






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 					lowercase__				(					__snake_case   :							str  ):
					UpperCAmelCase_   :				Optional[int]						=						analyze_text(__snake_case  )
					UpperCAmelCase_   :				Tuple						=						list(' ' + ascii_lowercase  )
					# what is our total sum of probabilities.
					UpperCAmelCase_   :				Optional[Any]						=						sum(single_char_strings.values()  )

					# one length string
					UpperCAmelCase_   :				Optional[int]						=						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_   :				int						=						single_char_strings[ch]
															UpperCAmelCase_   :				Tuple						=						my_str / all_sum
															my_fir_sum += prob * math.loga(__snake_case  )  # entropy formula.

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

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

    # 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__				(					__snake_case   :							str  ):
					UpperCAmelCase_   :				int						=						Counter()  # type: ignore
					UpperCAmelCase_   :				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(__snake_case  ) - 1  ):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings




def 					lowercase__				(					):
					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_tokenizers_available, is_torch_available


__UpperCAmelCase     							=						{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

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






