


'''simple docstring'''
from __future__ import annotations


def       _lowercase							(					UpperCamelCase__							:       str,					UpperCamelCase__							:       str      ):
 __A	:		List[str]	  =      get_failure_array(UpperCamelCase__      )

 # 2) Step through text searching for pattern
 __A  ,__A	:		List[Any]	  =      0, 0  # index into text, pattern
 while i < len(UpperCamelCase__      ):
  if pattern[j] == text[i]:
   if j == (len(UpperCamelCase__      ) - 1):
    return True
   j += 1

  # if this is a prefix in our pattern
  # just go back far enough to continue
  elif j > 0:
   __A	:		str	  =      failure[j - 1]
   continue
  i += 1
 return False


def       _lowercase							(					UpperCamelCase__							:       str      ):
 __A	:		Optional[int]	  =      [0]
 __A	:		Union[str, Any]	  =      0
 __A	:		List[str]	  =      1
 while j < len(UpperCamelCase__      ):
  if pattern[i] == pattern[j]:
   i += 1
  elif i > 0:
   __A	:		int	  =      failure[i - 1]
   continue
  j += 1
  failure.append(UpperCamelCase__      )
 return failure


if __name__ == "__main__":
 # Test 1)
 UpperCAmelCase_			:  Optional[Any]										=   'abc1abc12'
 UpperCAmelCase_			:  Optional[int]										=   'alskfjaldsabc1abc1abc12k23adsfabcabc'
 UpperCAmelCase_			:  Tuple										=   'alskfjaldsk23adsfabcabc'
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 UpperCAmelCase_			:  Dict										=   'ABABX'
 UpperCAmelCase_			:  Optional[Any]										=   'ABABZABABYABABX'
 assert kmp(pattern, text)

 # Test 3)
 UpperCAmelCase_			:  Tuple										=   'AAAB'
 UpperCAmelCase_			:  Tuple										=   'ABAAAAAB'
 assert kmp(pattern, text)

 # Test 4)
 UpperCAmelCase_			:  Tuple										=   'abcdabcy'
 UpperCAmelCase_			:  str										=   'abcxabcdabxabcdabcdabcy'
 assert kmp(pattern, text)

 # Test 5)
 UpperCAmelCase_			:  Union[str, Any]										=   'aabaabaaa'
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]










'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






