





"""simple docstring"""


from __future__ import annotations

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = None							,     SCREAMING_SNAKE_CASE = None							,     SCREAMING_SNAKE_CASE = False							,     ):
						UpperCamelCase				:		int				   =			cipher_alphabet or [chr(SCREAMING_SNAKE_CASE     ) for i in range(97							,     123     )]

						# If the argument is None or the user provided an empty dictionary
						if not frequencies_dict:
												# Frequencies of letters in the english language (how much they show up)
												UpperCamelCase				:		Any				   =			{
												    """a""": 0.0_84_97,
												    """b""": 0.0_14_92,
												    """c""": 0.0_22_02,
												    """d""": 0.0_42_53,
												    """e""": 0.1_11_62,
												    """f""": 0.0_22_28,
												    """g""": 0.0_20_15,
												    """h""": 0.0_60_94,
												    """i""": 0.0_75_46,
												    """j""": 0.0_01_53,
												    """k""": 0.0_12_92,
												    """l""": 0.0_40_25,
												    """m""": 0.0_24_06,
												    """n""": 0.0_67_49,
												    """o""": 0.0_75_07,
												    """p""": 0.0_19_29,
												    """q""": 0.0_00_95,
												    """r""": 0.0_75_87,
												    """s""": 0.0_63_27,
												    """t""": 0.0_93_56,
												    """u""": 0.0_27_58,
												    """v""": 0.0_09_78,
												    """w""": 0.0_25_60,
												    """x""": 0.0_01_50,
												    """y""": 0.0_19_94,
												    """z""": 0.0_00_77,
												}
						else:
												# Custom frequencies dictionary
												UpperCamelCase				:		List[Any]				   =			frequencies_dict

						if not case_sensitive:
												UpperCamelCase				:		Optional[Any]				   =			ciphertext.lower()

						# Chi squared statistic values
						UpperCamelCase				:		dict[int, tuple[float, str]]				   =			{}

						# cycle through all of the shifts
						for shift in range(len(SCREAMING_SNAKE_CASE     )     ):
												UpperCamelCase				:		List[str]				   =			""""""

												# decrypt the message with the shift
												for letter in ciphertext:
																		try:
																								# Try to index the letter in the alphabet
																								UpperCamelCase				:		Any				   =			(alphabet_letters.index(letter.lower()     ) - shift) % len(
																								    SCREAMING_SNAKE_CASE     )
																								decrypted_with_shift += (
																								    alphabet_letters[new_key].upper()
																								    if case_sensitive and letter.isupper()
																								    else alphabet_letters[new_key]
																								)
																		except ValueError:
																								# Append the character if it isn't in the alphabet
																								decrypted_with_shift += letter

												UpperCamelCase				:		str				   =			0.0

												# Loop through each letter in the decoded message with the shift
												for letter in decrypted_with_shift:
																		if case_sensitive:
																								UpperCamelCase				:		str				   =			letter.lower()
																								if letter in frequencies:
																														# Get the amount of times the letter occurs in the message
																														UpperCamelCase				:		str				   =			decrypted_with_shift.lower().count(SCREAMING_SNAKE_CASE     )

																														# Get the excepcted amount of times the letter should appear based
																														# on letter frequencies
																														UpperCamelCase				:		Optional[Any]				   =			frequencies[letter] * occurrences

																														# Complete the chi squared statistic formula
																														UpperCamelCase				:		str				   =			((occurrences - expected) ** 2) / expected

																														# Add the margin of error to the total chi squared statistic
																														chi_squared_statistic += chi_letter_value
																		else:
																								if letter.lower() in frequencies:
																														# Get the amount of times the letter occurs in the message
																														UpperCamelCase				:		Dict				   =			decrypted_with_shift.count(SCREAMING_SNAKE_CASE     )

																														# Get the excepcted amount of times the letter should appear based
																														# on letter frequencies
																														UpperCamelCase				:		int				   =			frequencies[letter] * occurrences

																														# Complete the chi squared statistic formula
																														UpperCamelCase				:		List[str]				   =			((occurrences - expected) ** 2) / expected

																														# Add the margin of error to the total chi squared statistic
																														chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
												UpperCamelCase				:		Optional[Any]				   =			(
												    chi_squared_statistic,
												    decrypted_with_shift,
												)

						# Get the most likely cipher by finding the cipher with the smallest chi squared
						# statistic
						def chi_squared_statistic_values_sorting_key(SCREAMING_SNAKE_CASE     ) -> tuple[float, str]:
												return chi_squared_statistic_values[key]

						UpperCamelCase				:		int				   =			min(
						    SCREAMING_SNAKE_CASE							,     key=SCREAMING_SNAKE_CASE							,     )

						# Get all the data from the most likely cipher (key, decoded message)
						(
						    (
						    UpperCamelCase
						)   ,						(
						    UpperCamelCase
						)   ,						
						)				:		Union[str, Any]				   =			chi_squared_statistic_values[most_likely_cipher]

						# Return the data on the most likely shift
						return (
						    most_likely_cipher,
						    most_likely_cipher_chi_squared_value,
						    decoded_most_likely_cipher,
						)










