


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def     UpperCamelCase_(			_A				:Tuple			,       _A				:List[str]					)->						List[Any]:
			assert isinstance(_A			,       _A					)
			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_(			_A				:Tuple			,       _A				:Optional[Any]			,       _A				:Dict					)->						Dict:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCamelCase__   =    CsvDatasetReader(_A			,       cache_dir=_A			,       keep_in_memory=_A					).read()
			_check_csv_dataset(_A			,       _A					)
@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_(			_A				:Optional[int]			,       _A				:int			,       _A				:str					)->						Dict:
			UpperCamelCase__   =    tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    features.copy() if features else default_expected_features
			UpperCamelCase__   =    (
			    Features({feature: Value(_A					) for feature, dtype in features.items()}					) if features is not None else None
			)
			UpperCamelCase__   =    CsvDatasetReader(_A			,       features=_A			,       cache_dir=_A					).read()
			_check_csv_dataset(_A			,       _A					)
@pytest.mark.parametrize("split"			,       [None, NamedSplit("train"					), "train", "test"]					)
def     UpperCamelCase_(			_A				:int			,       _A				:Tuple			,       _A				:Union[str, Any]					)->						Any:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    CsvDatasetReader(_A			,       cache_dir=_A			,       split=_A					).read()
			_check_csv_dataset(_A			,       _A					)
			assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"			,       [str, list]					)
def     UpperCamelCase_(			_A				:Optional[Any]			,       _A				:Dict			,       _A				:Tuple					)->						List[Any]:
			if issubclass(_A			,       _A					):
						UpperCamelCase__   =    csv_path
			elif issubclass(_A			,       _A					):
						UpperCamelCase__   =    [csv_path]
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    CsvDatasetReader(_A			,       cache_dir=_A					).read()
			_check_csv_dataset(_A			,       _A					)
def     UpperCamelCase_(			_A				:List[Any]			,       _A				:Tuple			,       _A				:Any=("train",)					)->						Dict:
			assert isinstance(_A			,       _A					)
			for split in splits:
						UpperCamelCase__   =    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_(			_A				:Union[str, Any]			,       _A				:str			,       _A				:int					)->						List[Any]:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=_A			,       keep_in_memory=_A					).read()
			_check_csv_datasetdict(_A			,       _A					)
@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_(			_A				:Optional[Any]			,       _A				:Dict			,       _A				:str					)->						Tuple:
			UpperCamelCase__   =    tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    features.copy() if features else default_expected_features
			UpperCamelCase__   =    (
			    Features({feature: Value(_A					) for feature, dtype in features.items()}					) if features is not None else None
			)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       features=_A			,       cache_dir=_A					).read()
			_check_csv_datasetdict(_A			,       _A					)
@pytest.mark.parametrize("split"			,       [None, NamedSplit("train"					), "train", "test"]					)
def     UpperCamelCase_(			_A				:Dict			,       _A				:List[str]			,       _A				:Union[str, Any]					)->						Dict:
			if split:
						UpperCamelCase__   =    {split: csv_path}
			else:
						UpperCamelCase__   =    {"train": csv_path, "test": csv_path}
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    CsvDatasetReader(_A			,       cache_dir=_A					).read()
			_check_csv_datasetdict(_A			,       _A			,       splits=list(path.keys()					)					)
			assert all(dataset[split].split == split for split in path.keys()					)
def     UpperCamelCase_(			_A				:Any					)->						Optional[int]:
			with open(_A			,       encoding="utf-8"					) as csvfile:
						yield from csv.reader(_A					)
def     UpperCamelCase_(			_A				:List[str]			,       _A				:Any					)->						Dict:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    os.path.join(_A			,       "tmp.csv"					)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=_A					).read()
			CsvDatasetWriter(dataset["train"]			,       _A			,       num_proc=1					).write()

			UpperCamelCase__   =    iter_csv_file(_A					)
			UpperCamelCase__   =    iter_csv_file(_A					)

			for rowa, rowa in zip(_A			,       _A					):
						assert rowa == rowa
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:Optional[Any]					)->						int:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    os.path.join(_A			,       "tmp.csv"					)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=_A					).read()
			CsvDatasetWriter(dataset["train"]			,       _A			,       num_proc=2					).write()

			UpperCamelCase__   =    iter_csv_file(_A					)
			UpperCamelCase__   =    iter_csv_file(_A					)

			for rowa, rowa in zip(_A			,       _A					):
						assert rowa == rowa







def     UpperCamelCase_(			_A				:Dict			,       _A				:List[str]					)->						str:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    os.path.join(_A			,       "tmp.csv"					)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=_A					).read()
			with pytest.raises(_A					):
						CsvDatasetWriter(dataset["train"]			,       _A			,       num_proc=0					)






import numpy as np
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:Dict="utf-8"			,       _A				:Optional[Any]="surrogatepass"					)->						int:
			UpperCamelCase__   =    bin(int.from_bytes(text.encode(_A			,       _A					)			,       "big"					)					)[2:]
			return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:int="utf-8"			,       _A				:List[str]="surrogatepass"					)->						Optional[Any]:
			UpperCamelCase__   =    int(_A			,       2					)
			return n.to_bytes((n.bit_length() + 7) // 8			,       "big"					).decode(_A			,       _A					) or "\0"
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:Tuple					)->						Optional[int]:
			if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
						raise ValueError("size of parity don't match with size of data"					)

			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			# sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# data position template + parity
			UpperCamelCase__   =    []
			# parity bit counter
			UpperCamelCase__   =    0
			# counter position of data bits
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(_A					) / np.log(2					)).is_integer():
												data_out_gab.append("P"					)
												UpperCamelCase__   =    qtd_bp + 1
									else:
												data_out_gab.append("D"					)
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a given parity
						UpperCamelCase__   =    0
						# counter to control the loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			return data_out
def     UpperCamelCase_(			_A				:int			,       _A				:str					)->						Dict:
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0
			# list of parity received
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []

			for x in range(1			,       len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]					)
						else:
									parity_received.append(data[cont_data]					)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			#  sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# Data position feedback + parity
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a certain parity
						UpperCamelCase__   =    0
						# Counter to control loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2					)					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # Parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			UpperCamelCase__   =    parity_received == parity
			return data_output, ack


