



'''simple docstring'''





import itertools
import string
from collections.abc import Generator, Iterable





def 		a__			(		_UpperCamelCase			: Iterable[str] ,_UpperCamelCase			: int							):
   __lowerCamelCase													=    iter(UpperCamelCase__							)
   while True:
      __lowerCamelCase													=    tuple(itertools.islice(UpperCamelCase__ ,UpperCamelCase__							)							)
      if not chunk:
         return
      yield chunk





def 		a__			(		_UpperCamelCase			: str							):
   __lowerCamelCase													=    ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]							)
   __lowerCamelCase													=    ''''''

   if len(UpperCamelCase__							) < 2:
      return dirty

   for i in range(len(UpperCamelCase__							) - 1							):
      clean += dirty[i]

      if dirty[i] == dirty[i + 1]:
         clean += "X"

   clean += dirty[-1]

   if len(UpperCamelCase__							) & 1:
      clean += "X"

   return clean





def 		a__			(		_UpperCamelCase			: str							):
   # I and J are used interchangeably to allow
   # us to use a 5x5 table (25 letters)
   __lowerCamelCase													=    '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
   # we're using a list instead of a '2d' array because it makes the math
   # for setting up the table and doing the actual encoding/decoding simpler
   __lowerCamelCase													=    []

   # copy key chars into the table if they are in `alphabet` ignoring duplicates
   for char in key.upper():
      if char not in table and char in alphabet:
         table.append(UpperCamelCase__							)

    # fill the rest of the table in with the remaining alphabet chars
   for char in alphabet:
      if char not in table:
         table.append(UpperCamelCase__							)

   return table





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str							):
   __lowerCamelCase													=    generate_table(UpperCamelCase__							)
   __lowerCamelCase													=    prepare_input(UpperCamelCase__							)
   __lowerCamelCase													=    ''''''

   # https://en.wikipedia.org/wiki/Playfair_cipher#Description
   for chara, chara in chunker(UpperCamelCase__ ,2							):
      __lowerCamelCase							,__lowerCamelCase													=    divmod(table.index(UpperCamelCase__							) ,5							)
      __lowerCamelCase							,__lowerCamelCase													=    divmod(table.index(UpperCamelCase__							) ,5							)

      if rowa == rowa:
         ciphertext += table[rowa * 5 + (cola + 1) % 5]
         ciphertext += table[rowa * 5 + (cola + 1) % 5]
      elif cola == cola:
         ciphertext += table[((rowa + 1) % 5) * 5 + cola]
         ciphertext += table[((rowa + 1) % 5) * 5 + cola]
      else:  # rectangle
         ciphertext += table[rowa * 5 + cola]
         ciphertext += table[rowa * 5 + cola]

   return ciphertext





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str							):
   __lowerCamelCase													=    generate_table(UpperCamelCase__							)
   __lowerCamelCase													=    ''''''

   # https://en.wikipedia.org/wiki/Playfair_cipher#Description
   for chara, chara in chunker(UpperCamelCase__ ,2							):
      __lowerCamelCase							,__lowerCamelCase													=    divmod(table.index(UpperCamelCase__							) ,5							)
      __lowerCamelCase							,__lowerCamelCase													=    divmod(table.index(UpperCamelCase__							) ,5							)

      if rowa == rowa:
         plaintext += table[rowa * 5 + (cola - 1) % 5]
         plaintext += table[rowa * 5 + (cola - 1) % 5]
      elif cola == cola:
         plaintext += table[((rowa - 1) % 5) * 5 + cola]
         plaintext += table[((rowa - 1) % 5) * 5 + cola]
      else:  # rectangle
         plaintext += table[rowa * 5 + cola]
         plaintext += table[rowa * 5 + cola]

   return plaintext











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






