


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

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


__SCREAMING_SNAKE_CASE							:      int						=      '__DUMMY_TRANSFORMERS_USER__'
__SCREAMING_SNAKE_CASE							:      List[Any]						=      'Dummy User'
__SCREAMING_SNAKE_CASE							:      List[str]						=      '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'

__SCREAMING_SNAKE_CASE							:      Dict						=      'https://hub-ci.huggingface.co'
__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
__SCREAMING_SNAKE_CASE							:      List[str]						=      CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      Path('~/.huggingface/hub_ci_token').expanduser()







@pytest.fixture
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							monkeypatch.setattr(
							    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""			,	UpperCamelCase__			)







@pytest.fixture
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""			,	UpperCamelCase__			)
							monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""			,	UpperCamelCase__			)







@pytest.fixture
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			List[str]:
							monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""			,	UpperCamelCase__			)







@pytest.fixture
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							HfFolder.save_token(UpperCamelCase__			)
							yield
							HfFolder.delete_token()







@pytest.fixture(scope="""session"""			)
def   _a			( )      ->			Optional[int]:
							return HfApi(endpoint=UpperCamelCase__			)







@pytest.fixture(scope="""session"""			)
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_          =						HfFolder.get_token()
							HfFolder.save_token(UpperCamelCase__			)
							yield CI_HUB_USER_TOKEN
							if previous_token is not None:
														HfFolder.save_token(UpperCamelCase__			)







@pytest.fixture
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			List[str]:
							def _cleanup_repo(_SCREAMING_SNAKE_CASE			):
														hf_api.delete_repo(UpperCamelCase__			,	token=UpperCamelCase__			,	repo_type="""dataset"""			)

							return _cleanup_repo







@pytest.fixture
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							@contextmanager
							def _temporary_repo(_SCREAMING_SNAKE_CASE			):
														try:
																					yield repo_id
														finally:
																					cleanup_repo(UpperCamelCase__			)

							return _temporary_repo







@pytest.fixture(scope="""session"""			)
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							snake_case_          =						f"""repo_txt_data-{int(time.time() * 1_0E3			)}"""
							snake_case_          =						f"""{CI_HUB_USER}/{repo_name}"""
							hf_api.create_repo(UpperCamelCase__			,	token=UpperCamelCase__			,	repo_type="""dataset"""			,	private=UpperCamelCase__			)
							hf_api.upload_file(
							    token=UpperCamelCase__			,	path_or_fileobj=str(UpperCamelCase__			)			,	path_in_repo="""data/text_data.txt"""			,	repo_id=UpperCamelCase__			,	repo_type="""dataset"""			,	)
							yield repo_id
							try:
														hf_api.delete_repo(UpperCamelCase__			,	token=UpperCamelCase__			,	repo_type="""dataset"""			)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass







@pytest.fixture()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							return hf_private_dataset_repo_txt_data_







@pytest.fixture(scope="""session"""			)
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						f"""repo_zipped_txt_data-{int(time.time() * 1_0E3			)}"""
							snake_case_          =						f"""{CI_HUB_USER}/{repo_name}"""
							hf_api.create_repo(UpperCamelCase__			,	token=UpperCamelCase__			,	repo_type="""dataset"""			,	private=UpperCamelCase__			)
							hf_api.upload_file(
							    token=UpperCamelCase__			,	path_or_fileobj=str(UpperCamelCase__			)			,	path_in_repo="""data.zip"""			,	repo_id=UpperCamelCase__			,	repo_type="""dataset"""			,	)
							yield repo_id
							try:
														hf_api.delete_repo(UpperCamelCase__			,	token=UpperCamelCase__			,	repo_type="""dataset"""			)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass







@pytest.fixture()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							return hf_private_dataset_repo_zipped_txt_data_







@pytest.fixture(scope="""session"""			)
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_          =						f"""repo_zipped_img_data-{int(time.time() * 1_0E3			)}"""
							snake_case_          =						f"""{CI_HUB_USER}/{repo_name}"""
							hf_api.create_repo(UpperCamelCase__			,	token=UpperCamelCase__			,	repo_type="""dataset"""			,	private=UpperCamelCase__			)
							hf_api.upload_file(
							    token=UpperCamelCase__			,	path_or_fileobj=str(UpperCamelCase__			)			,	path_in_repo="""data.zip"""			,	repo_id=UpperCamelCase__			,	repo_type="""dataset"""			,	)
							yield repo_id
							try:
														hf_api.delete_repo(UpperCamelCase__			,	token=UpperCamelCase__			,	repo_type="""dataset"""			)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass







@pytest.fixture()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Tuple:
							return hf_private_dataset_repo_zipped_img_data_







