




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			Tuple	,					__A    :			List[str]	,					__A    :			Optional[Any]	,					__A    :			List[str]  )			->			List[str]:
   if isinstance(__lowerCAmelCase	,					__lowerCAmelCase  ):
      _SCREAMING_SNAKE_CASE	=				np.full((len(__lowerCAmelCase  ), sequence_length, 2)	,					__lowerCAmelCase  )
   else:
      _SCREAMING_SNAKE_CASE	=				np.full((len(__lowerCAmelCase  ), sequence_length)	,					__lowerCAmelCase  )

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

   return out_tensor.tolist()
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				ord(__lowerCAmelCase  )
   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
   _SCREAMING_SNAKE_CASE	=				unicodedata.category(__lowerCAmelCase  )
   if cat.startswith("P"  ):
      return True
   return False







@dataclass
class 		lowercase_   (			UpperCamelCase_       ):


   """simple docstring"""

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



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      import torch

      _SCREAMING_SNAKE_CASE	=				"""label""" if """label""" in features[0].keys() else """labels"""
      _SCREAMING_SNAKE_CASE	=				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _SCREAMING_SNAKE_CASE	=				self.tokenizer.pad(
          _a  ,				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

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

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

      return batch







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

