d>"   ,							snake_case_					:   List[str]="</d>"   ,							snake_case_					:   Tuple="<s>"   ,							snake_case_					:   Optional[int]="</s>"   ,							snake_case_					:   int="<pad>"   ,							snake_case_					:   Tuple="<unk>"   ,							snake_case_					:   Tuple="</n>"   ,							snake_case_					:   List[Any]="</_>"   ,							snake_case_					:   Tuple="left"   ,							**snake_case_					:   Optional[int]   ,							):
    requires_backends(self   ,							['''jieba''']  )
    super().__init__(
        bod_token=UpperCamelCase__   ,							eod_token=UpperCamelCase__   ,							bos_token=UpperCamelCase__   ,							eos_token=UpperCamelCase__   ,							pad_token=UpperCamelCase__   ,							unk_token=UpperCamelCase__   ,							line_token=UpperCamelCase__   ,							space_token=UpperCamelCase__   ,							padding_side=UpperCamelCase__   ,							**UpperCamelCase__   ,							)
    __a					:    str													=    bod_token
    __a					:    Optional[Any]													=    eod_token
    __a					:    Union[str, Any]													=    load_vocab(UpperCamelCase__  )
    __a					:    Optional[Any]													=    self.encoder[space_token]
    __a					:    Optional[int]													=    self.encoder[line_token]

    del self.encoder[space_token]
    del self.encoder[line_token]

    __a					:    List[Any]													=    collections.OrderedDict(sorted(self.encoder.items()   ,							key=lambda snake_case_  : x[1]  )  )
    __a					:    Dict													=    {v: k for k, v in self.encoder.items()}

    __a					:    List[Any]													=    WordpieceTokenizer(vocab=self.encoder   ,							unk_token=self.unk_token  )
   @property
   def  lowerCAmelCase     (self					:   Tuple  ):
    return self.encoder[self.bod_token]
   @property
   def  lowerCAmelCase     (self					:   Union[str, Any]  ):
    return self.encoder[self.eod_token]
   @property
   def  lowerCAmelCase     (self					:   Optional[int]  ):
    return self.encoder["\n"]
   @property
   def  lowerCAmelCase     (self					:   str  ):
    return len(self.encoder  )
   def  lowerCAmelCase     (self					:   Tuple  ):
    return dict(self.encoder   ,							**self.added_tokens_encoder  )
   def  lowerCAmelCase     (self					:   Optional[Any]   ,							snake_case_					:   Dict  ):
    __a					:    Any													=    []
    for x in jieba.cut(UpperCamelCase__   ,							cut_all=UpperCamelCase__  ):
     output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase__  )  )
    return output_tokens
   def  lowerCAmelCase     (self					:   Optional[Any]   ,							snake_case_					:   List[str]   ,							**snake_case_					:   List[Any]  ):
    __a					:    Dict													=    [i for i in token_ids if i >= 0]
    __a					:    Dict													=    [
        x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
    ]
    return super()._decode(UpperCamelCase__   ,							**UpperCamelCase__  )
   def  lowerCAmelCase     (self					:   Optional[Any]   ,							snake_case_					:   List[str]  ):
    return token in self.encoder
   def  lowerCAmelCase     (self					:   List[Any]   ,							snake_case_					:   Union[str, Any]  ):
    return "".join(UpperCamelCase__  )
   def  lowerCAmelCase     (self					:   Optional[Any]   ,							snake_case_					:   Optional[Any]  ):
    return self.encoder.get(UpperCamelCase__   ,							self.encoder.get(self.unk_token  )  )
   def  lowerCAmelCase     (self					:   Optional[int]   ,							snake_case_					:   Any  ):
    return self.decoder.get(UpperCamelCase__   ,							self.unk_token  )
   def  lowerCAmelCase     (self					:   Tuple   ,							snake_case_					:   Union[str, Any]   ,							snake_case_					:   Union[str, Any] = None  ):




    if os.path.isdir(UpperCamelCase__  ):
     __a					:    List[Any]													=    os.path.join(
         UpperCamelCase__   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
    else:
     __a					:    List[Any]													=    (filename_prefix + "-" if filename_prefix else "") + save_directory
    __a					:    List[str]													=    0
    if " " in self.encoder:
     __a					:    Any													=    self.encoder[" "]
     del self.encoder[" "]
    if "\n" in self.encoder:
     __a					:    List[str]													=    self.encoder["\n"]
     del self.encoder["\n"]
    __a					:    Optional[Any]													=    collections.OrderedDict(sorted(self.encoder.items()   ,							key=lambda snake_case_  : x[1]  )  )
    with open(UpperCamelCase__   ,							'''w'''   ,							encoding='''utf-8'''  ) as writer:
     for token, token_index in self.encoder.items():
      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					:    Any													=    token_index
      writer.write(token + '''\n'''  )
      index += 1
    return (vocab_file,)
   def  lowerCAmelCase     (self					:   Union[str, Any]   ,							snake_case_					:   int   ,							snake_case_					:   List[Any] = None  ):




    if token_ids_a is None:
     return [self.bos_token_id] + token_ids_a
    return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a







   def  lowerCAmelCase     (self					:   str   ,							snake_case_					:   Union[str, Any]   ,							snake_case_					:   List[Any] = None   ,							snake_case_					:   Tuple = False  ):




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

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




"""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


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """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							( snake_case_      ):
   __UpperCAmelCase					:			Tuple				    =    '''longformer'''







   def __init__(      self		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 0		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 3_0522		,      UpperCamelCase__ = 768		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 3072		,      UpperCamelCase__ = "gelu"		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 0.02		,      UpperCamelCase__ = 1e-12		,      UpperCamelCase__ = False		,      **UpperCamelCase__		,      )      ->						str:

       '''simple docstring'''




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

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







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__ = "default"		,      UpperCamelCase__ = None		)      ->						List[str]:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
       snake_case       : int    =       True







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




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







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




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







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 1e-4







   @property
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return max(super().default_onnx_opset		,      14		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      )      ->						Mapping[str, Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       super().generate_dummy_inputs(
           preprocessor=UpperCamelCase__		,      batch_size=UpperCamelCase__		,      seq_length=UpperCamelCase__		,      is_pair=UpperCamelCase__		,      framework=UpperCamelCase__		)
       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
       snake_case       : Any    =       torch.zeros_like(inputs["input_ids"]		)
       # make every second token global
       snake_case       : Any    =       1

       return inputs





