




'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_albert import AlbertTokenizer
else:
		_SCREAMING_SNAKE_CASE							:   int      		= None

_SCREAMING_SNAKE_CASE							:   Any      		= logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE							:   Optional[Any]      		= {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE							:   Dict      		= {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_SCREAMING_SNAKE_CASE							:   str      		= "▁"




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      Union[str, Any]						       =    VOCAB_FILES_NAMES
			lowerCAmelCase_     :      Union[str, Any]						       =    PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_     :      List[str]						       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_     :      List[str]						       =    AlbertTokenizer




			def __init__(					self				,							a__=None				,							a__=None				,							a__=True				,							a__=True				,							a__=False				,							a__="[CLS]"				,							a__="[SEP]"				,							a__="<unk>"				,							a__="[SEP]"				,							a__="<pad>"				,							a__="[CLS]"				,							a__="[MASK]"				,							**a__				,							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      (
										    AddedToken(a__				,							lstrip=a__				,							rstrip=a__				,							normalized=a__							)
										    if isinstance(a__				,							a__							)
										    else mask_token
										)

										super().__init__(
										    a__				,							tokenizer_file=a__				,							do_lower_case=a__				,							remove_space=a__				,							keep_accents=a__				,							bos_token=a__				,							eos_token=a__				,							unk_token=a__				,							sep_token=a__				,							pad_token=a__				,							cls_token=a__				,							mask_token=a__				,							**a__				,							)

										snake_case_						=      do_lower_case
										snake_case_						=      remove_space
										snake_case_						=      keep_accents
										snake_case_						=      vocab_file
										snake_case_						=      False if not self.vocab_file else True




			def    lowerCAmelCase__				(					self				,							a__				,							a__ = None							)				->				List[int]:

										'''simple docstring'''




										snake_case_						=      [self.sep_token_id]
										snake_case_						=      [self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep




			def    lowerCAmelCase__				(					self				,							a__				,							a__ = None							)				->				List[int]:

										'''simple docstring'''




										snake_case_						=      [self.sep_token_id]
										snake_case_						=      [self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
										return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]




			def    lowerCAmelCase__				(					self				,							a__				,							a__ = None							)				->				Tuple[str]:

										'''simple docstring'''




										if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																	    "tokenizer."							)

										if not os.path.isdir(a__							):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																	return
										snake_case_						=      os.path.join(
										    a__				,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(a__							):
																	copyfile(self.vocab_file				,							a__							)

										return (out_vocab_file,)








from __future__ import annotations


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   List[Any]:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      text, pattern
									lowercase__     ,		lowercase__            =      len(UpperCamelCase_							), len(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if char == self.pattern[i]:
													return i
									return -1

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if self.pattern[i] != self.text[current_pos + i]:
													return current_pos + i
									return -1


							def       lowerCamelCase_ (							self:	List[Any]							)			->   list[int]:





									"""simple docstring"""







									lowercase__            =      []
									for i in range(self.textLen - self.patLen + 1							):
											lowercase__            =      self.mismatch_in_text(UpperCamelCase_							)
											if mismatch_index == -1:
													positions.append(UpperCamelCase_							)
											else:
													lowercase__            =      self.match_in_pattern(self.text[mismatch_index]							)
													lowercase__            =      (
													    mismatch_index - match_index
													)  # shifting index lgtm [py/multiple-definition]
									return positions
lowerCAmelCase       =							'ABAABA'
lowerCAmelCase       =							'AB'
lowerCAmelCase       =							BoyerMooreSearch(text, pattern)
lowerCAmelCase       =							bms.bad_character_heuristic()

if len(positions) == 0:
			print('No match found')
else:
			print('Pattern found in following positions: ')
			print(positions)


