


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class _lowerCamelCase(		_a     ):
       lowercase_				:			List[Any]									=    ["""image_processor""", """tokenizer"""]
       lowercase_				:			str									=    """CLIPImageProcessor"""
       lowercase_				:			Optional[int]									=    ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
       def __init__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							List[Any]   	=	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 :							Union[str, Any]   	=	kwargs.pop('feature_extractor')

           _lowercase :							List[str]   	=	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)
       def __call__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

           if text is None and images is None:
               raise ValueError('You have to specify either text or images. Both cannot be none.')

           if text is not None:
               _lowercase :							int   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if images is not None:
               _lowercase :							int   	=	self.image_processor(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if text is not None and images is not None:
               _lowercase :							List[str]   	=	image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowerCamelCase),   tensor_type=lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

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




       @property
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Dict   	=	self.tokenizer.model_input_names
           _lowercase :							str   	=	self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		List[str]:
    _lowercase :							Any   	=	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 :							Any   	=	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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[int]:
    _lowercase :							Union[str, Any]   	=	str(lowerCamelCase_				)
    dataset_info.write_to_directory(lowerCamelCase_				)
    _lowercase :							List[str]   	=	DatasetInfo.from_directory(lowerCamelCase_				)
    assert dataset_info == reloaded
    assert os.path.exists(os.path.join(lowerCamelCase_							,				'dataset_info.json'				)				)



def        UpperCamelCase_(   )					->		int:
    _lowercase :							Tuple   	=	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=1337							,				post_processing_size=442							,				dataset_size=1234							,				size_in_bytes=1337 + 442 + 1234							,				)
    _lowercase :							Optional[int]   	=	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 :							str   	=	yaml.safe_dump(lowerCamelCase_				)
    _lowercase :							str   	=	yaml.safe_load(lowerCamelCase_				)
    assert dataset_info_yaml_dict == reloaded



def        UpperCamelCase_(   )					->		int:
    _lowercase :							Tuple   	=	DatasetInfo()
    _lowercase :							Tuple   	=	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=1337				),
            }				),
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    _lowercase :							Tuple   	=	str(lowerCamelCase_				)
    dataset_infos_dict.write_to_directory(lowerCamelCase_				)
    _lowercase :							Tuple   	=	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 :							Any   	=	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 :							str   	=	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'				)				)


