


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[int]   					= analyze_text(_a	)
		lowerCAmelCase__       :      Dict   					= list(''' ''' + ascii_lowercase	)
		# what is our total sum of probabilities.
		lowerCAmelCase__       :      Tuple   					= sum(single_char_strings.values()	)

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




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= Counter()  # type: ignore
		lowerCAmelCase__       :      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(_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_   (	):




		"""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()








import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		def wrapper(*_a     ,       **_a	):
				lowerCAmelCase__       :      List[str]   					= timeit.default_timer()
				lowerCAmelCase__       :      List[Any]   					= func(*_a     ,       **_a	)
				lowerCAmelCase__       :      Any   					= timeit.default_timer() - starttime
				return delta

		lowerCAmelCase__       :      Any   					= func.__name__

		return wrapper






def 					lowerCamelCase_   (	_a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__       :      str   					= seq_shapes or {}
		for i in range(_a	):
				lowerCAmelCase__       :      List[str]   					= {}
				for col_id, (k, v) in enumerate(features.items()	):
						if isinstance(_a     ,       _ArrayXD	):
								lowerCAmelCase__       :      List[str]   					= np.random.rand(*v.shape	).astype(v.dtype	)
						elif isinstance(_a     ,       datasets.Value	):
								if v.dtype == "string":
										lowerCAmelCase__       :      Dict   					= '''The small grey turtle was surprisingly fast when challenged.'''
								else:
										lowerCAmelCase__       :      Any   					= np.random.randint(10     ,       size=1	).astype(v.dtype	).item()
						elif isinstance(_a     ,       datasets.Sequence	):
								while isinstance(_a     ,       datasets.Sequence	):
										lowerCAmelCase__       :      Optional[int]   					= v.feature
								lowerCAmelCase__       :      str   					= seq_shapes[k]
								lowerCAmelCase__       :      Any   					= np.random.rand(*_a	).astype(v.dtype	)
						lowerCAmelCase__       :      int   					= data

				dummy_data.append((i, example)	)

		return dummy_data






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= generate_examples(_a     ,       num_examples=_a     ,       seq_shapes=_a	)

		with ArrowWriter(features=_a     ,       path=_a	) as writer:
				for key, record in dummy_data:
						lowerCAmelCase__       :      Optional[int]   					= features.encode_example(_a	)
						writer.write(_a	)

				lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= writer.finalize()

		if not num_final_examples == num_examples:
				raise ValueError(
				    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'	)

		lowerCAmelCase__       :      List[Any]   					= datasets.Dataset.from_file(filename=_a     ,       info=datasets.DatasetInfo(features=_a	)	)

		return dataset




