'''simple docstring'''

import time
from contextlib import contextmanager
from pathlib import Path

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


lowercase_     		= '''__DUMMY_TRANSFORMERS_USER__'''
lowercase_     		= '''Dummy User'''
lowercase_     		= '''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'''

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



@pytest.fixture
def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






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



@pytest.fixture
def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






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



@pytest.fixture
def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






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



@pytest.fixture
def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						HfFolder.save_token(a__      )
						yield
						HfFolder.delete_token()



@pytest.fixture(scope='session'      )
def 							UpperCamelCase__			(			):


						'''simple docstring'''






						return HfApi(endpoint=a__      )



@pytest.fixture(scope='session'      )
def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=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__      ):


						'''simple docstring'''






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

						return _cleanup_repo



@pytest.fixture
def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






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

						return _temporary_repo



@pytest.fixture(scope='session'      )
def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=F'''repo_txt_data-{int(time.time() * 10E3      )}'''
						_lowerCAmelCase										=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__			,      a__			,      a__      ):


						'''simple docstring'''






						return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope='session'      )
def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=F'''repo_zipped_txt_data-{int(time.time() * 10E3      )}'''
						_lowerCAmelCase										=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__			,      a__			,      a__      ):


						'''simple docstring'''






						return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope='session'      )
def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=F'''repo_zipped_img_data-{int(time.time() * 10E3      )}'''
						_lowerCAmelCase										=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__			,      a__			,      a__      ):


						'''simple docstring'''






						return hf_private_dataset_repo_zipped_img_data_






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							['image_processor', 'tokenizer']
		lowercase   :      Any       =							'CLIPImageProcessor'
		lowercase   :      int       =							('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(   self		,						__A=None		,						__A=None		,						**__A				) ->     str:
								_lowerCAmelCase										=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		,						)
														_lowerCAmelCase										=kwargs.pop('feature_extractor'				)

								_lowerCAmelCase										=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				) ->     Optional[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:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

								if images is not None:
														_lowerCAmelCase										=self.image_processor(__A		,						return_tensors=__A		,						**__A				)

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






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Any:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






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






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,						__A		,						)
								return self.image_processor_class






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,						__A		,						)
								return self.image_processor





