


from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			:			List[Any]        =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:			Tuple        =     {
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}





class        snake_case__	(			SCREAMING_SNAKE_CASE_							):
	UpperCAmelCase :				Tuple       =			"""donut-swin"""

	UpperCAmelCase :				str       =			{
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}





	def __init__(	self					,     UpperCamelCase_=224					,     UpperCamelCase_=4					,     UpperCamelCase_=3					,     UpperCamelCase_=96					,     UpperCamelCase_=[2, 2, 6, 2]					,     UpperCamelCase_=[3, 6, 12, 24]					,     UpperCamelCase_=7					,     UpperCamelCase_=4.0					,     UpperCamelCase_=True					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.1					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=False					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     **UpperCamelCase_					,     )		->      Optional[int]:
								"""simple docstring"""






								super().__init__(**__SCREAMING_SNAKE_CASE				)

								a_							: str								=						image_size
								a_							: Optional[Any]								=						patch_size
								a_							: List[str]								=						num_channels
								a_							: Any								=						embed_dim
								a_							: Optional[int]								=						depths
								a_							: Any								=						len(__SCREAMING_SNAKE_CASE				)
								a_							: Tuple								=						num_heads
								a_							: Optional[Any]								=						window_size
								a_							: Union[str, Any]								=						mlp_ratio
								a_							: int								=						qkv_bias
								a_							: Optional[int]								=						hidden_dropout_prob
								a_							: Dict								=						attention_probs_dropout_prob
								a_							: Union[str, Any]								=						drop_path_rate
								a_							: Tuple								=						hidden_act
								a_							: List[Any]								=						use_absolute_embeddings
								a_							: Optional[int]								=						layer_norm_eps
								a_							: Optional[int]								=						initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								a_							: List[str]								=						int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE				) - 1)				)










'''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   )




