
import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Any:
		assert isinstance(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )
		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   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> List[str]:
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			tmp_path / 'cache'
		SCREAMING_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():
				SCREAMING_SNAKE_CASE_					:	Dict		  =			JsonDatasetReader(SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ,    keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
		_check_json_dataset(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )



@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   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> List[Any]:
		SCREAMING_SNAKE_CASE_					:	str		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_					:	str		  =			(
		    Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_					:	Any		  =			JsonDatasetReader(SCREAMING_SNAKE_CASE_ ,    features=SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ).read()
		_check_json_dataset(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )



@pytest.mark.parametrize(
    'features' ,    [
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ] ,    )
def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> str:
		SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	Tuple		  =			{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
		SCREAMING_SNAKE_CASE_					:	int		  =			features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_					:	str		  =			(
		    Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			JsonDatasetReader(SCREAMING_SNAKE_CASE_ ,    features=SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ).read()
		assert isinstance(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )
		assert dataset.num_rows == 2
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_3", "col_1", "col_2"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Optional[Any]:
		SCREAMING_SNAKE_CASE_					:	str		  =			{'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
		SCREAMING_SNAKE_CASE_					:	List[str]		  =			features.copy()
		SCREAMING_SNAKE_CASE_					:	Tuple		  =			(
		    Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_					:	List[str]		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	int		  =			JsonDatasetReader(SCREAMING_SNAKE_CASE_ ,    features=SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ).read()
		assert isinstance(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )
		assert dataset.num_rows == 2
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_2", "col_3", "col_1"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('split' ,    [None, NamedSplit('train' ), 'train', 'test'] )
def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Tuple:
		SCREAMING_SNAKE_CASE_					:	Dict		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			JsonDatasetReader(SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ,    split=SCREAMING_SNAKE_CASE_ ).read()
		_check_json_dataset(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )
		assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type' ,    [str, list] )
def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Dict:






		if issubclass(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ):
				SCREAMING_SNAKE_CASE_					:	Dict		  =			jsonl_path
		elif issubclass(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ):
				SCREAMING_SNAKE_CASE_					:	Any		  =			[jsonl_path]
		SCREAMING_SNAKE_CASE_					:	str		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	str		  =			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			JsonDatasetReader(SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ).read()
		_check_json_dataset(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE=("train",) )		-> Optional[Any]:
		assert isinstance(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )
		for split in splits:
				SCREAMING_SNAKE_CASE_					:	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   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Union[str, Any]:
		SCREAMING_SNAKE_CASE_					:	int		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	Tuple		  =			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_					:	List[Any]		  =			JsonDatasetReader({'train': jsonl_path} ,    cache_dir=SCREAMING_SNAKE_CASE_ ,    keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
		_check_json_datasetdict(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )



@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   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Any:
		SCREAMING_SNAKE_CASE_					:	int		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			(
		    Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_					:	List[str]		  =			JsonDatasetReader({'train': jsonl_path} ,    features=SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ).read()
		_check_json_datasetdict(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )



@pytest.mark.parametrize('split' ,    [None, NamedSplit('train' ), 'train', 'test'] )
def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Union[str, Any]:






		if split:
				SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			{split: jsonl_path}
		else:
				SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			'train'
				SCREAMING_SNAKE_CASE_					:	List[str]		  =			{'train': jsonl_path, 'test': jsonl_path}
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			tmp_path / 'cache'
		SCREAMING_SNAKE_CASE_					:	Dict		  =			{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			JsonDatasetReader(SCREAMING_SNAKE_CASE_ ,    cache_dir=SCREAMING_SNAKE_CASE_ ).read()
		_check_json_datasetdict(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ,    splits=list(path.keys() ) )
		assert all(dataset[split].split == split for split in path.keys() )



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE )		-> Dict:
		return json.load(SCREAMING_SNAKE_CASE_ )



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE )		-> str:
		return [json.loads(SCREAMING_SNAKE_CASE_ ) for line in buffer]

class 							snake_case_ :





