"""simple docstring"""

import warnings

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






class 		_lowercase (						lowercase__      ):



						"""simple docstring"""




						lowercase__       =  ['''image_processor''', '''tokenizer''']
						lowercase__       =  '''AutoImageProcessor'''
						lowercase__       =  '''AutoTokenizer'''





						def __init__(				self							:    Tuple     , UpperCamelCase__							:    int=None     , UpperCamelCase__							:    Tuple=None     , **UpperCamelCase__							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=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     , )
																				__UpperCamelCase							=kwargs.pop('''feature_extractor'''				)

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

													super().__init__(__lowerCamelCase     , __lowerCamelCase				)
													__UpperCamelCase							=self.image_processor





						def __call__(				self							:    Dict     , UpperCamelCase__							:    Optional[int]=None     , UpperCamelCase__							:    Dict=None     , UpperCamelCase__							:    str=None     , **UpperCamelCase__							:    Dict				)				->				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:
																				__UpperCamelCase							=self.tokenizer(__lowerCamelCase     , return_tensors=__lowerCamelCase     , **__lowerCamelCase				)

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

													if text is not None and images is not None:
																				__UpperCamelCase							=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							:    Optional[int]     , *UpperCamelCase__							:    Optional[int]     , **UpperCamelCase__							:    Tuple				)				->				Union[str, Any]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    Optional[Any]     , *UpperCamelCase__							:    Tuple     , **UpperCamelCase__							:    Dict				)				->				int:
													'''simple docstring'''



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





						@property
						def   UpperCAmelCase_ (				self							:    str				)				->				Optional[Any]:
													'''simple docstring'''



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





						@property
						def   UpperCAmelCase_ (				self							:    Dict				)				->				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   UpperCAmelCase_ (				self							:    Optional[int]				)				->				List[str]:
													'''simple docstring'''



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







import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def  lowercase_						(						_A     :       str		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    min(re.search("^ +"       ,   _A		).end() if t.startswith(" "		) else 0 for t in string.splitlines()		)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A		)]		)


A :       Optional[int]						=   "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

A :       str						=   "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


A :       Optional[Any]						=   "\\n# Begin of markdown\n\nSome cool dataset card\n"







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             with tempfile.TemporaryDirectory() as tmp_dir:
                   lowerCamelCase__						:							List[str]       =    Path(__lowerCamelCase			) / "README.md"
                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Dict       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Optional[Any]       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							int       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertEqual(__lowerCamelCase   , {}			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "			)
             with self.assertRaises(__lowerCamelCase			):
                   DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Optional[Any]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)


