






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     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
   if isinstance(__lowercase    ,      __lowercase):
      UpperCamelCase_									=     np.full((len(__lowercase), sequence_length, 2)    ,      __lowercase)
   else:
      UpperCamelCase_									=     np.full((len(__lowercase), sequence_length)    ,      __lowercase)

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

   return out_tensor.tolist()






def     _snake_case    (__lowercase):
   UpperCamelCase_									=     ord(__lowercase)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
      return True
   UpperCamelCase_									=     unicodedata.category(__lowercase)
   if cat.startswith('P'):
      return True
   return False


@dataclass
class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42
  A_						=   True
  A_						=   None
  A_						=   None
  A_						=   -100
  A_						=   "pt"




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[Any]:
     import torch

     UpperCamelCase_									=     'label' if 'label' in features[0].keys() else 'labels'
     UpperCamelCase_									=     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
     UpperCamelCase_									=     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

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

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

     return batch













snake_case__      :	List[Any]       	=      """Tobias Carryer"""

from time import time


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=int(time() ) )			-> Tuple:  # noqa: B008
     UpperCamelCase_									=     multiplier
     UpperCamelCase_									=     increment
     UpperCamelCase_									=     modulo
     UpperCamelCase_									=     seed




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     (self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__      :	List[Any]       	=      LinearCongruentialGenerator(1_6_6_4_5_2_5, 1_0_1_3_9_0_4_2_2_3, 2 << 3_1)
 while True:
  print(lcg.next_number())





