




import pytest


__A								=       "__dummy_dataset1__"

__A								=       "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"







@pytest.fixture
def      lowerCamelCase_						(			)	->			Optional[int]:





				"""simple docstring"""


				return DATASET_LOADING_SCRIPT_NAME







@pytest.fixture
def      lowerCamelCase_						(			)	->			Tuple:





				"""simple docstring"""


				return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def      lowerCamelCase_						(			UpperCamelCase__							: Any					,	UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: Optional[Any] )	->			Tuple:





				"""simple docstring"""


				__lowerCamelCase									=       dataset_loading_script_name
				__lowerCamelCase									=       tmp_path / 'datasets' / script_name
				script_dir.mkdir(parents=UpperCamelCase__ )
				__lowerCamelCase									=       script_dir / F"""{script_name}.py"""
				with open(UpperCamelCase__					,	'w' ) as f:
								f.write(UpperCamelCase__ )
				return str(UpperCamelCase__ )








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       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									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

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

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

    # 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_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       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(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""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()


