
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


__lowerCAmelCase					:			List[Any]			      =		datasets.utils.logging.get_logger(__name__)

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


@dataclass
class 						__lowerCAmelCase	(	datasets.BuilderConfig      ):



      """simple docstring"""







      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        =    1_0_0_0_0
      A__	:			Optional[datasets.Features]        =    None
      A__	:			Optional[str]        =    "strict"
      A__	:			Literal["error", "warn", "skip"]        =    "error"
      A__	:			Optional[str]        =    None




      def 		snake_case_			(      self		:       List[Any]				):
          if self.delimiter is not None:
              __lowercase		:			int	    =       self.delimiter
          if self.column_names is not None:
              __lowercase		:			Any	    =       self.column_names




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Union[str, 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()				,       _snake_case				):
                  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"""







      A__	:			Optional[int]        =    CsvConfig




      def 		snake_case_			(      self		:       Optional[Any]				):
          return datasets.DatasetInfo(features=self.config.features				)




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




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




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




__lowerCAmelCase					:			Dict			      =		"\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase					:			Tuple			      =		[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase					:			List[Any]			      =		{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


