





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  )					->		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  )					->		Optional[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_:							Union[str, 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  )					->		Any:
				UpperCamelCase_:							int		=  tmp_path / """cache"""
				UpperCamelCase_:							Tuple		=  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Union[str, Any]		=  features.copy() if features else default_expected_features
				UpperCamelCase_:							Optional[int]		=  (
				    Features({feature: Value(lowerCamelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
				)
				UpperCamelCase_:							Tuple		=  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  )					->		List[Any]:
				UpperCamelCase_:							Union[str, Any]		=  tmp_path / """cache"""
				UpperCamelCase_:							Optional[int]		=  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Union[str, 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  )					->		List[Any]:
				if issubclass(lowerCamelCase       ,      lowerCamelCase  ):
								UpperCamelCase_:							Optional[Any]		=  parquet_path
				elif issubclass(lowerCamelCase       ,      lowerCamelCase  ):
								UpperCamelCase_:							Tuple		=  [parquet_path]
				UpperCamelCase_:							Optional[Any]		=  tmp_path / """cache"""
				UpperCamelCase_:							Optional[Any]		=  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Tuple		=  ParquetDatasetReader(lowerCamelCase       ,      cache_dir=lowerCamelCase  ).read()
				_check_parquet_dataset(lowerCamelCase       ,      lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=("train",)  )					->		Union[str, Any]:
				assert isinstance(lowerCamelCase       ,      lowerCamelCase  )
				for split in splits:
								UpperCamelCase_:							List[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  )					->		int:
				UpperCamelCase_:							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_:							List[str]		=  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  )					->		Any:
				UpperCamelCase_:							Tuple		=  tmp_path / """cache"""
				UpperCamelCase_:							Optional[Any]		=  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Union[str, Any]		=  features.copy() if features else default_expected_features
				UpperCamelCase_:							Any		=  (
				    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  )					->		Tuple:
				if split:
								UpperCamelCase_:							List[Any]		=  {split: parquet_path}
				else:
								UpperCamelCase_:							Dict		=  """train"""
								UpperCamelCase_:							Optional[int]		=  {"""train""": parquet_path, """test""": parquet_path}
				UpperCamelCase_:							List[Any]		=  tmp_path / """cache"""
				UpperCamelCase_:							Dict		=  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Any		=  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  )					->		List[Any]:
				UpperCamelCase_:							Tuple		=  ParquetDatasetWriter(lowerCamelCase       ,      tmp_path / """foo.parquet"""  )
				assert writer.write() > 0
				UpperCamelCase_:							Dict		=  pq.ParquetFile(tmp_path / """foo.parquet"""  )
				UpperCamelCase_:							Union[str, Any]		=  pf.read()
				assert dataset.data.table == output_table







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

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

				UpperCamelCase_:							Tuple		=  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  )					->		List[str]:
				assert get_writer_batch_size(lowerCamelCase  ) == expected








import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizer
			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizerFast
			__UpperCamelCase :  Optional[Any]    =	True
			__UpperCamelCase :  Tuple    =	True

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							super().setUp()

			def  lowerCAmelCase__		(    self	:    List[str]  ,		**snake_case_	:    Union[str, Any] ):
							return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		**snake_case_	:    Union[str, Any] ):
							return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							int		=  """永和服装饰品有限公司,今天天气非常好"""
							UpperCamelCase_:							int		=  """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
							return input_text, output_text

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Dict		=  self.get_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							Optional[int]		=  tokenizer.tokenize(snake_case_ )

							self.assertListEqual(snake_case_  ,		output_text.split() )

							UpperCamelCase_:							Dict		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							int		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[int]		=  self.get_rust_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Any		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		output_text.split() )
							UpperCamelCase_:							Union[str, Any]		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							Optional[Any]		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							pass

			def  lowerCAmelCase__		(    self	:    Tuple ):
							pass




			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							pass






