from __future__ import annotations



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				List[str]:
         A						:     List[str]         =text, pattern
         A						:     Any         =len(__A     ), len(__A     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				Optional[int]:

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Tuple:

         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 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Union[str, Any]:
         # searches pattern in text and returns index positions
         A						:     List[str]         =[]
         for i in range(self.textLen - self.patLen + 1     ):
                A						:     int         =self.mismatch_in_text(__A     )
                if mismatch_index == -1:
                       positions.append(__A     )
                else:
                       A						:     List[str]         =self.match_in_pattern(self.text[mismatch_index]     )
                       A						:     List[str]         =(
                           mismatch_index - match_index
                       )  # shifting index lgtm [py/multiple-definition]
         return positions







_lowercase							:      int      							='ABAABA'
_lowercase							:      List[str]      							='AB'
_lowercase							:      Tuple      							=BoyerMooreSearch(text, pattern)
_lowercase							:      List[str]      							=bms.bad_character_heuristic()

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







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






