


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


A	:		str    =       datasets.utils.logging.get_logger(__name__)

A	:		List[Any]    =       ['names', 'prefix']
A	:		List[Any]    =       ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
A	:		List[Any]    =       ['encoding_errors', 'on_bad_lines']
A	:		str    =       ['date_format']



@dataclass
class 							A   (	datasets.BuilderConfig							):
					'''simple docstring'''






					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__      =			1_00_00
					A__      =			None
					A__      =			"strict"
					A__      =			"error"
					A__      =			None

					def   lowerCamelCase__					(self   :						str		)						->       Union[str, Any]:




							"""simple docstring"""


							if self.delimiter is not None:
									lowercase__					      =		self.delimiter
							if self.column_names is not None:
									lowercase__					      =		self.column_names


					@property
					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		{
							    """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()				, _UpperCAmelCase		):
											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							):
					'''simple docstring'''






					A__      =			CsvConfig

					def   lowerCamelCase__					(self   :						Any		)						->       Any:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Optional[int]				, _UpperCAmelCase   :						Tuple		)						->       Optional[int]:




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

					def   lowerCamelCase__					(self   :						Dict				, _UpperCAmelCase   :						pa.Table		)						->       pa.Table:




							"""simple docstring"""


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


					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						int		)						->       Dict:




							"""simple docstring"""


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





import pytest


A	:		Optional[Any]    =       '__dummy_dataset1__'

A	:		Tuple    =       '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'






@pytest.fixture
def 			UpperCamelCase (     )       ->  Any:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 			UpperCamelCase (     )       ->  Union[str, Any]:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Any					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		dataset_loading_script_name
		lowercase__					      =		tmp_path / """datasets""" / script_name
		script_dir.mkdir(parents=__magic_name__					)
		lowercase__					      =		script_dir / f'''{script_name}.py'''
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)

