






'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					["image_processor", "tokenizer"]
			snake_case__	=					"AutoImageProcessor"
			snake_case__	=					"AutoTokenizer"






			def __init__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Dict       )      ->      Optional[int]:
							super().__init__(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   self.image_processor






			def __call__(			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=None	,    __SCREAMING_SNAKE_CASE   :      int=None	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=None	,    **__SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:

							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_		:	Dict									=   self.tokenizer(__SCREAMING_SNAKE_CASE	,    return_tensors=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )

							if images is not None:
											a_		:	Dict									=   self.image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    *__SCREAMING_SNAKE_CASE   :      Tuple	,    **__SCREAMING_SNAKE_CASE   :      List[str]       )      ->      Any:
							return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    *__SCREAMING_SNAKE_CASE   :      Dict	,    **__SCREAMING_SNAKE_CASE   :      str       )      ->      Dict:
							return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )







			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Optional[int]:
							return ["input_ids", "attention_mask", "pixel_values"]












'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	Optional[Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	Union[str, Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	List[Any]									=   0.01
				with locka.acquire():
								with pytest.raises(__A   ):
												a_		:	List[Any]									=   time.time()
												locka.acquire(__A   )
												assert time.time() - _start > timeout





def 		_UpperCAmelCase (				__A				: Tuple   ):
				a_		:	Tuple									=   '''a''' * 10_00 + '''.lock'''
				a_		:	List[Any]									=   FileLock(str(tmpdir / filename   )   )
				assert locka._lock_file.endswith('''.lock'''   )
				assert not locka._lock_file.endswith(__A   )
				assert len(os.path.basename(locka._lock_file   )   ) <= 2_55
				a_		:	Optional[int]									=   FileLock(tmpdir / filename   )
				with locka.acquire():
								with pytest.raises(__A   ):
												locka.acquire(0   )




