


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


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

UpperCAmelCase   :    Dict     =		["names", "prefix"]
UpperCAmelCase   :    Dict     =		["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
UpperCAmelCase   :    Dict     =		["encoding_errors", "on_bad_lines"]
UpperCAmelCase   :    Optional[Any]     =		["date_format"]







@dataclass
class        __lowercase       (							datasets.BuilderConfig  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		","
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					Optional[Union[int, List[int], str]]			       =		"infer"
		UpperCamelCase     :					Optional[List[str]]			       =		None
		UpperCamelCase     :					Optional[List[str]]			       =		None
		UpperCamelCase     :					Optional[Union[int, str, List[int], List[str]]]			       =		None
		UpperCamelCase     :					Optional[Union[List[int], List[str]]]			       =		None
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					bool			       =		True
		UpperCamelCase     :					Optional[Literal["c", "python", "pyarrow"]]			       =		None
		UpperCamelCase     :					Dict[Union[int, str], Callable[[Any], Any]]			       =		None
		UpperCamelCase     :					Optional[list]			       =		None
		UpperCamelCase     :					Optional[list]			       =		None
		UpperCamelCase     :					bool			       =		False
		UpperCamelCase     :					Optional[Union[int, List[int]]]			       =		None
		UpperCamelCase     :					Optional[int]			       =		None
		UpperCamelCase     :					Optional[Union[str, List[str]]]			       =		None
		UpperCamelCase     :					bool			       =		True
		UpperCamelCase     :					bool			       =		True
		UpperCamelCase     :					bool			       =		False
		UpperCamelCase     :					bool			       =		True
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					str			       =		"."
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					str			       =		'"'
		UpperCamelCase     :					int			       =		0
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					bool			       =		True
		UpperCamelCase     :					bool			       =		True
		UpperCamelCase     :					int			       =		0
		UpperCamelCase     :					bool			       =		True
		UpperCamelCase     :					bool			       =		False
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					int			       =		1_0_0_0_0
		UpperCamelCase     :					Optional[datasets.Features]			       =		None
		UpperCamelCase     :					Optional[str]			       =		"strict"
		UpperCamelCase     :					Literal["error", "warn", "skip"]			       =		"error"
		UpperCamelCase     :					Optional[str]			       =		None







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







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







		@property
		def 					__A   ( self	)  -> List[Any]:







					'''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        __lowercase       (							datasets.ArrowBasedBuilder  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		CsvConfig







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A	)  -> Optional[Any]:







					'''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 					__A   ( self		,      A	)  -> pa.Table:







					'''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 					__A   ( self		,      A	)  -> Union[str, Any]:







					'''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





import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  logging.get_logger()

					# the current default level is logging.WARNING
					lowerCamelCase    		=  logging.get_verbosity()

					logging.set_verbosity_error()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_warning()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_info()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_debug()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					# restore to the original level
					logging.set_verbosity(A	)







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  logging.get_verbosity()

					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					lowerCamelCase    		=  """Testing 1, 2, 3"""

					# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
					if level_origin <= logging.WARNING:
								with CaptureLogger(A	) as cl:
											logger.warning(A	)
								self.assertEqual(cl.out		,      msg + """\n"""	)

					# this is setting the level for all of `transformers.*` loggers
					logging.set_verbosity_error()

					# should not be able to log warnings
					with CaptureLogger(A	) as cl:
								logger.warning(A	)
					self.assertEqual(cl.out		,      """"""	)

					# should be able to log warnings again
					logging.set_verbosity_warning()
					with CaptureLogger(A	) as cl:
								logger.warning(A	)
					self.assertEqual(cl.out		,      msg + """\n"""	)

					# restore to the original level
					logging.set_verbosity(A	)







		@mockenv(TRANSFORMERS_VERBOSITY="""error"""	)
		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()
					# this action activates the env var
					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)

					lowerCamelCase    		=  os.getenv("""TRANSFORMERS_VERBOSITY"""		,      A	)
					lowerCamelCase    		=  logging.log_levels[env_level_str]

					lowerCamelCase    		=  logging.get_verbosity()
					self.assertEqual(
					    A		,      A		,      F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'		,      )

					# restore to the original level
					lowerCamelCase    		=  """"""
					transformers.utils.logging._reset_library_root_logger()







		@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""	)
		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()
					lowerCamelCase    		=  logging.logging.getLogger()
					with CaptureLogger(A	) as cl:
								# this action activates the env var
								logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""		,      cl.out	)

					# no need to restore as nothing was changed







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()

					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					lowerCamelCase    		=  """Testing 1, 2, 3"""

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""	):
								# nothing should be logged as env var disables this method
								with CaptureLogger(A	) as cl:
											logger.warning_advice(A	)
								self.assertEqual(cl.out		,      """"""	)

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""	):
								# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
								with CaptureLogger(A	) as cl:
											logger.warning_advice(A	)
								self.assertEqual(cl.out		,      msg + """\n"""	)






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			disable_progress_bar()
			assert are_progress_bars_disabled()

			enable_progress_bar()
			assert not are_progress_bars_disabled()

