



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType






@dataclass
class     __a			:
	SCREAMING_SNAKE_CASE  	=							42
	SCREAMING_SNAKE_CASE  	=							None
	# Automatically constructed
	SCREAMING_SNAKE_CASE  	=							"dict"
	SCREAMING_SNAKE_CASE  	=							None
	SCREAMING_SNAKE_CASE  	=							field(default="Translation"						,     init=SCREAMING_SNAKE_CASE						,     repr=SCREAMING_SNAKE_CASE					)



	def __call__(    self				:     int)->				str:
						return pa.struct({lang: pa.string() for lang in sorted(self.languages)})



	def 		UpperCamelCase				(    self				:     List[Any])->				Union["FeatureType", Dict[str, "FeatureType"]]:
						from .features import Value

						return {k: Value("""string""") for k in sorted(self.languages)}






@dataclass
class     __a			:
	SCREAMING_SNAKE_CASE  	=							None
	SCREAMING_SNAKE_CASE  	=							None
	SCREAMING_SNAKE_CASE  	=							None
	# Automatically constructed
	SCREAMING_SNAKE_CASE  	=							"dict"
	SCREAMING_SNAKE_CASE  	=							None
	SCREAMING_SNAKE_CASE  	=							field(default="TranslationVariableLanguages"						,     init=SCREAMING_SNAKE_CASE						,     repr=SCREAMING_SNAKE_CASE					)



	def 		UpperCamelCase				(    self				:     str)->				str:
						__lowerCAmelCase						 =sorted(set(self.languages)) if self.languages else None
						__lowerCAmelCase						 =len(self.languages) if self.languages else None



	def __call__(    self				:     List[str])->				List[str]:
						return pa.struct({"""language""": pa.list_(pa.string()), """translation""": pa.list_(pa.string())})



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     Tuple)->				str:
						__lowerCAmelCase						 =set(self.languages)
						if self.languages and set(snake_case_) - lang_set:
											raise ValueError(
											    F"""Some languages in example ({', '.join(sorted(set(snake_case_) - lang_set))}) are not in valid set ({', '.join(snake_case_)}).""")

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						__lowerCAmelCase						 =[]
						for lang, text in translation_dict.items():
											if isinstance(snake_case_					,						snake_case_):
																translation_tuples.append((lang, text))
											else:
																translation_tuples.extend([(lang, el) for el in text])

        # Ensure translations are in ascending order by language code.
						__lowerCAmelCase ,						__lowerCAmelCase						 =zip(*sorted(snake_case_))

						return {"language": languages, "translation": translations}



	def 		UpperCamelCase				(    self				:     Dict)->				Union["FeatureType", Dict[str, "FeatureType"]]:
						from .features import Sequence, Value

						return {
						    "language": Sequence(Value("""string""")),
						    "translation": Sequence(Value("""string""")),
						}









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[Any]					, __lowerCamelCase						:     Any						)	->    Dict:
					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						:     Tuple					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]						)	->    List[Any]:
					__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						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Tuple						)	->    List[Any]:
					__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						:     Union[str, Any]					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Union[str, Any]						)	->    Any:
					__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[Any]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]						)	->    Any:
					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						:     Tuple					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     str=("train",)						)	->    Dict:
					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						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Dict:
					__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						:     Optional[int]					, __lowerCamelCase						:     Union[str, Any]						)	->    Tuple:
					__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						:     int					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any						)	->    int:
					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()						)




