
"""simple docstring"""




from __future__ import annotations



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						get_failure_array(lowerCAmelCase		)

     # 2) Step through text searching for pattern
     UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						0, 0  # index into text, pattern
     while i < len(lowerCAmelCase		):
          if pattern[j] == text[i]:
               if j == (len(lowerCAmelCase		) - 1):
                    return True
               j += 1

          # if this is a prefix in our pattern
          # just go back far enough to continue
          elif j > 0:
               UpperCAmelCase__							: Optional[Any] 							=						failure[j - 1]
               continue
          i += 1
     return False



def 							a__ (				lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						[0]
     UpperCAmelCase__							: int 							=						0
     UpperCAmelCase__							: List[Any] 							=						1
     while j < len(lowerCAmelCase		):
          if pattern[i] == pattern[j]:
               i += 1
          elif i > 0:
               UpperCAmelCase__							: Union[str, Any] 							=						failure[i - 1]
               continue
          j += 1
          failure.append(lowerCAmelCase		)
     return failure


if __name__ == "__main__":
    # Test 1)
    A__      :	Tuple          =    """abc1abc12"""
    A__      :	Tuple          =    """alskfjaldsabc1abc1abc12k23adsfabcabc"""
    A__      :	List[Any]          =    """alskfjaldsk23adsfabcabc"""
    assert kmp(pattern, texta) and not kmp(pattern, texta)

    # Test 2)
    A__      :	str          =    """ABABX"""
    A__      :	List[str]          =    """ABABZABABYABABX"""
    assert kmp(pattern, text)

    # Test 3)
    A__      :	Optional[int]          =    """AAAB"""
    A__      :	Optional[int]          =    """ABAAAAAB"""
    assert kmp(pattern, text)

    # Test 4)
    A__      :	Union[str, Any]          =    """abcdabcy"""
    A__      :	Tuple          =    """abcxabcdabxabcdabcdabcy"""
    assert kmp(pattern, text)

    # Test 5)
    A__      :	Tuple          =    """aabaabaaa"""
    assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]





"""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__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     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__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						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__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



