

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    __UpperCAmelCase (	__a      :			Dict	)			->     List[str]:

					"""simple docstring"""

					monkeypatch.setattr(
					    '''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'''		,__a	)



@pytest.fixture
def    __UpperCAmelCase (	__a      :			Any	)			->     Any:

					"""simple docstring"""

					monkeypatch.setattr('''datasets.config.HF_ENDPOINT'''		,__a	)
					monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL'''		,__a	)



@pytest.fixture
def    __UpperCAmelCase (	__a      :			List[Any]	)			->     Any:

					"""simple docstring"""

					monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token'''		,__a	)



@pytest.fixture
def    __UpperCAmelCase (	__a      :			int		,__a      :			List[str]	)			->     Dict:

					"""simple docstring"""

					HfFolder.save_token(__a	)
					yield
					HfFolder.delete_token()



@pytest.fixture(scope='''session'''	)
def    __UpperCAmelCase (	)			->     int:

					"""simple docstring"""

					return HfApi(endpoint=__a	)



@pytest.fixture(scope='''session'''	)
def    __UpperCAmelCase (	__a      :			HfApi	)			->     Any:

					"""simple docstring"""

					_a	:    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    __UpperCAmelCase (	__a      :			int	)			->     Dict:

					"""simple docstring"""

					def _cleanup_repo(__a      :			str	):
										hf_api.delete_repo(__a		,token=__a		,repo_type='''dataset'''	)

					return _cleanup_repo



@pytest.fixture
def    __UpperCAmelCase (	__a      :			Optional[Any]	)			->     Optional[int]:

					"""simple docstring"""

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

					return _temporary_repo



@pytest.fixture(scope='''session'''	)
def    __UpperCAmelCase (	__a      :			HfApi		,__a      :			Tuple		,__a      :			Dict	)			->     Dict:

					"""simple docstring"""

					_a	:    List[str]         =		F"""repo_txt_data-{int(time.time() * 10E3	)}"""
					_a	:    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    __UpperCAmelCase (	__a      :			Dict		,__a      :			int		,__a      :			str	)			->     Optional[Any]:

					"""simple docstring"""

					return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope='''session'''	)
def    __UpperCAmelCase (	__a      :			HfApi		,__a      :			Optional[Any]		,__a      :			Union[str, Any]	)			->     Any:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		F"""repo_zipped_txt_data-{int(time.time() * 10E3	)}"""
					_a	:    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    __UpperCAmelCase (	__a      :			Tuple		,__a      :			Dict		,__a      :			Tuple	)			->     int:

					"""simple docstring"""

					return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope='''session'''	)
def    __UpperCAmelCase (	__a      :			HfApi		,__a      :			Any		,__a      :			Dict	)			->     Union[str, Any]:

					"""simple docstring"""

					_a	:    str         =		F"""repo_zipped_img_data-{int(time.time() * 10E3	)}"""
					_a	:    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    __UpperCAmelCase (	__a      :			int		,__a      :			Optional[Any]		,__a      :			Optional[Any]	)			->     Tuple:

					"""simple docstring"""

					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__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[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.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		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       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												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	:    List[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	:    Union[str, 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       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


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




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



