
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					None:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      analyze_text(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      list(" " + ascii_lowercase)
							# what is our total sum of probabilities.
							__UpperCamelCase			:     Any         =      sum(single_char_strings.values())

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






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      Counter()  # type: ignore
							__UpperCamelCase			:     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 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''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()

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)