

'''simple docstring'''






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  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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

 return out_tensor.tolist()


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				ord(A__   )
 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
 __lowercase													=				unicodedata.category(A__   )
 if cat.startswith('''P'''   ):
  return True
 return False






@dataclass
class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						PreTrainedTokenizerBase
  SCREAMING_SNAKE_CASE					:						Union[bool, str, PaddingStrategy]           =    True
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    None
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    None
  SCREAMING_SNAKE_CASE					:						int           =    -1_0_0
  SCREAMING_SNAKE_CASE					:						str           =    "pt"






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Any    ):
   import torch

   __lowercase													=				'''label''' if '''label''' in features[0].keys() else '''labels'''
   __lowercase													=				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
   __lowercase													=				self.tokenizer.pad(
       lowercase__      ,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

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

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

   return batch




'''simple docstring'''






from collections.abc import Callable

import numpy as np


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				int(np.ceil((x_end - xa) / step_size   )   )
 __lowercase													=				np.zeros((n + 1,)   )
 __lowercase													=				ya
 __lowercase													=				xa

 for k in range(A__   ):
  __lowercase													=				y[k] + step_size * ode_func(A__  ,       y[k]   )
  x += step_size

 return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()

