



from __future__ import annotations

class 					_A :



       def __init__(	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  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							:  Tuple):


           '''simple docstring'''

           __a		 =       []
           for i in range(self.textLen - self.patLen + 1):
               __a		 =       self.mismatch_in_text(UpperCamelCase_)
               if mismatch_index == -1:
                   positions.append(UpperCamelCase_)
               else:
                   __a		 =       self.match_in_pattern(self.text[mismatch_index])
                   __a		 =       (
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
           return positions


__snake_case		:List[str]   	=  '''ABAABA'''
__snake_case		:Optional[Any]   	=  '''AB'''
__snake_case		:Tuple   	=  BoyerMooreSearch(text, pattern)
__snake_case		:Union[str, Any]   	=  bms.bad_character_heuristic()

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










from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class 					_A (			__UpperCAmelCase					):



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
           return Dataset.from_dict(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(dset.column_names  ,	['''col_1''', '''col_2'''])
           for i, r in enumerate(__SCREAMING_SNAKE_CASE):
               self.assertDictEqual(__SCREAMING_SNAKE_CASE  ,	example_records[i])



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           __a		 =       Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
           self.assertEqual(dset.info  ,	dset_from_dict.info)



       def 	_lowerCamelCase    (	self							:  int):  # checks what happens with missing columns


           '''simple docstring'''

           __a		 =       [{'''col_1''': 1}, {'''col_2''': '''x'''}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertDictEqual(dset[0]  ,	{'''col_1''': 1})
           self.assertDictEqual(dset[1]  ,	{'''col_1''': None})  # NB: first record is used for columns



       def 	_lowerCamelCase    (	self							:  Optional[Any]):  # checks if the type can be inferred from the second record


           '''simple docstring'''

           __a		 =       [{'''col_1''': []}, {'''col_1''': [1, 2]}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertEqual(dset.info.features['''col_1''']  ,	Sequence(Value('''int64''')))





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       Dataset.from_list([])
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	0)
           self.assertListEqual(dset.column_names  ,	[])





