

"""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  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       List[Any],			UpperCamelCase_     :       Any,			UpperCamelCase_     :       List[str])	->						Optional[int]:
   '''simple docstring'''


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

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

   return out_tensor.tolist()




def   _A  (  UpperCamelCase_     :       Dict)	->						str:
   '''simple docstring'''


   __lowercase								=						ord(UpperCamelCase_)
   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(UpperCamelCase_)
   if cat.startswith("P"):
      return True
   return False




@dataclass
class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







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


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[str]	):
    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(
        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

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

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

    return batch






SCREAMING_SNAKE_CASE							:Any							 =				256
# Modulus to hash a string
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				100_0003


def 						UpperCAmelCase			(	a_		,   a_						)		->	bool:







				"""simple docstring"""



				__A   			= len(a_						)
				__A   			= len(a_						)
				if p_len > t_len:
								return False

				__A   			= 0
				__A   			= 0
				__A   			= 1

				# Calculating the hash of pattern and substring of text
				for i in range(a_						):
								__A   			= (ord(pattern[i]						) + p_hash * alphabet_size) % modulus
								__A   			= (ord(text[i]						) + text_hash * alphabet_size) % modulus
								if i == p_len - 1:
												continue
								__A   			= (modulus_power * alphabet_size) % modulus

				for i in range(0		,   t_len - p_len + 1						):
								if text_hash == p_hash and text[i : i + p_len] == pattern:
												return True
								if i == t_len - p_len:
												continue
								# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
								__A   			= (
								    (text_hash - ord(text[i]						) * modulus_power) * alphabet_size
								    + ord(text[i + p_len]						)
								) % modulus
				return False


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= "abc1abc12"
				__A   			= "alskfjaldsabc1abc1abc12k23adsfabcabc"
				__A   			= "alskfjaldsk23adsfabcabc"
				assert rabin_karp(a_		,   a_						) and not rabin_karp(a_		,   a_						)

				# Test 2)
				__A   			= "ABABX"
				__A   			= "ABABZABABYABABX"
				assert rabin_karp(a_		,   a_						)

				# Test 3)
				__A   			= "AAAB"
				__A   			= "ABAAAAAB"
				assert rabin_karp(a_		,   a_						)

				# Test 4)
				__A   			= "abcdabcy"
				__A   			= "abcxabcdabxabcdabcdabcy"
				assert rabin_karp(a_		,   a_						)

				# Test 5)
				__A   			= "Lü"
				__A   			= "Lüsai"
				assert rabin_karp(a_		,   a_						)
				__A   			= "Lue"
				assert not rabin_karp(a_		,   a_						)
				print("Success."						)


if __name__ == "__main__":
					test_rabin_karp()
