





"""simple docstring"""






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			None:



			'''simple docstring'''
			a__ ,	a__  :	Optional[Any]		 =			analyze_text(lowerCAmelCase__			)
			a__  :	List[str]		 =			list(" " + ascii_lowercase			)
			# what is our total sum of probabilities.
			a__  :	List[str]		 =			sum(single_char_strings.values()			)

			# one length string
			a__  :	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:
									a__  :	List[str]		 =			single_char_strings[ch]
									a__  :	Optional[int]		 =			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
			a__  :	Dict		 =			sum(two_char_strings.values()			)
			a__  :	Dict		 =			0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									a__  :	Optional[Any]		 =			cha + cha
									if sequence in two_char_strings:
												a__  :	List[str]		 =			two_char_strings[sequence]
												a__  :	List[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      lowercase__ (					lowerCAmelCase__   :			str			)    ->			tuple[dict, dict]:



			'''simple docstring'''
			a__  :	Tuple		 =			Counter()  # type: ignore
			a__  :	List[Any]		 =			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      lowercase__ (					)    ->			int:



			'''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()






"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase						=			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						=			[
		    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InformerForPrediction''',
		    '''InformerModel''',
		    '''InformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_informer import (
				    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InformerForPrediction,
				    InformerModel,
				    InformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)