import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


lowerCamelCase_    :		Tuple							=	datasets.utils.logging.get_logger(__name__)

lowerCamelCase_    :		str							=	["""names""", """prefix"""]
lowerCamelCase_    :		str							=	["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
lowerCamelCase_    :		List[str]							=	["""encoding_errors""", """on_bad_lines"""]
lowerCamelCase_    :		Optional[int]							=	["""date_format"""]


@dataclass
class 						a__							(       datasets.BuilderConfig		):
	A__    :       str        =      ","
	A__    :       Optional[str]        =      None
	A__    :       Optional[Union[int, List[int], str]]        =      "infer"
	A__    :       Optional[List[str]]        =      None
	A__    :       Optional[List[str]]        =      None
	A__    :       Optional[Union[int, str, List[int], List[str]]]        =      None
	A__    :       Optional[Union[List[int], List[str]]]        =      None
	A__    :       Optional[str]        =      None
	A__    :       bool        =      True
	A__    :       Optional[Literal["c", "python", "pyarrow"]]        =      None
	A__    :       Dict[Union[int, str], Callable[[Any], Any]]        =      None
	A__    :       Optional[list]        =      None
	A__    :       Optional[list]        =      None
	A__    :       bool        =      False
	A__    :       Optional[Union[int, List[int]]]        =      None
	A__    :       Optional[int]        =      None
	A__    :       Optional[Union[str, List[str]]]        =      None
	A__    :       bool        =      True
	A__    :       bool        =      True
	A__    :       bool        =      False
	A__    :       bool        =      True
	A__    :       Optional[str]        =      None
	A__    :       str        =      "."
	A__    :       Optional[str]        =      None
	A__    :       str        =      '"'
	A__    :       int        =      0
	A__    :       Optional[str]        =      None
	A__    :       Optional[str]        =      None
	A__    :       Optional[str]        =      None
	A__    :       Optional[str]        =      None
	A__    :       bool        =      True
	A__    :       bool        =      True
	A__    :       int        =      0
	A__    :       bool        =      True
	A__    :       bool        =      False
	A__    :       Optional[str]        =      None
	A__    :       int        =      1_00_00
	A__    :       Optional[datasets.Features]        =      None
	A__    :       Optional[str]        =      "strict"
	A__    :       Literal["error", "warn", "skip"]        =      "error"
	A__    :       Optional[str]        =      None



	def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:




						if self.delimiter is not None:
											__a		 =       self.delimiter
						if self.column_names is not None:
											__a		 =       self.column_names







	@property
	def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
						__a		 =       {
						    "sep": self.sep,
						    "header": self.header,
						    "names": self.names,
						    "index_col": self.index_col,
						    "usecols": self.usecols,
						    "prefix": self.prefix,
						    "mangle_dupe_cols": self.mangle_dupe_cols,
						    "engine": self.engine,
						    "converters": self.converters,
						    "true_values": self.true_values,
						    "false_values": self.false_values,
						    "skipinitialspace": self.skipinitialspace,
						    "skiprows": self.skiprows,
						    "nrows": self.nrows,
						    "na_values": self.na_values,
						    "keep_default_na": self.keep_default_na,
						    "na_filter": self.na_filter,
						    "verbose": self.verbose,
						    "skip_blank_lines": self.skip_blank_lines,
						    "thousands": self.thousands,
						    "decimal": self.decimal,
						    "lineterminator": self.lineterminator,
						    "quotechar": self.quotechar,
						    "quoting": self.quoting,
						    "escapechar": self.escapechar,
						    "comment": self.comment,
						    "encoding": self.encoding,
						    "dialect": self.dialect,
						    "error_bad_lines": self.error_bad_lines,
						    "warn_bad_lines": self.warn_bad_lines,
						    "skipfooter": self.skipfooter,
						    "doublequote": self.doublequote,
						    "memory_map": self.memory_map,
						    "float_precision": self.float_precision,
						    "chunksize": self.chunksize,
						    "encoding_errors": self.encoding_errors,
						    "on_bad_lines": self.on_bad_lines,
						    "date_format": self.date_format,
						}

						# some kwargs must not be passed if they don't have a default value
						# some others are deprecated and we can also not pass them if they are the default value
						for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
											if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()				,						lowerCAmelCase__   ):
																del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
						if not (datasets.config.PANDAS_VERSION.major >= 2):
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
						if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																del pd_read_csv_kwargs[pd_read_csv_parameter]

						return pd_read_csv_kwargs






class 						a__							(       datasets.ArrowBasedBuilder		):
	A__    :       int        =      CsvConfig



	def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
						return datasets.DatasetInfo(features=self.config.features   )



	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:




						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}'''   )
						__a		 =       dl_manager.download_and_extract(self.config.data_files   )
						if isinstance(lowerCAmelCase__				,						(str, list, tuple)   ):
											__a		 =       data_files
											if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ):
																__a		 =       [files]
											__a		 =       [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,						gen_kwargs={'files': files}   )]
						__a		 =       []
						for split_name, files in data_files.items():
											if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ):
																__a		 =       [files]
											__a		 =       [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
											splits.append(datasets.SplitGenerator(name=lowerCAmelCase__				,						gen_kwargs={'files': files}   )   )
						return splits



	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   pa.Table:




						if self.config.features is not None:
											__a		 =       self.config.features.arrow_schema
											if all(not require_storage_cast(lowerCAmelCase__   ) for feature in self.config.features.values()   ):
																# cheaper cast
																__a		 =       pa.Table.from_arrays([pa_table[field.name] for field in schema]				,						schema=lowerCAmelCase__   )
											else:
																# more expensive cast; allows str <-> int/float or str to Audio for example
																__a		 =       table_cast(lowerCAmelCase__				,						lowerCAmelCase__   )
						return pa_table







	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Dict:
						__a		 =       self.config.features.arrow_schema if self.config.features else None
						# dtype allows reading an int column as str
						__a		 =       (
						    {
						        name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__   ) else object
						        for name, dtype, feature in zip(schema.names				,						schema.types				,						self.config.features.values()   )
						    }
						    if schema is not None
						    else None
						)
						for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__   )   ):
											__a		 =       pd.read_csv(lowerCAmelCase__				,						iterator=lowerCAmelCase__				,						dtype=lowerCAmelCase__				,						**self.config.pd_read_csv_kwargs   )
											try:
																for batch_idx, df in enumerate(lowerCAmelCase__   ):
																					__a		 =       pa.Table.from_pandas(lowerCAmelCase__   )
																					# 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 (file_idx, batch_idx), self._cast_table(lowerCAmelCase__   )
											except ValueError as e:
																logger.error(f'''Failed to read file \'{file}\' with error {type(lowerCAmelCase__   )}: {e}'''   )
																raise





def 							lowerCAmelCase(     __lowerCamelCase						):
					if not all(char in '01' for char in bin_string						):
										raise ValueError('Non-binary value was passed to the function'						)
					if not bin_string:
										raise ValueError('Empty string was passed to the function'						)
					__a		 =       ''
					while len(__lowerCamelCase						) % 3 != 0:
										__a		 =       '0' + bin_string
					__a		 =       [
					    bin_string[index : index + 3]
					    for index in range(len(__lowerCamelCase						)						)
					    if index % 3 == 0
					]
					for bin_group in bin_string_in_3_list:
										__a		 =       0
										for index, val in enumerate(__lowerCamelCase						):
															oct_val += int(2 ** (2 - index) * int(__lowerCamelCase						)						)
										oct_string += str(__lowerCamelCase						)
					return oct_string


if __name__ == "__main__":
	from doctest import testmod

	testmod()




