


'''simple docstring'''







import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					int						       =       tmp_path / "cache"
				__magic_name__				:					Union[str, Any]						       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__				:					Optional[Any]						       =       CsvDatasetReader(UpperCamelCase__     ,   cache_dir=UpperCamelCase__     ,   keep_in_memory=UpperCamelCase__		).read()
				_check_csv_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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       tmp_path / "cache"
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__magic_name__				:					List[str]						       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				__magic_name__				:					Any						       =       features.copy() if features else default_expected_features
				__magic_name__				:					List[str]						       =       (
				    Features({feature: Value(UpperCamelCase__		) for feature, dtype in features.items()}		) if features is not None else None
				)
				__magic_name__				:					Tuple						       =       CsvDatasetReader(UpperCamelCase__     ,   features=UpperCamelCase__     ,   cache_dir=UpperCamelCase__		).read()
				_check_csv_dataset(UpperCamelCase__     ,   UpperCamelCase__		)



@pytest.mark.parametrize("split"     ,   [None, NamedSplit("train"		), "train", "test"]		)
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[int]						       =       tmp_path / "cache"
				__magic_name__				:					Optional[Any]						       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				__magic_name__				:					Any						       =       CsvDatasetReader(UpperCamelCase__     ,   cache_dir=UpperCamelCase__     ,   split=UpperCamelCase__		).read()
				_check_csv_dataset(UpperCamelCase__     ,   UpperCamelCase__		)
				assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"     ,   [str, list]		)
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				if issubclass(UpperCamelCase__     ,   UpperCamelCase__		):
								__magic_name__				:					Any						       =       csv_path
				elif issubclass(UpperCamelCase__     ,   UpperCamelCase__		):
								__magic_name__				:					List[str]						       =       [csv_path]
				__magic_name__				:					Optional[Any]						       =       tmp_path / "cache"
				__magic_name__				:					Dict						       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				__magic_name__				:					Tuple						       =       CsvDatasetReader(UpperCamelCase__     ,   cache_dir=UpperCamelCase__		).read()
				_check_csv_dataset(UpperCamelCase__     ,   UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=("train",)		):






				"""simple docstring"""



				assert isinstance(UpperCamelCase__     ,   UpperCamelCase__		)
				for split in splits:
								__magic_name__				:					Dict						       =       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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[str]						       =       tmp_path / "cache"
				__magic_name__				:					Optional[Any]						       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__				:					Optional[int]						       =       CsvDatasetReader({"train": csv_path}     ,   cache_dir=UpperCamelCase__     ,   keep_in_memory=UpperCamelCase__		).read()
				_check_csv_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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					int						       =       tmp_path / "cache"
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__magic_name__				:					Tuple						       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				__magic_name__				:					List[str]						       =       features.copy() if features else default_expected_features
				__magic_name__				:					Any						       =       (
				    Features({feature: Value(UpperCamelCase__		) for feature, dtype in features.items()}		) if features is not None else None
				)
				__magic_name__				:					str						       =       CsvDatasetReader({"train": csv_path}     ,   features=UpperCamelCase__     ,   cache_dir=UpperCamelCase__		).read()
				_check_csv_datasetdict(UpperCamelCase__     ,   UpperCamelCase__		)



@pytest.mark.parametrize("split"     ,   [None, NamedSplit("train"		), "train", "test"]		)
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				if split:
								__magic_name__				:					int						       =       {split: csv_path}
				else:
								__magic_name__				:					Optional[Any]						       =       {"train": csv_path, "test": csv_path}
				__magic_name__				:					Any						       =       tmp_path / "cache"
				__magic_name__				:					Dict						       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				__magic_name__				:					Optional[Any]						       =       CsvDatasetReader(UpperCamelCase__     ,   cache_dir=UpperCamelCase__		).read()
				_check_csv_datasetdict(UpperCamelCase__     ,   UpperCamelCase__     ,   splits=list(path.keys()		)		)
				assert all(dataset[split].split == split for split in path.keys()		)



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				with open(UpperCamelCase__     ,   encoding="utf-8"		) as csvfile:
								yield from csv.reader(UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Union[str, Any]						       =       tmp_path / "cache"
				__magic_name__				:					Any						       =       os.path.join(UpperCamelCase__     ,   "tmp.csv"		)
				__magic_name__				:					Tuple						       =       CsvDatasetReader({"train": csv_path}     ,   cache_dir=UpperCamelCase__		).read()
				CsvDatasetWriter(dataset["train"]     ,   UpperCamelCase__     ,   num_proc=1		).write()

				__magic_name__				:					List[Any]						       =       iter_csv_file(UpperCamelCase__		)
				__magic_name__				:					Any						       =       iter_csv_file(UpperCamelCase__		)

				for rowa, rowa in zip(UpperCamelCase__     ,   UpperCamelCase__		):
								assert rowa == rowa



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Union[str, Any]						       =       tmp_path / "cache"
				__magic_name__				:					str						       =       os.path.join(UpperCamelCase__     ,   "tmp.csv"		)
				__magic_name__				:					List[Any]						       =       CsvDatasetReader({"train": csv_path}     ,   cache_dir=UpperCamelCase__		).read()
				CsvDatasetWriter(dataset["train"]     ,   UpperCamelCase__     ,   num_proc=2		).write()

				__magic_name__				:					Union[str, Any]						       =       iter_csv_file(UpperCamelCase__		)
				__magic_name__				:					str						       =       iter_csv_file(UpperCamelCase__		)

				for rowa, rowa in zip(UpperCamelCase__     ,   UpperCamelCase__		):
								assert rowa == rowa




def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[int]						       =       tmp_path / "cache"
				__magic_name__				:					List[Any]						       =       os.path.join(UpperCamelCase__     ,   "tmp.csv"		)
				__magic_name__				:					Optional[Any]						       =       CsvDatasetReader({"train": csv_path}     ,   cache_dir=UpperCamelCase__		).read()
				with pytest.raises(UpperCamelCase__		):
								CsvDatasetWriter(dataset["train"]     ,   UpperCamelCase__     ,   num_proc=0		)



'''simple docstring'''







import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	List[Any]:
										return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       self.tokenizer_ab
										__magic_name__				:					Optional[int]						       =       " I am a small frog."
										__magic_name__				:					Optional[int]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					int						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										__magic_name__				:					Tuple						       =       self.rust_tokenizer_ab
										__magic_name__				:					str						       =       " I am a small frog."
										__magic_name__				:					Union[str, Any]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					Any						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]







						def lowerCAmelCase__ (  self:	List[str]     )							->	List[Any]:
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]