
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
				assert isinstance(lowerCamelCase     ,    lowerCamelCase						)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""     ,    [False, True]						)
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[Any]:
				UpperCamelCase_:							List[str]	=						tmp_path / """cache"""
				UpperCamelCase_:							Any	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								UpperCamelCase_:							Optional[Any]	=						ParquetDatasetReader(lowerCamelCase     ,    cache_dir=lowerCamelCase     ,    keep_in_memory=lowerCamelCase						).read()
				_check_parquet_dataset(lowerCamelCase     ,    lowerCamelCase						)







@pytest.mark.parametrize(
    """features"""     ,    [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]     ,    )
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				UpperCamelCase_:							Optional[Any]	=						tmp_path / """cache"""
				UpperCamelCase_:							Optional[Any]	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Optional[Any]	=						features.copy() if features else default_expected_features
				UpperCamelCase_:							List[Any]	=						(
				    Features({feature: Value(lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
				)
				UpperCamelCase_:							str	=						ParquetDatasetReader(lowerCamelCase     ,    features=lowerCamelCase     ,    cache_dir=lowerCamelCase						).read()
				_check_parquet_dataset(lowerCamelCase     ,    lowerCamelCase						)







@pytest.mark.parametrize("""split"""     ,    [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				UpperCamelCase_:							str	=						tmp_path / """cache"""
				UpperCamelCase_:							int	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Optional[Any]	=						ParquetDatasetReader(lowerCamelCase     ,    cache_dir=lowerCamelCase     ,    split=lowerCamelCase						).read()
				_check_parquet_dataset(lowerCamelCase     ,    lowerCamelCase						)
				assert dataset.split == split if split else "train"







@pytest.mark.parametrize("""path_type"""     ,    [str, list]						)
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Any:
				if issubclass(lowerCamelCase     ,    lowerCamelCase						):
								UpperCamelCase_:							List[str]	=						parquet_path
				elif issubclass(lowerCamelCase     ,    lowerCamelCase						):
								UpperCamelCase_:							Tuple	=						[parquet_path]
				UpperCamelCase_:							Any	=						tmp_path / """cache"""
				UpperCamelCase_:							Optional[Any]	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Optional[Any]	=						ParquetDatasetReader(lowerCamelCase     ,    cache_dir=lowerCamelCase						).read()
				_check_parquet_dataset(lowerCamelCase     ,    lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=("train",)						)  ->	Optional[Any]:
				assert isinstance(lowerCamelCase     ,    lowerCamelCase						)
				for split in splits:
								UpperCamelCase_:							Any	=						dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""     ,    [False, True]						)
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				UpperCamelCase_:							Optional[Any]	=						tmp_path / """cache"""
				UpperCamelCase_:							Union[str, Any]	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								UpperCamelCase_:							Any	=						ParquetDatasetReader(
								    {"""train""": parquet_path}     ,    cache_dir=lowerCamelCase     ,    keep_in_memory=lowerCamelCase						).read()
				_check_parquet_datasetdict(lowerCamelCase     ,    lowerCamelCase						)







@pytest.mark.parametrize(
    """features"""     ,    [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]     ,    )
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Dict:
				UpperCamelCase_:							str	=						tmp_path / """cache"""
				UpperCamelCase_:							Tuple	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Optional[int]	=						features.copy() if features else default_expected_features
				UpperCamelCase_:							Dict	=						(
				    Features({feature: Value(lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
				)
				UpperCamelCase_:							Dict	=						ParquetDatasetReader({"""train""": parquet_path}     ,    features=lowerCamelCase     ,    cache_dir=lowerCamelCase						).read()
				_check_parquet_datasetdict(lowerCamelCase     ,    lowerCamelCase						)







@pytest.mark.parametrize("""split"""     ,    [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
				if split:
								UpperCamelCase_:							List[str]	=						{split: parquet_path}
				else:
								UpperCamelCase_:							int	=						"""train"""
								UpperCamelCase_:							List[str]	=						{"""train""": parquet_path, """test""": parquet_path}
				UpperCamelCase_:							Tuple	=						tmp_path / """cache"""
				UpperCamelCase_:							Union[str, Any]	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Optional[int]	=						ParquetDatasetReader(lowerCamelCase     ,    cache_dir=lowerCamelCase						).read()
				_check_parquet_datasetdict(lowerCamelCase     ,    lowerCamelCase     ,    splits=list(path.keys()						)						)
				assert all(dataset[split].split == split for split in path.keys()						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				UpperCamelCase_:							List[Any]	=						ParquetDatasetWriter(lowerCamelCase     ,    tmp_path / """foo.parquet"""						)
				assert writer.write() > 0
				UpperCamelCase_:							List[str]	=						pq.ParquetFile(tmp_path / """foo.parquet"""						)
				UpperCamelCase_:							Any	=						pf.read()
				assert dataset.data.table == output_table







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Dict:
				UpperCamelCase_:							Tuple	=						str(shared_datadir / """test_image_rgb.jpg"""						)
				UpperCamelCase_:							List[str]	=						{"""image""": [image_path]}
				UpperCamelCase_:							Tuple	=						Features({"""image""": Image()}						)
				UpperCamelCase_:							Any	=						Dataset.from_dict(lowerCamelCase     ,    features=lowerCamelCase						)
				UpperCamelCase_:							str	=						ParquetDatasetWriter(lowerCamelCase     ,    tmp_path / """foo.parquet"""						)
				assert writer.write() > 0

				UpperCamelCase_:							Optional[int]	=						Dataset.from_parquet(str(tmp_path / """foo.parquet"""						)						)
				assert dataset.features == reloaded_dataset.features

				UpperCamelCase_:							str	=						ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)     ,    streaming=lowerCamelCase						).read()
				assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    """feature, expected"""     ,    [
        (Features({"""foo""": Value("""int32"""						)}						), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]     ,    )
def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
				assert get_writer_batch_size(lowerCamelCase						) == expected








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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						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(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""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(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											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(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

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

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

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) 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       lowerCAmelCase__	(       self :       List[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

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

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







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






