

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


__A			:					Optional[Any]						=			datasets.utils.logging.get_logger(__name__)

__A			:					Any						=			['names', 'prefix']
__A			:					Tuple						=			['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__A			:					Dict						=			['encoding_errors', 'on_bad_lines']
__A			:					str						=			['date_format']





@dataclass
class 			_SCREAMING_SNAKE_CASE (   datasets.BuilderConfig		):







   '''simple docstring'''




   lowerCamelCase__				=      ","
   lowerCamelCase__				=      None
   lowerCamelCase__				=      "infer"
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      True
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      False
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      True
   lowerCamelCase__				=      True
   lowerCamelCase__				=      False
   lowerCamelCase__				=      True
   lowerCamelCase__				=      None
   lowerCamelCase__				=      "."
   lowerCamelCase__				=      None
   lowerCamelCase__				=      '"'
   lowerCamelCase__				=      0
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      None
   lowerCamelCase__				=      True
   lowerCamelCase__				=      True
   lowerCamelCase__				=      0
   lowerCamelCase__				=      True
   lowerCamelCase__				=      False
   lowerCamelCase__				=      None
   lowerCamelCase__				=      1_0_0_0_0
   lowerCamelCase__				=      None
   lowerCamelCase__				=      "strict"
   lowerCamelCase__				=      "error"
   lowerCamelCase__				=      None





   def      _snake_case		(       self					:			Tuple			):
       if self.delimiter is not None:
           SCREAMING_SNAKE_CASE										=   self.delimiter
       if self.column_names is not None:
           SCREAMING_SNAKE_CASE										=   self.column_names





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   {
           "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 			_SCREAMING_SNAKE_CASE (   datasets.ArrowBasedBuilder		):







   '''simple docstring'''




   lowerCamelCase__				=      CsvConfig





   def      _snake_case		(       self					:			int			):
       return datasets.DatasetInfo(features=self.config.features			)





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





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			pa.Table			):
       if self.config.features is not None:
           SCREAMING_SNAKE_CASE										=   self.config.features.arrow_schema
           if all(not require_storage_cast(__lowerCamelCase			) for feature in self.config.features.values()			):
               # cheaper cast
               SCREAMING_SNAKE_CASE										=   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
               SCREAMING_SNAKE_CASE										=   table_cast(__lowerCamelCase     , __lowerCamelCase			)
       return pa_table





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.config.features.arrow_schema if self.config.features else None
       # dtype allows reading an int column as str
       SCREAMING_SNAKE_CASE										=   (
           {
               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			)			):
           SCREAMING_SNAKE_CASE										=   pd.read_csv(__lowerCamelCase     , iterator=__lowerCamelCase     , dtype=__lowerCamelCase     , **self.config.pd_read_csv_kwargs			)
           try:
               for batch_idx, df in enumerate(__lowerCamelCase			):
                   SCREAMING_SNAKE_CASE										=   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


from __future__ import annotations





def 	__a     (      A__			:  list[int | str]       ):
    create_state_space_tree(A__			,  []			,  0			,  [0 for i in range(len(A__       )       )]       )





def 	__a     (      A__			:  list[int | str]			,  A__			:  list[int | str]			,  A__			:  int			,  A__			:  list[int]			,  ):

    if index == len(A__       ):
        print(A__       )
        return

    for i in range(len(A__       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            SCREAMING_SNAKE_CASE										=   True
            create_state_space_tree(A__			,  A__			,  index + 1			,  A__       )
            current_sequence.pop()
            SCREAMING_SNAKE_CASE										=   False
__A			:					list[int | str]						=			[3, 1, 2, 4]
generate_all_permutations(sequence)

__A			:					list[int | str]						=			["A", "B", "C"]





generate_all_permutations(sequence_a)