



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__		:					Optional[int]							={
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[Any]							=['''ConvNextFeatureExtractor''']
			lowerCAmelCase__		:					Optional[int]							=['''ConvNextImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[Any]							=[
			    '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ConvNextForImageClassification''',
			    '''ConvNextModel''',
			    '''ConvNextPreTrainedModel''',
			    '''ConvNextBackbone''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[Any]							=[
			    '''TFConvNextForImageClassification''',
			    '''TFConvNextModel''',
			    '''TFConvNextPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_convnext import ConvNextFeatureExtractor
						from .image_processing_convnext import ConvNextImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnext import (
						    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextBackbone,
						    ConvNextForImageClassification,
						    ConvNextModel,
						    ConvNextPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
			import sys

			lowerCAmelCase__		:					List[Any]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure)











import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def      __lowercase       (			a__       )    ->			List[Any]:  # picklable for multiprocessing
							return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def      __lowercase       (			)    ->			Any:
							with parallel_backend('spark'       ):
														assert ParallelBackendConfig.backend_name == "spark"

							__SCREAMING_SNAKE_CASE									= [1, 2, 3]
							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=2       )

							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=-1       )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'							,       [2, -1]       )
def      __lowercase       (			a__       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= [1, 2]
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': [1, 2], 'b': [3, 4]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 1}, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2, 'c': 3, 'd': 4}
							__SCREAMING_SNAKE_CASE									= [2, 3]
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': [2, 3], 'b': [4, 5]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 2}, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3, 'c': 4, 'd': 5}

							with parallel_backend('spark'       ):
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa






