



_lowerCAmelCase	:   int    =   "\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	:   Optional[Any]    =   [{"type": "code", "content": INSTALL_CONTENT}]
_lowerCAmelCase	:   Optional[Any]    =   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





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

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


@dataclass
class 	__snake_case   ( datasets.BuilderConfig ):
		SCREAMING_SNAKE_CASE__								=	","
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"infer"
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"."
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	'"'
		SCREAMING_SNAKE_CASE__								=	0
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	0
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	10000
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"strict"
		SCREAMING_SNAKE_CASE__								=	"error"
		SCREAMING_SNAKE_CASE__								=	None
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.delimiter is not None:
										lowerCAmelCase__		  =  self.delimiter
						if self.column_names is not None:
										lowerCAmelCase__		  =  self.column_names



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  {
						    '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 	__snake_case   ( datasets.ArrowBasedBuilder ):
		SCREAMING_SNAKE_CASE__								=	CsvConfig
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return datasets.DatasetInfo(features=self.config.features						)
		def 						SCREAMING_SNAKE_CASE_					(							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}'						)
						lowerCAmelCase__		  =  dl_manager.download_and_extract(self.config.data_files						)
						if isinstance(a_    ,(str, list, tuple)						):
										lowerCAmelCase__		  =  data_files
										if isinstance(a_    ,a_						):
														lowerCAmelCase__		  =  [files]
										lowerCAmelCase__		  =  [dl_manager.iter_files(a_						) for file in files]
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,gen_kwargs={'files': files}						)]
						lowerCAmelCase__		  =  []
						for split_name, files in data_files.items():
										if isinstance(a_    ,a_						):
														lowerCAmelCase__		  =  [files]
										lowerCAmelCase__		  =  [dl_manager.iter_files(a_						) for file in files]
										splits.append(datasets.SplitGenerator(name=a_    ,gen_kwargs={'files': files}						)						)
						return splits
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						if self.config.features is not None:
										lowerCAmelCase__		  =  self.config.features.arrow_schema
										if all(not require_storage_cast(a_						) for feature in self.config.features.values()						):
														# cheaper cast
														lowerCAmelCase__		  =  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
														lowerCAmelCase__		  =  table_cast(a_    ,a_						)
						return pa_table



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.config.features.arrow_schema if self.config.features else None
						# dtype allows reading an int column as str
						lowerCAmelCase__		  =  (
						    {
						        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_						)						):
										lowerCAmelCase__		  =  pd.read_csv(a_    ,iterator=a_    ,dtype=a_    ,**self.config.pd_read_csv_kwargs						)
										try:
														for batch_idx, df in enumerate(a_						):
																		lowerCAmelCase__		  =  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