"""simple docstring"""


import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												UpperCamelCase				:		Tuple				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		int				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Union[str, Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		str				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Optional[int]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]							,     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
						UpperCamelCase				:		Optional[Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		Tuple				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		int				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						UpperCamelCase				:		str				   =			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
						UpperCamelCase				:		Tuple				   =			features.copy()
						UpperCamelCase				:		Tuple				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Optional[int]				   =			tmp_path / """cache"""
						UpperCamelCase				:		List[Any]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Any				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Dict				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE							,     split=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""							,     [str, list]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			jsonl_path
						elif issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			[jsonl_path]
						UpperCamelCase				:		List[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		List[Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Optional[int]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=("train",)     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						for split in splits:
												UpperCamelCase				:		Optional[Any]				   =			dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			tmp_path / """cache"""
						UpperCamelCase				:		int				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												UpperCamelCase				:		Any				   =			JsonDatasetReader({"""train""": jsonl_path}							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Optional[Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		Optional[Any]				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Optional[Any]				   =			JsonDatasetReader({"""train""": jsonl_path}							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if split:
												UpperCamelCase				:		List[Any]				   =			{split: jsonl_path}
						else:
												UpperCamelCase				:		Tuple				   =			"""train"""
												UpperCamelCase				:		Union[str, Any]				   =			{"""train""": jsonl_path, """test""": jsonl_path}
						UpperCamelCase				:		Optional[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Union[str, Any]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     splits=list(path.keys()     )     )
						assert all(dataset[split].split == split for split in path.keys()     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return json.load(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return [json.loads(SCREAMING_SNAKE_CASE     ) for line in buffer]






class 							lowercase__			:

			"""simple docstring"""







			@pytest.mark.parametrize("""lines, load_json_function"""						,   [(True, load_json_lines), (False, load_json)]    )
			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		Optional[Any]				   =			load_json_function(_A    )
									assert isinstance(_A						,   _A    )
									assert isinstance(exported_content[0]						,   _A    )
									assert len(_A    ) == 1_0







			@pytest.mark.parametrize(
			    """orient, container, keys, len_at"""						,   [
			        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
			        ("""split""", dict, {"""columns""", """data"""}, """data"""),
			        ("""index""", dict, set("""0123456789"""    ), None),
			        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
			        ("""values""", list, None, None),
			        ("""table""", dict, {"""schema""", """data"""}, """data"""),
			    ]						,   )
			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   orient=_A    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		str				   =			load_json(_A    )
									assert isinstance(_A						,   _A    )
									if keys:
															if container is dict:
																					assert exported_content.keys() == keys
															else:
																					assert exported_content[0].keys() == keys
									else:
															assert not hasattr(_A						,   """keys"""    ) and not hasattr(exported_content[0]						,   """keys"""    )
									if len_at:
															assert len(exported_content[len_at]    ) == 1_0
									else:
															assert len(_A    ) == 1_0







			@pytest.mark.parametrize("""lines, load_json_function"""						,   [(True, load_json_lines), (False, load_json)]    )
			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   num_proc=2    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		List[Any]				   =			load_json_function(_A    )
									assert isinstance(_A						,   _A    )
									assert isinstance(exported_content[0]						,   _A    )
									assert len(_A    ) == 1_0







			@pytest.mark.parametrize(
			    """orient, container, keys, len_at"""						,   [
			        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
			        ("""split""", dict, {"""columns""", """data"""}, """data"""),
			        ("""index""", dict, set("""0123456789"""    ), None),
			        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
			        ("""values""", list, None, None),
			        ("""table""", dict, {"""schema""", """data"""}, """data"""),
			    ]						,   )
			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   orient=_A						,   num_proc=2    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		Dict				   =			load_json(_A    )
									assert isinstance(_A						,   _A    )
									if keys:
															if container is dict:
																					assert exported_content.keys() == keys
															else:
																					assert exported_content[0].keys() == keys
									else:
															assert not hasattr(_A						,   """keys"""    ) and not hasattr(exported_content[0]						,   """keys"""    )
									if len_at:
															assert len(exported_content[len_at]    ) == 1_0
									else:
															assert len(_A    ) == 1_0







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									with pytest.raises(_A    ):
															with io.BytesIO() as buffer:
																					JsonDatasetWriter(_A						,   _A						,   num_proc=0    )







			@pytest.mark.parametrize("""compression, extension"""						,   [("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]    )
			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			tmp_path_factory.mktemp("""data"""    ) / f"""test.json.{extension}"""
									UpperCamelCase				:		Tuple				   =			str(shared_datadir / f"""test_file.json.{extension}"""    )
									JsonDatasetWriter(_A						,   _A						,   compression=_A    ).write()

									with fsspec.open(_A						,   """rb"""						,   compression="""infer"""    ) as f:
															UpperCamelCase				:		Dict				   =			f.read()
									with fsspec.open(_A						,   """rb"""						,   compression="""infer"""    ) as f:
															UpperCamelCase				:		Tuple				   =			f.read()
									assert exported_content == original_content



