

'''simple docstring'''





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  None:
						lowercase		,	lowercase :							Dict    		=analyze_text(__magic_name__						)
						lowercase :							int    		=list(''' ''' + ascii_lowercase						)
						# what is our total sum of probabilities.
						lowercase :							List[Any]    		=sum(single_char_strings.values()						)

						# one length string
						lowercase :							Tuple    		=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:
																		lowercase :							str    		=single_char_strings[ch]
																		lowercase :							Dict    		=my_str / all_sum
																		my_fir_sum += prob * math.loga(__magic_name__						)  # entropy formula.

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

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

    # 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			(   __magic_name__						:     str						)				->  tuple[dict, dict]:
						lowercase :							Dict    		=Counter()  # type: ignore
						lowercase :							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(__magic_name__						) - 1						):
												single_char_strings[text[i]] += 1
												two_char_strings[text[i : i + 2]] += 1
						return single_char_strings, two_char_strings






def 				_lowerCAmelCase			(   )				->  Optional[int]:
						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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    						=       {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MBartForCausalLM""",
							    """MBartForConditionalGeneration""",
							    """MBartForQuestionAnswering""",
							    """MBartForSequenceClassification""",
							    """MBartModel""",
							    """MBartPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TFMBartForConditionalGeneration""",
							    """TFMBartModel""",
							    """TFMBartPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """FlaxMBartForConditionalGeneration""",
							    """FlaxMBartForQuestionAnswering""",
							    """FlaxMBartForSequenceClassification""",
							    """FlaxMBartModel""",
							    """FlaxMBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





