

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def 						UpperCamelCase (lowercase_:			str )					->				None:
 A__       ,       A__							:   List[str]					       =		analyze_text(lowercase_ )
 A__							:   Any					       =		list(""" """ + ascii_lowercase )
 # what is our total sum of probabilities.
 A__							:   List[str]					       =		sum(single_char_strings.values() )

 # one length string
 A__							:   List[Any]					       =		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:
   A__							:   int					       =		single_char_strings[ch]
   A__							:   Tuple					       =		my_str / all_sum
   my_fir_sum += prob * math.loga(lowercase_ )  # entropy formula.

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

 # two len string
 A__							:   List[str]					       =		sum(two_char_strings.values() )
 A__							:   Tuple					       =		0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   A__							:   List[str]					       =		cha + cha
   if sequence in two_char_strings:
    A__							:   Tuple					       =		two_char_strings[sequence]
    A__							:   str					       =		int(lowercase_ ) / all_sum
    my_sec_sum += prob * math.loga(lowercase_ )

    # 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 						UpperCamelCase (lowercase_:			str )					->				tuple[dict, dict]:
 A__							:   int					       =		Counter()  # type: ignore
 A__							:   Optional[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(lowercase_ ) - 1 ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings

def 						UpperCamelCase ()					->				Union[str, Any]:
 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()






from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	help="""Path to location to store the models"""      )
						download_parser.add_argument(
						    """--force"""   ,	action="""store_true"""   ,	help="""Force the model to be download even if already in cache-dir"""      )
						download_parser.add_argument(
						    """--trust-remote-code"""   ,	action="""store_true"""   ,	help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,	)
						download_parser.add_argument("""model"""   ,	type=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		trust_remote_code






					def   __A      (			self      ):
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )
						AutoTokenizer.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )



