'''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


SCREAMING_SNAKE_CASE_:	Union[str, Any]    =datasets.utils.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	Dict    =['names', 'prefix']
SCREAMING_SNAKE_CASE_:	Optional[int]    =['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
SCREAMING_SNAKE_CASE_:	List[str]    =['encoding_errors', 'on_bad_lines']
SCREAMING_SNAKE_CASE_:	Any    =['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			  =    10_000
    a__			:		Optional[datasets.Features]			  =    None
    a__			:		Optional[str]			  =    "strict"
    a__			:		Literal["error", "warn", "skip"]			  =    "error"
    a__			:		Optional[str]			  =    None



    def    _lowercase		(self       :       Tuple					):
         if self.delimiter is not None:
              UpperCAmelCase_			       =  self.delimiter
         if self.column_names is not None:
              UpperCAmelCase_			       =  self.column_names






    @property
    def    _lowercase		(self       :       Optional[Any]					):
         UpperCAmelCase_			       =  {
             "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 			__A (					datasets.ArrowBasedBuilder      ):
    a__			:		int			  =    CsvConfig



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



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



    def    _lowercase		(self       :       Tuple  ,     __a       :       pa.Table					):
         if self.config.features is not None:
              UpperCAmelCase_			       =  self.config.features.arrow_schema
              if all(not require_storage_cast(__a					) for feature in self.config.features.values()					):
                   # cheaper cast
                   UpperCAmelCase_			       =  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
                   UpperCAmelCase_			       =  table_cast(__a  ,     __a					)
         return pa_table






    def    _lowercase		(self       :       Tuple  ,     __a       :       str					):
         UpperCAmelCase_			       =  self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         UpperCAmelCase_			       =  (
             {
                 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					)					):
              UpperCAmelCase_			       =  pd.read_csv(__a  ,     iterator=__a  ,     dtype=__a  ,     **self.config.pd_read_csv_kwargs					)
              try:
                   for batch_idx, df in enumerate(__a					):
                        UpperCAmelCase_			       =  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





UpperCAmelCase_           =				'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_           =				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_           =				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}