



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 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Any    ):


						'''simple docstring'''






						assert isinstance(__UpperCamelCase					, __UpperCamelCase    )
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			tmp_path / """cache"""
						snake_case_   : List[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():
												snake_case_   : str						=			ParquetDatasetReader(__UpperCamelCase					, cache_dir=__UpperCamelCase					, keep_in_memory=__UpperCamelCase    ).read()
						_check_parquet_dataset(__UpperCamelCase					, __UpperCamelCase    )
@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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path / """cache"""
						snake_case_   : Union[str, Any]						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : Optional[Any]						=			features.copy() if features else default_expected_features
						snake_case_   : Union[str, Any]						=			(
						    Features({feature: Value(__UpperCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						snake_case_   : Optional[Any]						=			ParquetDatasetReader(__UpperCamelCase					, features=__UpperCamelCase					, cache_dir=__UpperCamelCase    ).read()
						_check_parquet_dataset(__UpperCamelCase					, __UpperCamelCase    )
@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			tmp_path / """cache"""
						snake_case_   : Tuple						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : str						=			ParquetDatasetReader(__UpperCamelCase					, cache_dir=__UpperCamelCase					, split=__UpperCamelCase    ).read()
						_check_parquet_dataset(__UpperCamelCase					, __UpperCamelCase    )
						assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""					, [str, list]    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						if issubclass(__UpperCamelCase					, __UpperCamelCase    ):
												snake_case_   : Union[str, Any]						=			parquet_path
						elif issubclass(__UpperCamelCase					, __UpperCamelCase    ):
												snake_case_   : Any						=			[parquet_path]
						snake_case_   : int						=			tmp_path / """cache"""
						snake_case_   : Any						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : Union[str, Any]						=			ParquetDatasetReader(__UpperCamelCase					, cache_dir=__UpperCamelCase    ).read()
						_check_parquet_dataset(__UpperCamelCase					, __UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Optional[int]=("train",)    ):


						'''simple docstring'''






						assert isinstance(__UpperCamelCase					, __UpperCamelCase    )
						for split in splits:
												snake_case_   : Optional[int]						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path / """cache"""
						snake_case_   : Dict						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												snake_case_   : Optional[int]						=			ParquetDatasetReader(
												    {"""train""": parquet_path}					, cache_dir=__UpperCamelCase					, keep_in_memory=__UpperCamelCase    ).read()
						_check_parquet_datasetdict(__UpperCamelCase					, __UpperCamelCase    )
@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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : str						=			tmp_path / """cache"""
						snake_case_   : List[Any]						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : Optional[int]						=			features.copy() if features else default_expected_features
						snake_case_   : Optional[int]						=			(
						    Features({feature: Value(__UpperCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						snake_case_   : Optional[Any]						=			ParquetDatasetReader({"""train""": parquet_path}					, features=__UpperCamelCase					, cache_dir=__UpperCamelCase    ).read()
						_check_parquet_datasetdict(__UpperCamelCase					, __UpperCamelCase    )
@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			Any    ):


						'''simple docstring'''






						if split:
												snake_case_   : Optional[int]						=			{split: parquet_path}
						else:
												snake_case_   : Tuple						=			"""train"""
												snake_case_   : List[Any]						=			{"""train""": parquet_path, """test""": parquet_path}
						snake_case_   : Any						=			tmp_path / """cache"""
						snake_case_   : Union[str, Any]						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : str						=			ParquetDatasetReader(__UpperCamelCase					, cache_dir=__UpperCamelCase    ).read()
						_check_parquet_datasetdict(__UpperCamelCase					, __UpperCamelCase					, splits=list(path.keys()    )    )
						assert all(dataset[split].split == split for split in path.keys()    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : int						=			ParquetDatasetWriter(__UpperCamelCase					, tmp_path / """foo.parquet"""    )
						assert writer.write() > 0
						snake_case_   : str						=			pq.ParquetFile(tmp_path / """foo.parquet"""    )
						snake_case_   : Optional[int]						=			pf.read()
						assert dataset.data.table == output_table
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			str(shared_datadir / """test_image_rgb.jpg"""    )
						snake_case_   : Union[str, Any]						=			{"""image""": [image_path]}
						snake_case_   : Optional[int]						=			Features({"""image""": Image()}    )
						snake_case_   : str						=			Dataset.from_dict(__UpperCamelCase					, features=__UpperCamelCase    )
						snake_case_   : Optional[int]						=			ParquetDatasetWriter(__UpperCamelCase					, tmp_path / """foo.parquet"""    )
						assert writer.write() > 0

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

						snake_case_   : int						=			ParquetDatasetReader(str(tmp_path / """foo.parquet"""    )					, streaming=__UpperCamelCase    ).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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						assert get_writer_batch_size(__UpperCamelCase    ) == expected






"""simple docstring"""





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       MgpstrTokenizer
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       {}
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								super().setUp()

								# fmt: off
								snake_case_   : Optional[Any]						=			["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
								# fmt: on
								snake_case_   : str						=			dict(zip(_lowercase							,    range(len(_lowercase   )   )   )   )

								snake_case_   : Tuple						=			os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES["""vocab_file"""]   )
								with open(self.vocab_file							,    """w"""							,    encoding="""utf-8"""   ) as fp:
														fp.write(json.dumps(_lowercase   ) + """\n"""   )





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								return MgpstrTokenizer.from_pretrained(self.tmpdirname							,    **_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			"""tester"""
								snake_case_   : Tuple						=			"""tester"""
								return input_text, output_text





		@unittest.skip("""MGP-STR always lower cases letters."""   )
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Dict						=			self.get_tokenizers(do_lower_case=_lowercase   )
								for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																				snake_case_   : Optional[Any]						=			"""[SPECIAL_TOKEN]"""

																				tokenizer.add_special_tokens({"""cls_token""": special_token}   )
																				snake_case_   : Union[str, Any]						=			tokenizer.encode([special_token]							,    add_special_tokens=_lowercase   )
																				self.assertEqual(len(_lowercase   )							,    1   )

																				snake_case_   : List[Any]						=			tokenizer.decode(_lowercase							,    skip_special_tokens=_lowercase   )
																				self.assertTrue(special_token not in decoded   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.get_tokenizers()
								for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																				snake_case_			,		snake_case_   : Union[str, Any]						=			self.get_input_output_texts(_lowercase   )

																				snake_case_   : List[Any]						=			tokenizer.tokenize(_lowercase   )
																				snake_case_   : Union[str, Any]						=			tokenizer.convert_tokens_to_ids(_lowercase   )
																				snake_case_   : Union[str, Any]						=			tokenizer.encode(_lowercase							,    add_special_tokens=_lowercase   )
																				self.assertListEqual(_lowercase							,    _lowercase   )

																				snake_case_   : List[str]						=			tokenizer.convert_ids_to_tokens(_lowercase   )
																				self.assertNotEqual(len(_lowercase   )							,    0   )
																				snake_case_   : str						=			tokenizer.decode(_lowercase   )
																				self.assertIsInstance(_lowercase							,    _lowercase   )

																				self.assertEqual(text_a.replace(""" """							,    """"""   )							,    _lowercase   )





		@unittest.skip("""MGP-STR tokenizer only handles one sequence."""   )
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								pass





		@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""   )
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								pass

