





'''simple docstring'''




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch


class 					SCREAMING_SNAKE_CASE       (_A				):
     lowerCAmelCase						     =			"char"
     lowerCAmelCase						     =			"bpe"
     lowerCAmelCase						     =			"wp"




lowercase__       : List[Any]         =       (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class 					SCREAMING_SNAKE_CASE       (_A				):
     lowerCAmelCase						     =			["image_processor", "char_tokenizer"]
     lowerCAmelCase						     =			"ViTImageProcessor"
     lowerCAmelCase						     =			"MgpstrTokenizer"


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			None
          if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.'		,      __lowerCAmelCase		,      )
               __A		:					str						=			kwargs.pop('feature_extractor')

          __A		:					Optional[int]						=			image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.')
          if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.')

          __A		:					Tuple						=			tokenizer
          __A		:					List[str]						=			AutoTokenizer.from_pretrained('gpt2')
          __A		:					Dict						=			AutoTokenizer.from_pretrained('bert-base-uncased')

          super().__init__(__lowerCAmelCase		,      __lowerCAmelCase)


     def __call__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase):

          '''simple docstring'''






          if images is None and text is None:
               raise ValueError('You need to specify either an `images` or `text` input to process.')

          if images is not None:
               __A		:					Optional[Any]						=			self.image_processor(__lowerCAmelCase		,      return_tensors=__lowerCAmelCase		,      **__lowerCAmelCase)
          if text is not None:
               __A		:					Dict						=			self.char_tokenizer(__lowerCAmelCase		,      return_tensors=__lowerCAmelCase		,      **__lowerCAmelCase)

          if text is None:
               return inputs
          elif images is None:
               return encodings
          else:
               __A		:					Union[str, Any]						=			encodings['input_ids']
               return inputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A					,__A					,__A		:					Union[str, Any]						=			sequences
          __A		:					Optional[int]						=			char_preds.size(0)

          __A					,__A		:					Optional[Any]						=			self._decode_helper(__lowerCAmelCase		,      'char')
          __A					,__A		:					Optional[Any]						=			self._decode_helper(__lowerCAmelCase		,      'bpe')
          __A					,__A		:					Optional[Any]						=			self._decode_helper(__lowerCAmelCase		,      'wp')

          __A		:					str						=			[]
          __A		:					Tuple						=			[]
          for i in range(__lowerCAmelCase):
               __A		:					Union[str, Any]						=			[char_scores[i], bpe_scores[i], wp_scores[i]]
               __A		:					Union[str, Any]						=			[char_strs[i], bpe_strs[i], wp_strs[i]]
               __A		:					Optional[Any]						=			scores.index(max(__lowerCAmelCase))
               final_strs.append(strs[max_score_index])
               final_scores.append(scores[max_score_index])

          __A		:					Union[str, Any]						=			{}
          __A		:					Dict						=			final_strs
          __A		:					Any						=			final_scores
          __A		:					Optional[Any]						=			char_strs
          __A		:					Dict						=			bpe_strs
          __A		:					Optional[Any]						=			wp_strs
          return out


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if format == DecodeType.CHARACTER:
               __A		:					List[str]						=			self.char_decode
               __A		:					Optional[Any]						=			1
               __A		:					List[str]						=			'[s]'
          elif format == DecodeType.BPE:
               __A		:					List[str]						=			self.bpe_decode
               __A		:					List[str]						=			2
               __A		:					List[Any]						=			'#'
          elif format == DecodeType.WORDPIECE:
               __A		:					Optional[Any]						=			self.wp_decode
               __A		:					Tuple						=			102
               __A		:					Union[str, Any]						=			'[SEP]'
          else:
               raise ValueError(F'Format {format} is not supported.')

          __A					,__A		:					str						=			[], []
          __A		:					List[Any]						=			pred_logits.size(0)
          __A		:					Tuple						=			pred_logits.size(1)
          __A					,__A		:					Optional[int]						=			pred_logits.topk(1		,      dim=-1		,      largest=__lowerCAmelCase		,      sorted=__lowerCAmelCase)
          __A		:					Tuple						=			preds_index.view(-1		,      __lowerCAmelCase)[:, 1:]
          __A		:					Dict						=			decoder(__lowerCAmelCase)
          __A					,__A		:					Tuple						=			torch.nn.functional.softmax(__lowerCAmelCase		,      dim=2).max(dim=2)
          __A		:					Any						=			preds_max_prob[:, 1:]

          for index in range(__lowerCAmelCase):
               __A		:					Dict						=			preds_str[index].find(__lowerCAmelCase)
               __A		:					Union[str, Any]						=			preds_str[index][:pred_eos]
               __A		:					int						=			preds_index[index].cpu().tolist()
               __A		:					Tuple						=			pred_index.index(__lowerCAmelCase) if eos_token in pred_index else -1
               __A		:					List[Any]						=			preds_max_prob[index][: pred_eos_index + 1]
               __A		:					int						=			pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
               dec_strs.append(__lowerCAmelCase)
               conf_scores.append(__lowerCAmelCase)

          return dec_strs, conf_scores


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			[seq.replace(' '		,      '') for seq in self.char_tokenizer.batch_decode(__lowerCAmelCase)]
          return decode_strs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return self.bpe_tokenizer.batch_decode(__lowerCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			[seq.replace(' '		,      '') for seq in self.wp_tokenizer.batch_decode(__lowerCAmelCase)]
          return decode_strs




UpperCAmelCase_		 =					2_5_6
# Modulus to hash a string
UpperCAmelCase_		 =					1_0_0_0_0_0_3

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     bool:
   _A      =       len(_snake_case				)
   _A      =       len(_snake_case				)
   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(_snake_case				):
      _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 	SCREAMING_SNAKE_CASE_							(	)  ->     None:
   _A      =       '''abc1abc12'''
   _A      =       '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   _A      =       '''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(_snake_case	,   _snake_case				) and not rabin_karp(_snake_case	,   _snake_case				)

   # Test 2)
   _A      =       '''ABABX'''
   _A      =       '''ABABZABABYABABX'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 3)
   _A      =       '''AAAB'''
   _A      =       '''ABAAAAAB'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 4)
   _A      =       '''abcdabcy'''
   _A      =       '''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 5)
   _A      =       '''Lü'''
   _A      =       '''Lüsai'''
   assert rabin_karp(_snake_case	,   _snake_case				)
   _A      =       '''Lue'''
   assert not rabin_karp(_snake_case	,   _snake_case				)
   print('''Success.'''				)


if __name__ == "__main__":
   test_rabin_karp()



