




'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a__		     =     {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     ['''EfficientNetImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     [
	    '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''EfficientNetForImageClassification''',
	    '''EfficientNetModel''',
	    '''EfficientNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_efficientnet import (
	    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    EfficientNetConfig,
	    EfficientNetOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientnet import EfficientNetImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientnet import (
		    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientNetForImageClassification,
		    EfficientNetModel,
		    EfficientNetPreTrainedModel,
		)

else:
	import sys

	a__		     =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






