


'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''distilbert'''
     UpperCAmelCase 					=				{
         '''hidden_size''': '''dim''',
         '''num_attention_heads''': '''n_heads''',
         '''num_hidden_layers''': '''n_layers''',
     }






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=512     ,							__UpperCamelCase=False     ,							__UpperCamelCase=6     ,							__UpperCamelCase=12     ,							__UpperCamelCase=768     ,							__UpperCamelCase=4 * 768     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.2     ,							__UpperCamelCase=0     ,							**__UpperCamelCase     ,							)       ->    Tuple:
          _a						=		vocab_size
          _a						=		max_position_embeddings
          _a						=		sinusoidal_pos_embds
          _a						=		n_layers
          _a						=		n_heads
          _a						=		dim
          _a						=		hidden_dim
          _a						=		dropout
          _a						=		attention_dropout
          _a						=		activation
          _a						=		initializer_range
          _a						=		qa_dropout
          _a						=		seq_classif_dropout
          super().__init__(**__UpperCamelCase     ,							pad_token_id=__UpperCamelCase		)







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a						=		{0: "batch", 1: "choice", 2: "sequence"}
          else:
               _a						=		{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]		)





'''simple docstring'''

lowercase__				     =		256
# Modulus to hash a string
lowercase__				     =		1_000_003






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     bool:







     '''simple docstring'''






     _a						=		len(__lowerCamelCase				)
     _a						=		len(__lowerCamelCase				)
     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(__lowerCamelCase				):
          _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(__lowerCamelCase      ,  __lowerCamelCase				) and not rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 2)
     _a						=		"ABABX"
     _a						=		"ABABZABABYABABX"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 3)
     _a						=		"AAAB"
     _a						=		"ABAAAAAB"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 4)
     _a						=		"abcdabcy"
     _a						=		"abcxabcdabxabcdabcdabcy"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

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


if __name__ == "__main__":
   test_rabin_karp()

