


'''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     :						Any	=     datasets.utils.logging.get_logger(__name__)

__lowercase     :						int	=     ["""names""", """prefix"""]
__lowercase     :						Union[str, Any]	=     ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__lowercase     :						List[Any]	=     ["""encoding_errors""", """on_bad_lines"""]
__lowercase     :						Dict	=     ["""date_format"""]





@dataclass
class 							__UpperCamelCase    (		datasets.BuilderConfig ):
 A_           =   ","
 A_           =   None
 A_           =   "infer"
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   True
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   False
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   True
 A_           =   True
 A_           =   False
 A_           =   True
 A_           =   None
 A_           =   "."
 A_           =   None
 A_           =   '"'
 A_           =   0
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   None
 A_           =   True
 A_           =   True
 A_           =   0
 A_           =   True
 A_           =   False
 A_           =   None
 A_           =   10000
 A_           =   None
 A_           =   "strict"
 A_           =   "error"
 A_           =   None




 def 				__UpperCAmelCase							(       self							):

      '''simple docstring'''





      if self.delimiter is not None:
           __a      :	Optional[Any]    =  self.delimiter
      if self.column_names is not None:
           __a      :	Any    =  self.column_names





 @property
 def 				__UpperCAmelCase							(       self							):

      '''simple docstring'''





      __a      :	List[Any]    =  {
          '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()						,    _lowercase							):
                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 ):
 A_           =   CsvConfig




 def 				__UpperCAmelCase							(       self							):

      '''simple docstring'''





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




 def 				__UpperCAmelCase							(       self						,    __a							):

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




 def 				__UpperCAmelCase							(       self						,    __a							):

      '''simple docstring'''





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





 def 				__UpperCAmelCase							(       self						,    __a							):

      '''simple docstring'''





      __a      :	Tuple    =  self.config.features.arrow_schema if self.config.features else None
      # dtype allows reading an int column as str
      __a      :	Union[str, Any]    =  (
          {
              name: dtype.to_pandas_dtype() if not require_storage_cast(_lowercase							) 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(_lowercase							)							):
           __a      :	Any    =  pd.read_csv(_lowercase						,    iterator=_lowercase						,    dtype=_lowercase						,    **self.config.pd_read_csv_kwargs							)
           try:
                for batch_idx, df in enumerate(_lowercase							):
                     __a      :	Dict    =  pa.Table.from_pandas(_lowercase							)
                     # 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(_lowercase							)
           except ValueError as e:
                logger.error(f"""Failed to read file '{file}' with error {type(_lowercase							)}: {e}"""							)
                raise







'''simple docstring'''



import re

from filelock import FileLock


try:
	import nltk

	__lowercase     :						Optional[Any]	=     True
except (ImportError, ModuleNotFoundError):
	__lowercase     :						Dict	=     False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					re.sub('<n>'						,				''						,				_SCREAMING_SNAKE_CASE       )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE       )       )



