

# 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_			:					str    			=     {
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					List[Any]    			=     [
	    '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_			:					Union[str, Any]    			=     _LazyModule(__name__, globals()['__file__'], _import_structure)






def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



