

import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json


@pytest.fixture
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''file.jsonl'''
						snake_case_       =     textwrap.dedent(
						    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''	)
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''	) as f:
												f.write(SCREAMING_SNAKE_CASE__	)
						return str(SCREAMING_SNAKE_CASE__	)


@pytest.fixture
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''file_utf16_encoded.jsonl'''
						snake_case_       =     textwrap.dedent(
						    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''	)
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''  ,		encoding='''utf-16'''	) as f:
												f.write(SCREAMING_SNAKE_CASE__	)
						return str(SCREAMING_SNAKE_CASE__	)


@pytest.fixture
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''file_with_list_of_dicts.json'''
						snake_case_       =     textwrap.dedent(
						    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''	)
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''	) as f:
												f.write(SCREAMING_SNAKE_CASE__	)
						return str(SCREAMING_SNAKE_CASE__	)


@pytest.fixture
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''file_with_list_of_dicts_field.json'''
						snake_case_       =     textwrap.dedent(
						    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''	)
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''	) as f:
												f.write(SCREAMING_SNAKE_CASE__	)
						return str(SCREAMING_SNAKE_CASE__	)


@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''  ,		[
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]  ,		)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     Json(**SCREAMING_SNAKE_CASE__	)
						snake_case_       =     json._generate_tables([[request.getfixturevalue(SCREAMING_SNAKE_CASE__	)]]	)
						snake_case_       =     pa.concat_tables([table for _, table in generator]	)
						assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}


@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''  ,		[
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''	), '''col_2''': Value('''int64'''	), '''missing_col''': Value('''string'''	)}	)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''	), '''col_2''': Value('''int64'''	), '''missing_col''': Value('''string'''	)}	)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''	), '''col_2''': Value('''int64'''	), '''missing_col''': Value('''string'''	)}	),
            },
        ),
    ]  ,		)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     Json(**SCREAMING_SNAKE_CASE__	)
						snake_case_       =     json._generate_tables([[request.getfixturevalue(SCREAMING_SNAKE_CASE__	)]]	)
						snake_case_       =     pa.concat_tables([table for _, table in generator]	)
						assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''							,       _UpperCamelCase							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''							)
									if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''							)

									super().__init__(_UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     args[1:]

									if images is None and text is None:
															raise ValueError('''You need to specify either an `images` or `text` input to process.'''							)

									if images is not None:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_       =     encodings['''input_ids''']
															return inputs







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->Any:
									warnings.warn(
									    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
									    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
									    '''your images inputs, or in a separate call.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor_class







			@property
			def     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor