
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A		: Dict								=     namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Any:
						return (abs(source - target       ) / target) < 0.01




@pytest.mark.integration
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     List[str]:
						_lowercase					       =					_TestCommandArgs(dataset=SCREAMING_SNAKE_CASE_		,   all_configs=SCREAMING_SNAKE_CASE_		,   save_infos=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					TestCommand(*SCREAMING_SNAKE_CASE_       )
						test_command.run()
						_lowercase					       =					os.path.join(SCREAMING_SNAKE_CASE_		,   """README.md"""       )
						assert os.path.exists(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					DatasetInfosDict(
						    {
						        """default""": DatasetInfo(
						            features=Features(
						                {
						                    """tokens""": Sequence(Value("""string"""       )       ),
						                    """ner_tags""": Sequence(
						                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]       )       ),
						                    """langs""": Sequence(Value("""string"""       )       ),
						                    """spans""": Sequence(Value("""string"""       )       ),
						                }       )		,   splits=[
						                {
						                    """name""": """train""",
						                    """num_bytes""": 2_35_15_63,
						                    """num_examples""": 1_00_00,
						                },
						                {
						                    """name""": """validation""",
						                    """num_bytes""": 23_84_18,
						                    """num_examples""": 10_00,
						                },
						            ]		,   download_size=3_94_06_80		,   dataset_size=2_58_99_81		,   )
						    }       )
						assert dataset_infos.keys() == expected_dataset_infos.keys()
						for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
												_lowercase       ,						_lowercase					       =					getattr(dataset_infos["""default"""]		,   SCREAMING_SNAKE_CASE_       ), getattr(expected_dataset_infos["""default"""]		,   SCREAMING_SNAKE_CASE_       )
												if key == "num_bytes":
																		assert is_apercent_close(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												elif key == "splits":
																		assert list(SCREAMING_SNAKE_CASE_       ) == list(SCREAMING_SNAKE_CASE_       )
																		for split in result:
																								assert result[split].name == expected[split].name
																								assert result[split].num_examples == expected[split].num_examples
																								assert is_apercent_close(result[split].num_bytes		,   expected[split].num_bytes       )
												else:
																		result == expected

import warnings

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



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_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."""				,			__UpperCamelCase				,			)
															_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__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									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					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

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

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_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    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class