



'''simple docstring'''

import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_A				:    Dict     						=datasets.utils.logging.get_logger(__name__)



@dataclass
class 		lowerCamelCase__      (						datasets.BuilderConfig     ):




					'''simple docstring'''



					A_						    =						1_0000
					A_						    =						None
					A_						    =						None






class 		lowerCamelCase__      (						datasets.ArrowBasedBuilder     ):




					'''simple docstring'''



					A_						    =						ParquetConfig




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       int:



						'''simple docstring'''





						return datasets.DatasetInfo(features=self.config.features					)




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Optional[Any]					)  ->       List[str]:



						'''simple docstring'''





						if not self.config.data_files:
							raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''					)
						_lowercase					:       Dict       =		dl_manager.download_and_extract(self.config.data_files					)
						if isinstance(UpperCamelCase_     ,  (str, list, tuple)					):
							_lowercase					:       Any       =		data_files
							if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       Optional[Any]       =		[files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							_lowercase					:       Any       =		[dl_manager.iter_files(UpperCamelCase_					) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,  gen_kwargs={'files': files}					)]
						_lowercase					:       Tuple       =		[]
						for split_name, files in data_files.items():
							if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       List[Any]       =		[files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							_lowercase					:       Union[str, Any]       =		[dl_manager.iter_files(UpperCamelCase_					) for file in files]
							# Infer features is they are stoed in the arrow schema
							if self.info.features is None:
								for file in itertools.chain.from_iterable(UpperCamelCase_					):
									with open(UpperCamelCase_     ,  'rb'					) as f:
										_lowercase					:       int       =		datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase_					)					)
									break
							splits.append(datasets.SplitGenerator(name=UpperCamelCase_     ,  gen_kwargs={'files': files}					)					)
						return splits




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	pa.Table					)  ->       pa.Table:



						'''simple docstring'''





						if self.info.features is not None:
							# more expensive cast to support nested features with keys in a different order
							# allows str <-> int/float or str to Audio for example
							_lowercase					:       Dict       =		table_cast(UpperCamelCase_     ,  self.info.features.arrow_schema					)
						return pa_table




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		self.info.features.arrow_schema if self.info.features is not None else None
						if self.info.features is not None and self.config.columns is not None:
							if sorted(field.name for field in schema					) != sorted(self.config.columns					):
								raise ValueError(
								    F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''					)
						for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_					)					):
							with open(UpperCamelCase_     ,  'rb'					) as f:
								_lowercase					:       Optional[Any]       =		pq.ParquetFile(UpperCamelCase_					)
								try:
									for batch_idx, record_batch in enumerate(
									    parquet_file.iter_batches(batch_size=self.config.batch_size     ,  columns=self.config.columns					)					):
										_lowercase					:       Union[str, Any]       =		pa.Table.from_batches([record_batch]					)
										# Uncomment for debugging (will print the Arrow table size and elements)
										# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
										# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
										yield F'''{file_idx}_{batch_idx}''', self._cast_table(UpperCamelCase_					)
								except ValueError as e:
									logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase_					)}: {e}'''					)
									raise





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

	# one length string
	_lowercase					:       Union[str, 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:
			_lowercase					:       Any       =		single_char_strings[ch]
			_lowercase					:       int       =		my_str / all_sum
			my_fir_sum += prob * math.loga(_lowercase					)  # entropy formula.

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

	# two len string
	_lowercase					:       str       =		sum(two_char_strings.values()					)
	_lowercase					:       str       =		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					:       int       =		two_char_strings[sequence]
				_lowercase					:       Optional[int]       =		int(_lowercase					) / all_sum
				my_sec_sum += prob * math.loga(_lowercase					)

    # 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 						__UpperCamelCase				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       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(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	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()
