






'''simple docstring'''




import warnings

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

class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						['''image_processor''', '''tokenizer''']
   snake_case    				=						'''CLIPImageProcessor'''
   snake_case    				=						('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')

   def __init__(		self :	Any      ,      __UpperCAmelCase :	List[Any]=None      ,      __UpperCAmelCase :	List[Any]=None      ,      **__UpperCAmelCase :	str		):





         '''simple docstring'''





         _A			     =	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      ,      )
               _A			     =	kwargs.pop("feature_extractor"		)

         _A			     =	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		)

   def __call__(		self :	Any      ,      __UpperCAmelCase :	int=None      ,      __UpperCAmelCase :	Tuple=None      ,      __UpperCAmelCase :	List[Any]=None      ,      **__UpperCAmelCase :	List[Any]		):





         '''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:
               _A			     =	self.tokenizer(__UpperCAmelCase      ,      return_tensors=__UpperCAmelCase      ,      **__UpperCAmelCase		)

         if images is not None:
               _A			     =	self.image_processor(__UpperCAmelCase      ,      return_tensors=__UpperCAmelCase      ,      **__UpperCAmelCase		)

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

   def 						lowerCAmelCase (		self :	List[str]      ,      *__UpperCAmelCase :	Tuple      ,      **__UpperCAmelCase :	List[Any]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Optional[int]      ,      *__UpperCAmelCase :	Any      ,      **__UpperCAmelCase :	Any		):





         '''simple docstring'''





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

   @property
   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





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









'''simple docstring'''
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


UpperCamelCase__    					=      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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[int]:
    return (abs(source - target						) / target) < 0.0_1







@pytest.mark.integration
def 					a__       (					lowerCAmelCase__						)    ->	Optional[int]:
    UpperCAmelCase__			:		str													=				_TestCommandArgs(dataset=lowerCAmelCase__  ,					all_configs=lowerCAmelCase__  ,					save_infos=lowerCAmelCase__						)
    UpperCAmelCase__			:		Tuple													=				TestCommand(*lowerCAmelCase__						)
    test_command.run()
    UpperCAmelCase__			:		List[Any]													=				os.path.join(lowerCAmelCase__  ,					'''README.md'''						)
    assert os.path.exists(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				DatasetInfosDict.from_directory(lowerCAmelCase__						)
    UpperCAmelCase__			:		Any													=				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:
        UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				getattr(dataset_infos['''default''']  ,					lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']  ,					lowerCAmelCase__						)
        if key == "num_bytes":
            assert is_apercent_close(lowerCAmelCase__  ,					lowerCAmelCase__						)
        elif key == "splits":
            assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
            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