			@pytest.mark.parametrize('lines, load_json_function'      ,						[(True, load_json_lines), (False, load_json)]      )
			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					with io.BytesIO() as buffer:
							JsonDatasetWriter(_lowercase      ,						_lowercase      ,						lines=_lowercase      ).write()
							buffer.seek(0      )
							SCREAMING_SNAKE_CASE_					:	str		  =			load_json_function(_lowercase      )
					assert isinstance(_lowercase      ,						_lowercase      )
					assert isinstance(exported_content[0]      ,						_lowercase      )
					assert len(_lowercase      ) == 10





			@pytest.mark.parametrize(
			    'orient, container, keys, len_at'      ,						[
			        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
			        ('split', dict, {'columns', 'data'}, 'data'),
			        ('index', dict, set('0123456789'      ), None),
			        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
			        ('values', list, None, None),
			        ('table', dict, {'schema', 'data'}, 'data'),
			    ]      ,						)
			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					with io.BytesIO() as buffer:
							JsonDatasetWriter(_lowercase      ,						_lowercase      ,						lines=_lowercase      ,						orient=_lowercase      ).write()
							buffer.seek(0      )
							SCREAMING_SNAKE_CASE_					:	str		  =			load_json(_lowercase      )
					assert isinstance(_lowercase      ,						_lowercase      )
					if keys:
							if container is dict:
									assert exported_content.keys() == keys
							else:
									assert exported_content[0].keys() == keys
					else:
							assert not hasattr(_lowercase      ,						'keys'      ) and not hasattr(exported_content[0]      ,						'keys'      )
					if len_at:
							assert len(exported_content[len_at]      ) == 10
					else:
							assert len(_lowercase      ) == 10





			@pytest.mark.parametrize('lines, load_json_function'      ,						[(True, load_json_lines), (False, load_json)]      )
			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					with io.BytesIO() as buffer:
							JsonDatasetWriter(_lowercase      ,						_lowercase      ,						lines=_lowercase      ,						num_proc=2      ).write()
							buffer.seek(0      )
							SCREAMING_SNAKE_CASE_					:	int		  =			load_json_function(_lowercase      )
					assert isinstance(_lowercase      ,						_lowercase      )
					assert isinstance(exported_content[0]      ,						_lowercase      )
					assert len(_lowercase      ) == 10





			@pytest.mark.parametrize(
			    'orient, container, keys, len_at'      ,						[
			        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
			        ('split', dict, {'columns', 'data'}, 'data'),
			        ('index', dict, set('0123456789'      ), None),
			        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
			        ('values', list, None, None),
			        ('table', dict, {'schema', 'data'}, 'data'),
			    ]      ,						)
			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					with io.BytesIO() as buffer:
							JsonDatasetWriter(_lowercase      ,						_lowercase      ,						lines=_lowercase      ,						orient=_lowercase      ,						num_proc=2      ).write()
							buffer.seek(0      )
							SCREAMING_SNAKE_CASE_					:	Dict		  =			load_json(_lowercase      )
					assert isinstance(_lowercase      ,						_lowercase      )
					if keys:
							if container is dict:
									assert exported_content.keys() == keys
							else:
									assert exported_content[0].keys() == keys
					else:
							assert not hasattr(_lowercase      ,						'keys'      ) and not hasattr(exported_content[0]      ,						'keys'      )
					if len_at:
							assert len(exported_content[len_at]      ) == 10
					else:
							assert len(_lowercase      ) == 10





			def 						__A						(		self      ,						__lowerCAmelCase      ):
					with pytest.raises(_lowercase      ):
							with io.BytesIO() as buffer:
									JsonDatasetWriter(_lowercase      ,						_lowercase      ,						num_proc=0      )






			@pytest.mark.parametrize('compression, extension'      ,						[('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]      )
			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			tmp_path_factory.mktemp('data'      ) / F'test.json.{extension}'
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			str(shared_datadir / F'test_file.json.{extension}'      )
					JsonDatasetWriter(_lowercase      ,						_lowercase      ,						compression=_lowercase      ).write()

					with fsspec.open(_lowercase      ,						'rb'      ,						compression='infer'      ) as f:
							SCREAMING_SNAKE_CASE_					:	Any		  =			f.read()
					with fsspec.open(_lowercase      ,						'rb'      ,						compression='infer'      ) as f:
							SCREAMING_SNAKE_CASE_					:	List[Any]		  =			f.read()
					assert exported_content == original_content









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
