





'''simple docstring'''

def lowerCamelCase						(     __lowerCamelCase :    int = 1000    )						->int:
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			1, 1
    _SCREAMING_SNAKE_CASE									=			[]
    for i in range(1					,       n + 1    ):
        _SCREAMING_SNAKE_CASE									=			prev_numerator + 2 * prev_denominator
        _SCREAMING_SNAKE_CASE									=			prev_numerator + prev_denominator
        if len(str(__lowerCamelCase    )    ) > len(str(__lowerCamelCase    )    ):
            result.append(__lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			numerator
        _SCREAMING_SNAKE_CASE									=			denominator

    return len(__lowerCamelCase    )


if __name__ == "__main__":
      print(f"""{solution() = }""")










'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    List[str]      	=			{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='trocr'
				__a            =['past_key_values']
				__a            ={
				    'num_attention_heads': 'decoder_attention_heads',
				    'hidden_size': 'd_model',
				    'num_hidden_layers': 'decoder_layers',
				}




				def __init__(  self     :					Optional[int]					,				__a     :					Any=5_02_65					,				__a     :					Optional[int]=10_24					,				__a     :					List[Any]=12					,				__a     :					str=16					,				__a     :					int=40_96					,				__a     :					Optional[Any]="gelu"					,				__a     :					Union[str, Any]=5_12					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.0					,				__a     :					Union[str, Any]=0.0					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					Any=0.0					,				__a     :					List[str]=True					,				__a     :					Optional[Any]=False					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Any=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				**__a     :					Optional[Any]					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= activation_function
								_a								= max_position_embeddings
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= init_std
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= scale_embedding
								_a								= use_learned_position_embeddings
								_a								= layernorm_embedding

								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				decoder_start_token_id=__a					,				**__a					,				)
