

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							analyze_text(_a)
	SCREAMING_SNAKE_CASE :   Tuple					  =							list(" " + ascii_lowercase)
	# what is our total sum of probabilities.
	SCREAMING_SNAKE_CASE :   List[Any]					  =							sum(single_char_strings.values())

	# one length string
	SCREAMING_SNAKE_CASE :   Optional[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:
			SCREAMING_SNAKE_CASE :   Tuple					  =							single_char_strings[ch]
			SCREAMING_SNAKE_CASE :   List[str]					  =							my_str / all_sum
			my_fir_sum += prob * math.loga(_a)  # entropy formula.

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

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

    # 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__			(       _a):
	SCREAMING_SNAKE_CASE :   Tuple					  =							Counter()  # type: ignore
	SCREAMING_SNAKE_CASE :   Tuple					  =							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(_a) - 1):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def 			lowerCamelCase__			(       ):
	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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_      =     {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'VanForImageClassification',
			    'VanModel',
			    'VanPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_van import (
						    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    VanForImageClassification,
						    VanModel,
						    VanPreTrainedModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)