
'''simple docstring'''
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_(     A						:		Optional[int]		,     A						:		Union[str, Any]):
							assert isinstance(A		,     A)
							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_(     A						:		Tuple		,     A						:		Optional[int]		,     A						:		Tuple):
							UpperCamelCase   			=     tmp_path / 'cache'
							UpperCamelCase   			=     {'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   			=     ParquetDatasetReader(A		,     cache_dir=A		,     keep_in_memory=A).read()
							_check_parquet_dataset(A		,     A)







@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_(     A						:		List[Any]		,     A						:		int		,     A						:		str):
							UpperCamelCase   			=     tmp_path / 'cache'
							UpperCamelCase   			=     {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
							UpperCamelCase   			=     features.copy() if features else default_expected_features
							UpperCamelCase   			=     (
							    Features({feature: Value(A) for feature, dtype in features.items()}) if features is not None else None
							)
							UpperCamelCase   			=     ParquetDatasetReader(A		,     features=A		,     cache_dir=A).read()
							_check_parquet_dataset(A		,     A)







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







@pytest.mark.parametrize('path_type'		,     [str, list])
def 		A_(     A						:		str		,     A						:		List[str]		,     A						:		List[Any]):
							if issubclass(A		,     A):
														UpperCamelCase   			=     parquet_path
							elif issubclass(A		,     A):
														UpperCamelCase   			=     [parquet_path]
							UpperCamelCase   			=     tmp_path / 'cache'
							UpperCamelCase   			=     {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
							UpperCamelCase   			=     ParquetDatasetReader(A		,     cache_dir=A).read()
							_check_parquet_dataset(A		,     A)







def 		A_(     A						:		str		,     A						:		Any		,     A						:		Union[str, Any]=("train",)):
							assert isinstance(A		,     A)
							for split in splits:
														UpperCamelCase   			=     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_(     A						:		List[Any]		,     A						:		Optional[int]		,     A						:		Optional[int]):
							UpperCamelCase   			=     tmp_path / 'cache'
							UpperCamelCase   			=     {'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   			=     ParquetDatasetReader(
														    {'train': parquet_path}		,     cache_dir=A		,     keep_in_memory=A).read()
							_check_parquet_datasetdict(A		,     A)







@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_(     A						:		int		,     A						:		Optional[Any]		,     A						:		Optional[Any]):
							UpperCamelCase   			=     tmp_path / 'cache'
							UpperCamelCase   			=     {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
							UpperCamelCase   			=     features.copy() if features else default_expected_features
							UpperCamelCase   			=     (
							    Features({feature: Value(A) for feature, dtype in features.items()}) if features is not None else None
							)
							UpperCamelCase   			=     ParquetDatasetReader({'train': parquet_path}		,     features=A		,     cache_dir=A).read()
							_check_parquet_datasetdict(A		,     A)







@pytest.mark.parametrize('split'		,     [None, NamedSplit('train'), 'train', 'test'])
def 		A_(     A						:		List[str]		,     A						:		Any		,     A						:		Tuple):
							if split:
														UpperCamelCase   			=     {split: parquet_path}
							else:
														UpperCamelCase   			=     'train'
														UpperCamelCase   			=     {'train': parquet_path, 'test': parquet_path}
							UpperCamelCase   			=     tmp_path / 'cache'
							UpperCamelCase   			=     {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
							UpperCamelCase   			=     ParquetDatasetReader(A		,     cache_dir=A).read()
							_check_parquet_datasetdict(A		,     A		,     splits=list(path.keys()))
							assert all(dataset[split].split == split for split in path.keys())







def 		A_(     A						:		int		,     A						:		Optional[int]):
							UpperCamelCase   			=     ParquetDatasetWriter(A		,     tmp_path / 'foo.parquet')
							assert writer.write() > 0
							UpperCamelCase   			=     pq.ParquetFile(tmp_path / 'foo.parquet')
							UpperCamelCase   			=     pf.read()
							assert dataset.data.table == output_table







def 		A_(     A						:		int		,     A						:		List[Any]):
							UpperCamelCase   			=     str(shared_datadir / 'test_image_rgb.jpg')
							UpperCamelCase   			=     {'image': [image_path]}
							UpperCamelCase   			=     Features({'image': Image()})
							UpperCamelCase   			=     Dataset.from_dict(A		,     features=A)
							UpperCamelCase   			=     ParquetDatasetWriter(A		,     tmp_path / 'foo.parquet')
							assert writer.write() > 0

							UpperCamelCase   			=     Dataset.from_parquet(str(tmp_path / 'foo.parquet'))
							assert dataset.features == reloaded_dataset.features

							UpperCamelCase   			=     ParquetDatasetReader(str(tmp_path / 'foo.parquet')		,     streaming=A).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_(     A						:		Union[str, Any]		,     A						:		List[Any]):
							assert get_writer_batch_size(A) == expected







import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)