



'''simple docstring'''



import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict



@pytest.mark.parametrize(
    'files'  ,   [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=tmp_path_factory.mktemp('dset_infos_dir'					)
							if "full:README.md" in files:
														with open(dataset_infos_dir / 'README.md'  ,   'w'					) as f:
																					f.write('---\ndataset_info:\n  dataset_size: 42\n---'					)
							if "empty:README.md" in files:
														with open(dataset_infos_dir / 'README.md'  ,   'w'					) as f:
																					f.write(''					)
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / 'dataset_infos.json'  ,   'w'					) as f:
																					f.write('{"default": {"dataset_size": 42}}'					)
							__lowercase       	=DatasetInfosDict.from_directory(_lowerCAmelCase					)
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42



@pytest.mark.parametrize(
    'dataset_info'  ,   [
        DatasetInfo(),
        DatasetInfo(
            description='foo'  ,   features=Features({'a': Value('int32'					)}					)  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train'}]  ,   download_size=42  ,   ),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=str(_lowerCAmelCase					)
							dataset_info.write_to_directory(_lowerCAmelCase					)
							__lowercase       	=DatasetInfo.from_directory(_lowerCAmelCase					)
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(_lowerCAmelCase  ,   'dataset_info.json'					)					)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=DatasetInfo(
							    description='foo'  ,   citation='bar'  ,   homepage='https://foo.bar'  ,   license='CC0'  ,   features=Features({'a': Value('int32'					)}					)  ,   post_processed={}  ,   supervised_keys=()  ,   task_templates=[]  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train', 'num_examples': 42}]  ,   download_checksums={}  ,   download_size=1_337  ,   post_processing_size=442  ,   dataset_size=1_234  ,   size_in_bytes=1_337 + 442 + 1_234  ,   )
							__lowercase       	=dataset_info._to_yaml_dict()
							assert sorted(_lowerCAmelCase					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]  ,   (list, dict, int, str)					)
							__lowercase       	=yaml.safe_dump(_lowerCAmelCase					)
							__lowercase       	=yaml.safe_load(_lowerCAmelCase					)
							assert dataset_info_yaml_dict == reloaded



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=DatasetInfo()
							__lowercase       	=dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}



@pytest.mark.parametrize(
    'dataset_infos_dict'  ,   [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}					),
        DatasetInfosDict({'my_config_name': DatasetInfo()}					),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'  ,   features=Features({'a': Value('int32'					)}					)  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train'}]  ,   download_size=42  ,   )
            }					),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42					),
                'v2': DatasetInfo(dataset_size=1_337					),
            }					),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=str(_lowerCAmelCase					)
							dataset_infos_dict.write_to_directory(_lowerCAmelCase					)
							__lowercase       	=DatasetInfosDict.from_directory(_lowerCAmelCase					)

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														__lowercase       	=config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														__lowercase       	=DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(_lowerCAmelCase  ,   'README.md'					)					)








'''simple docstring'''



import warnings
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 _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    ["""image_processor""", """tokenizer"""]
				lowerCAmelCase__					    =    """ViltImageProcessor"""
				lowerCAmelCase__					    =    ("""BertTokenizer""", """BertTokenizerFast""")

				def __init__(							self  :  str  ,       _lowerCAmelCase  :  Optional[Any]=None  ,       _lowerCAmelCase  :  int=None  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'  ,       _lowerCAmelCase  ,       )
																		__lowercase       	=kwargs.pop('feature_extractor')

											__lowercase       	=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__(_lowerCAmelCase  ,       _lowerCAmelCase)
											__lowercase       	=self.image_processor

				def __call__(							self  :  List[Any]  ,       _lowerCAmelCase  :  Tuple  ,       _lowerCAmelCase  :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Union[bool, str, PaddingStrategy] = False  ,       _lowerCAmelCase  :  Union[bool, str, TruncationStrategy] = None  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  int = 0  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[bool] = None  ,       _lowerCAmelCase  :  Optional[bool] = None  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[Union[str, TensorType]] = None  ,       **_lowerCAmelCase  :  Any  ,       ):







											'''simple docstring'''

											__lowercase       	=self.tokenizer(
											    text=_lowerCAmelCase  ,       add_special_tokens=_lowerCAmelCase  ,       padding=_lowerCAmelCase  ,       truncation=_lowerCAmelCase  ,       max_length=_lowerCAmelCase  ,       stride=_lowerCAmelCase  ,       pad_to_multiple_of=_lowerCAmelCase  ,       return_token_type_ids=_lowerCAmelCase  ,       return_attention_mask=_lowerCAmelCase  ,       return_overflowing_tokens=_lowerCAmelCase  ,       return_special_tokens_mask=_lowerCAmelCase  ,       return_offsets_mapping=_lowerCAmelCase  ,       return_length=_lowerCAmelCase  ,       verbose=_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )
											# add pixel_values + pixel_mask
											__lowercase       	=self.image_processor(_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase)
											encoding.update(_lowerCAmelCase)

											return encoding

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.tokenizer.decode(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				@property
				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=self.tokenizer.model_input_names
											__lowercase       	=self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

				@property
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,       _lowerCAmelCase  ,       )
											return self.image_processor_class

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,       _lowerCAmelCase  ,       )
											return self.image_processor



