



from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_			=					logging.get_logger(__name__)


a_			=					{
    """nielsr/canine-s""": 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
a_			=					1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
a_			=					0

a_			=					0xe_0_0_0
a_			=					0xe_0_0_1
a_			=					0xe_0_0_2
a_			=					0xe_0_0_3
a_			=					0xe_0_0_4

# Maps special codepoints to human-readable names.
a_			=					{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
a_			=					{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




 def __init__(						self	,				__UpperCAmelCase=chr(__UpperCAmelCase				)	,				__UpperCAmelCase=chr(__UpperCAmelCase				)	,				__UpperCAmelCase=chr(__UpperCAmelCase				)	,				__UpperCAmelCase=chr(__UpperCAmelCase				)	,				__UpperCAmelCase=chr(__UpperCAmelCase				)	,				__UpperCAmelCase=chr(__UpperCAmelCase				)	,				__UpperCAmelCase=False	,				__UpperCAmelCase=2048	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    AddedToken(__UpperCAmelCase	,				lstrip=__UpperCAmelCase	,				rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				) else bos_token
    __lowerCamelCase													=    AddedToken(__UpperCAmelCase	,				lstrip=__UpperCAmelCase	,				rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				) else eos_token
    __lowerCamelCase													=    AddedToken(__UpperCAmelCase	,				lstrip=__UpperCAmelCase	,				rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				) else sep_token
    __lowerCamelCase													=    AddedToken(__UpperCAmelCase	,				lstrip=__UpperCAmelCase	,				rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				) else cls_token
    __lowerCamelCase													=    AddedToken(__UpperCAmelCase	,				lstrip=__UpperCAmelCase	,				rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    __lowerCamelCase													=    AddedToken(__UpperCAmelCase	,				lstrip=__UpperCAmelCase	,				rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				) else mask_token

    super().__init__(
        bos_token=__UpperCAmelCase	,				eos_token=__UpperCAmelCase	,				sep_token=__UpperCAmelCase	,				cls_token=__UpperCAmelCase	,				pad_token=__UpperCAmelCase	,				mask_token=__UpperCAmelCase	,				add_prefix_space=__UpperCAmelCase	,				model_max_length=__UpperCAmelCase	,				**__UpperCAmelCase	,				)

    # Creates a mapping for looking up the IDs of special symbols.
    __lowerCamelCase													=    {}
    for codepoint, name in SPECIAL_CODEPOINTS.items():
       __lowerCamelCase													=    codepoint

    # Creates a mapping for looking up the string forms of special symbol IDs.
    __lowerCamelCase													=    {
        codepoint: name for name, codepoint in self._special_codepoints.items()
    }

    __lowerCamelCase													=    UNICODE_VOCAB_SIZE
    __lowerCamelCase													=    len(self._special_codepoints				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._unicode_vocab_size




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return list(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    try:
       return ord(__UpperCAmelCase				)
    except TypeError:
       raise ValueError(F"""invalid token: '{token}'"""				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    try:
       if index in SPECIAL_CODEPOINTS:
          return SPECIAL_CODEPOINTS[index]
       return chr(__UpperCAmelCase				)
    except TypeError:
       raise ValueError(F"""invalid id: {index}"""				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return "".join(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    __lowerCamelCase													=    [self.sep_token_id]
    __lowerCamelCase													=    [self.cls_token_id]

    __lowerCamelCase													=    cls + token_ids_a + sep
    if token_ids_a is not None:
       result += token_ids_a + sep
    return result




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				__UpperCAmelCase = False				):



    '''simple docstring'''
    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__UpperCAmelCase	,				token_ids_a=__UpperCAmelCase	,				already_has_special_tokens=__UpperCAmelCase				)

    __lowerCamelCase													=    [1] + ([0] * len(__UpperCAmelCase				)) + [1]
    if token_ids_a is not None:
       result += ([0] * len(__UpperCAmelCase				)) + [1]
    return result




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    __lowerCamelCase													=    [self.sep_token_id]
    __lowerCamelCase													=    [self.cls_token_id]

    __lowerCamelCase													=    len(cls + token_ids_a + sep				) * [0]
    if token_ids_a is not None:
       result += len(token_ids_a + sep				) * [1]
    return result




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    return ()











import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				padding=self.padding	,				max_length=self.max_length	,				pad_to_multiple_of=self.pad_to_multiple_of	,				return_tensors='''pt''' if labels is None else None	,				)

    if labels is None:
       return batch

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






