



# 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       _SCREAMING_SNAKE_CASE	:





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = False				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	scheduler
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	optimizers if isinstance(UpperCamelCase     ,  (list, tuple)				) else [optimizers]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	split_batches
      __SCREAMING_SNAKE_CASE       :    int       =	step_with_optimizer
      __SCREAMING_SNAKE_CASE       :    Any       =	GradientState()





     def 	__snake_case    (       self :							Any     ,  *UpperCamelCase :							List[Any]     ,  **UpperCamelCase :							Optional[Any]				)->Optional[int]:
      if not self.step_with_optimizer:
       # No link between scheduler and optimizer -> just step
       self.scheduler.step(*UpperCamelCase     ,  **UpperCamelCase				)
       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(*UpperCamelCase     ,  **UpperCamelCase				)
      else:
       # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
       # num_processes steps per training step
       __SCREAMING_SNAKE_CASE       :    Any       =	AcceleratorState().num_processes
       for _ in range(UpperCamelCase				):
        # 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(*UpperCamelCase     ,  **UpperCamelCase				)
        else:
         self.scheduler.step(*UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Dict				)->Dict:
      return self.scheduler.get_last_lr()





     def 	__snake_case    (       self :							List[str]				)->Dict:
      return self.scheduler.state_dict()





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Any				)->Union[str, Any]:
      self.scheduler.load_state_dict(UpperCamelCase				)





     def 	__snake_case    (       self :							Union[str, Any]				)->Optional[int]:
      return self.scheduler.get_lr()






     def 	__snake_case    (       self :							Optional[Any]     ,  *UpperCamelCase :							Dict     ,  **UpperCamelCase :							List[str]				)->Optional[Any]:
      return self.scheduler.print_lr(*UpperCamelCase     ,  **UpperCamelCase				)











from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	analyze_text(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(" " + ascii_lowercase     )
 # what is our total sum of probabilities.
 __SCREAMING_SNAKE_CASE       :    int       =	sum(single_char_strings.values()     )

 # one length string
 __SCREAMING_SNAKE_CASE       :    str       =	0
 # for each alpha we go in our dict and if it is in it we calculate entropy
 for ch in my_alphas:
  if ch in single_char_strings:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	single_char_strings[ch]
   __SCREAMING_SNAKE_CASE       :    str       =	my_str / all_sum
   my_fir_sum += prob * math.loga(__lowerCamelCase     )  # entropy formula.

    # print entropy
 print(F"""{round(-1 * my_fir_sum     ):.1f}"""     )

 # two len string
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum(two_char_strings.values()     )
 __SCREAMING_SNAKE_CASE       :    int       =	0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   __SCREAMING_SNAKE_CASE       :    int       =	cha + cha
   if sequence in two_char_strings:
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	two_char_strings[sequence]
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(__lowerCamelCase     ) / all_sum
    my_sec_sum += prob * math.loga(__lowerCamelCase     )

    # print second entropy
 print(F"""{round(-1 * my_sec_sum     ):.1f}"""     )

 # print the difference between them
 print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	Counter()  # type: ignore
 __SCREAMING_SNAKE_CASE       :    int       =	Counter()  # type: ignore
 single_char_strings[text[-1]] += 1

 # first case when we have space at start.
 two_char_strings[" " + text[0]] += 1
 for i in range(0		,	len(__lowerCamelCase     ) - 1     ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 import doctest

 doctest.testmod()
 # text = (
 #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
 #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
 #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
 #     "beloved evident through no service elderly is. Blind there if every no so "
 #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
 #     "of message cottage windows do besides against uncivil.  Delightful "
 #     "unreserved impossible few estimating men favourable see entreaties. She "
 #     "propriety immediate was improving. He or entrance humoured likewise "
 #     "moderate. Much nor game son say feel. Fat make met can must form into "
 #     "gate. Me we offending prevailed discovery. "
 # )

 # calculate_prob(text)


if __name__ == "__main__":
      main()