"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Any							 =      ["""image_processor""", """tokenizer"""]
							__lowercase:						Dict							 =      """AutoImageProcessor"""
							__lowercase:						List[Any]							 =      """AutoTokenizer"""



							def __init__(			self							: Tuple						,    UpperCAmelCase_							: int=None						,    UpperCAmelCase_							: Tuple=None						,    **UpperCAmelCase_							: str    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						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_						,    )
																					snake_case_          =						kwargs.pop("""feature_extractor"""    )

														snake_case_          =						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_    )
														snake_case_          =						self.image_processor
														snake_case_          =						False



							def __call__(			self							: Any						,    *UpperCAmelCase_							: Optional[int]						,    **UpperCAmelCase_							: str    )   ->List[str]:



														"""simple docstring"""



														if self._in_target_context_manager:
																					return self.current_processor(*UpperCAmelCase_						,    **UpperCAmelCase_    )

														snake_case_          =						kwargs.pop("""images"""						,    UpperCAmelCase_    )
														snake_case_          =						kwargs.pop("""text"""						,    UpperCAmelCase_    )
														if len(UpperCAmelCase_    ) > 0:
																					snake_case_          =						args[0]
																					snake_case_          =						args[1:]

														if images is None and text is None:
																					raise ValueError("""You need to specify either an `images` or `text` input to process."""    )

														if images is not None:
																					snake_case_          =						self.image_processor(UpperCAmelCase_						,    *UpperCAmelCase_						,    **UpperCAmelCase_    )
														if text is not None:
																					snake_case_          =						self.tokenizer(UpperCAmelCase_						,    **UpperCAmelCase_    )

														if text is None:
																					return inputs
														elif images is None:
																					return encodings
														else:
																					snake_case_          =						encodings["""input_ids"""]
																					return inputs



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    *UpperCAmelCase_							: Optional[Any]						,    **UpperCAmelCase_							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]						,    *UpperCAmelCase_							: Union[str, Any]						,    **UpperCAmelCase_							: List[Any]    )   ->str:



														"""simple docstring"""



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



							@contextmanager
							def 						lowerCAmelCase    (			self							: List[str]    )   ->Dict:



														"""simple docstring"""



														warnings.warn(
														    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
														    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
														    """your images inputs, or in a separate call."""    )
														snake_case_          =						True
														snake_case_          =						self.tokenizer
														yield
														snake_case_          =						self.image_processor
														snake_case_          =						False



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: str=False						,    UpperCAmelCase_							: str=None    )   ->Tuple:



														"""simple docstring"""



														if added_vocab is None:
																					snake_case_          =						self.tokenizer.get_added_vocab()

														snake_case_          =						{}

														while tokens:
																					snake_case_          =						re.search(R"""<s_(.*?)>"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																					if start_token is None:
																												break
																					snake_case_          =						start_token.group(1    )
																					snake_case_          =						re.search(RF"""</s_{key}>"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																					snake_case_          =						start_token.group()
																					if end_token is None:
																												snake_case_          =						tokens.replace(UpperCAmelCase_						,    """"""    )
																					else:
																												snake_case_          =						end_token.group()
																												snake_case_          =						re.escape(UpperCAmelCase_    )
																												snake_case_          =						re.escape(UpperCAmelCase_    )
																												snake_case_          =						re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																												if content is not None:
																																			snake_case_          =						content.group(1    ).strip()
																																			if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																										snake_case_          =						self.tokenajson(UpperCAmelCase_						,    is_inner_value=UpperCAmelCase_						,    added_vocab=UpperCAmelCase_    )
																																										if value:
																																																	if len(UpperCAmelCase_    ) == 1:
																																																								snake_case_          =						value[0]
																																																	snake_case_          =						value
																																			else:  # leaf nodes
																																										snake_case_          =						[]
																																										for leaf in content.split(R"""<sep/>"""    ):
																																																	snake_case_          =						leaf.strip()
																																																	if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																								snake_case_          =						leaf[1:-2]  # for categorical special tokens
																																																	output[key].append(UpperCAmelCase_    )
																																										if len(output[key]    ) == 1:
																																																	snake_case_          =						output[key][0]

																												snake_case_          =						tokens[tokens.find(UpperCAmelCase_    ) + len(UpperCAmelCase_    ) :].strip()
																												if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																			return [output] + self.tokenajson(tokens[6:]						,    is_inner_value=UpperCAmelCase_						,    added_vocab=UpperCAmelCase_    )

														if len(UpperCAmelCase_    ):
																					return [output] if is_inner_value else output
														else:
																					return [] if is_inner_value else {"text_sequence": tokens}



							@property
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->Optional[int]:



														"""simple docstring"""



														warnings.warn(
														    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,    UpperCAmelCase_						,    )
														return self.image_processor_class



							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->str:



														"""simple docstring"""



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



