

"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


_A						 =   """__DUMMY_TRANSFORMERS_USER__"""
_A						 =   """Dummy User"""
_A						 =   """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"""

_A						 =   """https://hub-ci.huggingface.co"""
_A						 =   CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
_A						 =   CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
_A						 =   Path("""~/.huggingface/hub_ci_token""").expanduser()




@pytest.fixture
def  a__		(		lowerCAmelCase  )     ->      List[str]:
      monkeypatch.setattr(
          """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""       ,     __a  )




@pytest.fixture
def  a__		(		lowerCAmelCase  )     ->      Any:
      monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""       ,     __a  )
      monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""       ,     __a  )




@pytest.fixture
def  a__		(		lowerCAmelCase  )     ->      Any:
      monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""       ,     __a  )




@pytest.fixture
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase  )     ->      Dict:
      HfFolder.save_token(__a  )
      yield
      HfFolder.delete_token()




@pytest.fixture(scope="""session"""  )
def  a__		(		)     ->      int:
      return HfApi(endpoint=__a  )




@pytest.fixture(scope="""session"""  )
def  a__		(		lowerCAmelCase  )     ->      Any:
      UpperCAmelCase__       :				List[str]						 =  HfFolder.get_token()
      HfFolder.save_token(__a  )
      yield CI_HUB_USER_TOKEN
      if previous_token is not None:
            HfFolder.save_token(__a  )




@pytest.fixture
def  a__		(		lowerCAmelCase  )     ->      Dict:

      def _cleanup_repo(lowerCAmelCase  ):
            hf_api.delete_repo(__a       ,     token=__a       ,     repo_type="""dataset"""  )

      return _cleanup_repo




@pytest.fixture
def  a__		(		lowerCAmelCase  )     ->      Optional[int]:

      @contextmanager
      def _temporary_repo(lowerCAmelCase  ):
            try:
                  yield repo_id
            finally:
                  cleanup_repo(__a  )

      return _temporary_repo




@pytest.fixture(scope="""session"""  )
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      Dict:
      UpperCAmelCase__       :				List[str]						 =  F"""repo_txt_data-{int(time.time() * 10E3  )}"""
      UpperCAmelCase__       :				Any						 =  F"""{CI_HUB_USER}/{repo_name}"""
      hf_api.create_repo(__a       ,     token=__a       ,     repo_type="""dataset"""       ,     private=__a  )
      hf_api.upload_file(
          token=__a       ,     path_or_fileobj=str(__a  )       ,     path_in_repo="""data/text_data.txt"""       ,     repo_id=__a       ,     repo_type="""dataset"""       ,     )
      yield repo_id
      try:
            hf_api.delete_repo(__a       ,     token=__a       ,     repo_type="""dataset"""  )
      except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
            pass




@pytest.fixture()
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      Optional[Any]:
      return hf_private_dataset_repo_txt_data_




@pytest.fixture(scope="""session"""  )
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      Any:
      UpperCAmelCase__       :				Union[str, Any]						 =  F"""repo_zipped_txt_data-{int(time.time() * 10E3  )}"""
      UpperCAmelCase__       :				Optional[int]						 =  F"""{CI_HUB_USER}/{repo_name}"""
      hf_api.create_repo(__a       ,     token=__a       ,     repo_type="""dataset"""       ,     private=__a  )
      hf_api.upload_file(
          token=__a       ,     path_or_fileobj=str(__a  )       ,     path_in_repo="""data.zip"""       ,     repo_id=__a       ,     repo_type="""dataset"""       ,     )
      yield repo_id
      try:
            hf_api.delete_repo(__a       ,     token=__a       ,     repo_type="""dataset"""  )
      except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
            pass




@pytest.fixture()
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      int:
      return hf_private_dataset_repo_zipped_txt_data_




@pytest.fixture(scope="""session"""  )
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      Union[str, Any]:
      UpperCAmelCase__       :				str						 =  F"""repo_zipped_img_data-{int(time.time() * 10E3  )}"""
      UpperCAmelCase__       :				Union[str, Any]						 =  F"""{CI_HUB_USER}/{repo_name}"""
      hf_api.create_repo(__a       ,     token=__a       ,     repo_type="""dataset"""       ,     private=__a  )
      hf_api.upload_file(
          token=__a       ,     path_or_fileobj=str(__a  )       ,     path_in_repo="""data.zip"""       ,     repo_id=__a       ,     repo_type="""dataset"""       ,     )
      yield repo_id
      try:
            hf_api.delete_repo(__a       ,     token=__a       ,     repo_type="""dataset"""  )
      except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
            pass





@pytest.fixture()
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      Tuple:
      return hf_private_dataset_repo_zipped_img_data_




import warnings

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Optional[int]	    =    "CLIPImageProcessor"
							UpperCAmelCase__       :						Tuple	    =    ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    int         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    int         =		kwargs.pop('''feature_extractor'''       )

												_a	:    int         =		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__(_a       ,							_a       )


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    List[Any]:

												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	:    str         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    List[Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[int]:
												return self.tokenizer.decode(*_a       ,							**_a       )




							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



