
"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__        =		{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCamelCase__        =		["""YolosFeatureExtractor"""]
				lowerCamelCase__        =		["""YolosImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCamelCase__        =		[
				    """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """YolosForObjectDetection""",
				    """YolosModel""",
				    """YolosPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_yolos import YolosFeatureExtractor
								from .image_processing_yolos import YolosImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_yolos import (
								    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    YolosForObjectDetection,
								    YolosModel,
								    YolosPreTrainedModel,
								)


else:
				import sys

				lowerCamelCase__        =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size'	,  [None, 400 * 2**20, 600 * 2**20]		)
@pytest.mark.parametrize('input_in_memory_max_size'	,  ['default', 0, 100 * 2**20, 900 * 2**20]		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	if input_in_memory_max_size != "default":
		monkeypatch.setattr(datasets.config	,  'IN_MEMORY_MAX_SIZE'	,  _UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				datasets.config.IN_MEMORY_MAX_SIZE
	if input_in_memory_max_size == "default":
		assert in_memory_max_size == 0
	else:
		assert in_memory_max_size == input_in_memory_max_size
	if dataset_size and in_memory_max_size:
		__lowerCAmelCase      :						Tuple						      =				dataset_size < in_memory_max_size
	else:
		__lowerCAmelCase      :						str						      =				False
	__lowerCAmelCase      :						Optional[int]						      =				is_small_dataset(_UpperCamelCase		)
	assert result == expected