
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			assert isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				)
			assert dataset.num_rows == 4
			assert dataset.num_columns == 1
			assert dataset.column_names == ["text"]
			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__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  List[str]				):




			"""simple docstring"""


			lowerCAmelCase_						=	tmp_path / "cache"
			lowerCAmelCase_						=	{"text": "string"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase_						=	TextDatasetReader(__lowerCAmelCase    ,							cache_dir=__lowerCAmelCase    ,							keep_in_memory=__lowerCAmelCase				).read()
			_check_text_dataset(__lowerCAmelCase    ,							__lowerCAmelCase				)






@pytest.mark.parametrize(
    "features"    ,							[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]    ,							)
def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Any				):




			"""simple docstring"""


			lowerCAmelCase_						=	tmp_path / "cache"
			lowerCAmelCase_						=	{"text": "string"}
			lowerCAmelCase_						=	features.copy() if features else default_expected_features
			lowerCAmelCase_						=	(
			    Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
			)
			lowerCAmelCase_						=	TextDatasetReader(__lowerCAmelCase    ,							features=__lowerCAmelCase    ,							cache_dir=__lowerCAmelCase				).read()
			_check_text_dataset(__lowerCAmelCase    ,							__lowerCAmelCase				)






@pytest.mark.parametrize("split"    ,							[None, NamedSplit("train"				), "train", "test"]				)
def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Optional[Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	tmp_path / "cache"
			lowerCAmelCase_						=	{"text": "string"}
			lowerCAmelCase_						=	TextDatasetReader(__lowerCAmelCase    ,							cache_dir=__lowerCAmelCase    ,							split=__lowerCAmelCase				).read()
			_check_text_dataset(__lowerCAmelCase    ,							__lowerCAmelCase				)
			assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"    ,							[str, list]				)
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  List[Any]				):




			"""simple docstring"""


			if issubclass(__lowerCAmelCase    ,							__lowerCAmelCase				):
						lowerCAmelCase_						=	text_path
			elif issubclass(__lowerCAmelCase    ,							__lowerCAmelCase				):
						lowerCAmelCase_						=	[text_path]
			lowerCAmelCase_						=	tmp_path / "cache"
			lowerCAmelCase_						=	{"text": "string"}
			lowerCAmelCase_						=	TextDatasetReader(__lowerCAmelCase    ,							cache_dir=__lowerCAmelCase				).read()
			_check_text_dataset(__lowerCAmelCase    ,							__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Union[str, Any]=("train",)				):




			"""simple docstring"""


			assert isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				)
			for split in splits:
						lowerCAmelCase_						=	dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 1
						assert dataset.column_names == ["text"]
						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__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Optional[Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	tmp_path / "cache"
			lowerCAmelCase_						=	{"text": "string"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase_						=	TextDatasetReader({"train": text_path}    ,							cache_dir=__lowerCAmelCase    ,							keep_in_memory=__lowerCAmelCase				).read()
			_check_text_datasetdict(__lowerCAmelCase    ,							__lowerCAmelCase				)






@pytest.mark.parametrize(
    "features"    ,							[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]    ,							)
def    lowerCamelCase__				(				__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  Dict				):




			"""simple docstring"""


			lowerCAmelCase_						=	tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase_						=	{"text": "string"}
			lowerCAmelCase_						=	features.copy() if features else default_expected_features
			lowerCAmelCase_						=	(
			    Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
			)
			lowerCAmelCase_						=	TextDatasetReader({"train": text_path}    ,							features=__lowerCAmelCase    ,							cache_dir=__lowerCAmelCase				).read()
			_check_text_datasetdict(__lowerCAmelCase    ,							__lowerCAmelCase				)






@pytest.mark.parametrize("split"    ,							[None, NamedSplit("train"				), "train", "test"]				)
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


			if split:
						lowerCAmelCase_						=	{split: text_path}
			else:
						lowerCAmelCase_						=	"train"
						lowerCAmelCase_						=	{"train": text_path, "test": text_path}
			lowerCAmelCase_						=	tmp_path / "cache"
			lowerCAmelCase_						=	{"text": "string"}
			lowerCAmelCase_						=	TextDatasetReader(__lowerCAmelCase    ,							cache_dir=__lowerCAmelCase				).read()
			_check_text_datasetdict(__lowerCAmelCase    ,							__lowerCAmelCase    ,							splits=list(path.keys()				)				)
			assert all(dataset[split].split == split for split in path.keys()				)





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=None
				_lowercase					=BloomTokenizerFast
				_lowercase					=BloomTokenizerFast
				_lowercase					=True
				_lowercase					=False
				_lowercase					='''tokenizer_file'''
				_lowercase					={'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def      __a						(					self     )				->      Dict:
							super().setUp()
							lowerCAmelCase_						=	BloomTokenizerFast.from_pretrained("bigscience/tokenizer"     )
							tokenizer.save_pretrained(self.tmpdirname     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Tuple:
							kwargs.update(self.special_tokens_map     )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.get_rust_tokenizer()

							lowerCAmelCase_						=	["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase_						=	[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

							lowerCAmelCase_						=	tokenizer.batch_encode_plus(_UpperCamelCase     )["input_ids"]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokenizer.batch_decode(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase=6     )				->      Tuple:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													lowerCAmelCase_						=	"This is a simple input"
													lowerCAmelCase_						=	["This is a simple input 1", "This is a simple input 2"]
													lowerCAmelCase_						=	("This is a simple input", "This is a pair")
													lowerCAmelCase_						=	[
													    ("This is a simple input 1", "This is a simple input 2"),
													    ("This is a simple pair 1", "This is a simple pair 2"),
													]

													# Simple input tests
													try:
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )

																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
													except ValueError:
																self.fail("Bloom Tokenizer should be able to deal with padding"     )

													lowerCAmelCase_						=	None  # Hotfixing padding = None
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Simple input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Simple input
													self.assertRaises(
													    _UpperCamelCase				,				tokenizer_r.batch_encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"				,				)

													# Pair input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Pair input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Pair input
													self.assertRaises(
													    _UpperCamelCase				,				tokenizer_r.batch_encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"				,				)




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.get_rust_tokenizer()
							lowerCAmelCase_						=	load_dataset("xnli"				,				"all_languages"				,				split="test"				,				streaming=_UpperCamelCase     )

							lowerCAmelCase_						=	next(iter(_UpperCamelCase     )     )["premise"]  # pick up one data
							lowerCAmelCase_						=	list(sample_data.values()     )

							lowerCAmelCase_						=	list(map(tokenizer.encode				,				_UpperCamelCase     )     )
							lowerCAmelCase_						=	[tokenizer.decode(_UpperCamelCase				,				clean_up_tokenization_spaces=_UpperCamelCase     ) for x in output_tokens]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
							# any sequence length constraints. This test of the parent class will fail since it relies on the
							# maximum sequence length of the positoonal embeddings.
							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )				,				1     )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )				,				1     )



