


# 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


snake_case_						:			Union[str, Any]													=							{
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_						:			Optional[int]													=							[
           '''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

       snake_case_						:			Optional[Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



from collections.abc import Generator







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =0, 1
				while True:
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =b, a + b
								yield b







def 						lowerCamelCase(   a__ = 1000):
				_SCREAMING_SNAKE_CASE			  =1
				_SCREAMING_SNAKE_CASE			  =fibonacci_generator()
				while len(str(next(a__))) < n:
								answer += 1
				return answer + 1


if __name__ == "__main__":
							print(solution(int(str(input()).strip())))