
"""simple docstring"""


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


_lowercase				 =					datasets.utils.logging.get_logger(__name__)

_lowercase				 =					['''names''', '''prefix''']
_lowercase				 =					['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
_lowercase				 =					['''encoding_errors''', '''on_bad_lines''']
_lowercase				 =					['''date_format''']







@dataclass
class 					lowerCAmelCase_					(   datasets.BuilderConfig     ):



						'''simple docstring'''


						_lowerCamelCase:     str   =     ","
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[Union[int, List[int], str]]   =     "infer"
						_lowerCamelCase:     Optional[List[str]]   =     None
						_lowerCamelCase:     Optional[List[str]]   =     None
						_lowerCamelCase:     Optional[Union[int, str, List[int], List[str]]]   =     None
						_lowerCamelCase:     Optional[Union[List[int], List[str]]]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     Optional[Literal["c", "python", "pyarrow"]]   =     None
						_lowerCamelCase:     Dict[Union[int, str], Callable[[Any], Any]]   =     None
						_lowerCamelCase:     Optional[list]   =     None
						_lowerCamelCase:     Optional[list]   =     None
						_lowerCamelCase:     bool   =     False
						_lowerCamelCase:     Optional[Union[int, List[int]]]   =     None
						_lowerCamelCase:     Optional[int]   =     None
						_lowerCamelCase:     Optional[Union[str, List[str]]]   =     None
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     False
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     str   =     "."
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     str   =     '"'
						_lowerCamelCase:     int   =     0
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     int   =     0
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     False
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     int   =     10000
						_lowerCamelCase:     Optional[datasets.Features]   =     None
						_lowerCamelCase:     Optional[str]   =     "strict"
						_lowerCamelCase:     Literal["error", "warn", "skip"]   =     "error"
						_lowerCamelCase:     Optional[str]   =     None


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Any:
							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[Any]   )  -> Any:
							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()	,A_   ):
									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 					lowerCAmelCase_					(   datasets.ArrowBasedBuilder     ):



						'''simple docstring'''


						_lowerCamelCase:     Any   =     CsvConfig


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Any   )  -> 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(A_	,(str, list, tuple)   ):
								A     = data_files
								if isinstance(A_	,A_   ):
									A     = [files]
								A     = [dl_manager.iter_files(A_   ) 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(A_	,A_   ):
									A     = [files]
								A     = [dl_manager.iter_files(A_   ) for file in files]
								splits.append(datasets.SplitGenerator(name=A_	,gen_kwargs={'files': files}   )   )
							return splits


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




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Union[str, Any]   )  -> List[Any]:
							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(A_   ) 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(A_   )   ):
								A     = pd.read_csv(A_	,iterator=A_	,dtype=A_	,**self.config.pd_read_csv_kwargs   )
								try:
									for batch_idx, df in enumerate(A_   ):
										A     = pa.Table.from_pandas(A_   )
										# 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(A_   )
								except ValueError as e:
									logger.error(F'Failed to read file \'{file}\' with error {type(A_   )}: {e}'   )
									raise







"""simple docstring"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    size
										_SCREAMING_SNAKE_CASE   :		str       		=    [0] * size
										_SCREAMING_SNAKE_CASE   :		Any       		=    [0] * size







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return index | (index + 1)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return (index & (index + 1)) - 1







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		str       		=    value
										while index < self.size:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.get_prev(lowerCAmelCase_							) + 1
														if current_left_border == index:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    value
														else:
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    max(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		str       		=    self.get_next(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										right -= 1  # Because of right is exclusive
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    0
										while left <= right:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_prev(lowerCAmelCase_							)
														if left <= current_left:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    max(lowerCAmelCase_   ,				self.tree[right]							)
																		_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current_left
														else:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    max(lowerCAmelCase_   ,				self.arr[right]							)
																		right -= 1
										return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()

