





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


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

SCREAMING_SNAKE_CASE_						   =       ["""names""", """prefix"""]
SCREAMING_SNAKE_CASE_						   =       ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
SCREAMING_SNAKE_CASE_						   =       ["""encoding_errors""", """on_bad_lines"""]
SCREAMING_SNAKE_CASE_						   =       ["""date_format"""]

@dataclass
class 		UpperCamelCase__ (      datasets.BuilderConfig   ):



     '''simple docstring'''

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Dict:




           '''simple docstring'''


           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 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Union[str, Any]:




           '''simple docstring'''


           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 		UpperCamelCase__ (      datasets.ArrowBasedBuilder   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			CsvConfig






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[Any]:




           '''simple docstring'''


           return datasets.DatasetInfo(features=self.config.features       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    Optional[Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           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 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    pa.Table       )     ->		pa.Table:




           '''simple docstring'''


           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 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    List[Any]       )     ->		Dict:




           '''simple docstring'''


           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









import os
from distutils.util import strtobool



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Tuple:


      '''simple docstring'''


      for e in env_keys:
            SCREAMING_SNAKE_CASE		=						int(os.environ.get(_SCREAMING_SNAKE_CASE ,						-1	)	)
            if val >= 0:
                  return val
      return default



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						os.environ.get(_SCREAMING_SNAKE_CASE ,						str(_SCREAMING_SNAKE_CASE	)	)
      return strtobool(_SCREAMING_SNAKE_CASE	) == 1  # As its name indicates `strtobool` actually returns an int...



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE="no"	) ->   Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						os.environ.get(_SCREAMING_SNAKE_CASE ,						str(_SCREAMING_SNAKE_CASE	)	)
      return value


