


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


_snake_case			 =	logging.get_logger(__name__)

_snake_case			 =	{
    '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       UpperCAmelCase_   (   __snake_case       ):

   '''simple docstring'''




   __A					: int							=						"trocr"
   __A					: List[Any]							=						["past_key_values"]
   __A					: List[str]							=						{
       "num_attention_heads": "decoder_attention_heads",
       "hidden_size": "d_model",
       "num_hidden_layers": "decoder_layers",
   }






   def __init__(						self		,							__A=5_0265		,							__A=1024		,							__A=12		,							__A=16		,							__A=4096		,							__A="gelu"		,							__A=512		,							__A=0.1		,							__A=0.0		,							__A=0.0		,							__A=2		,							__A=0.02		,							__A=0.0		,							__A=True		,							__A=False		,							__A=True		,							__A=True		,							__A=1		,							__A=0		,							__A=2		,							**__A		,							):






     """simple docstring"""





     lowerCamelCase					:   Optional[int]       =							vocab_size
     lowerCamelCase					:   Tuple       =							d_model
     lowerCamelCase					:   Optional[int]       =							decoder_layers
     lowerCamelCase					:   Dict       =							decoder_attention_heads
     lowerCamelCase					:   int       =							decoder_ffn_dim
     lowerCamelCase					:   List[str]       =							activation_function
     lowerCamelCase					:   str       =							max_position_embeddings
     lowerCamelCase					:   str       =							dropout
     lowerCamelCase					:   Optional[int]       =							attention_dropout
     lowerCamelCase					:   Optional[int]       =							activation_dropout
     lowerCamelCase					:   Tuple       =							init_std
     lowerCamelCase					:   Tuple       =							decoder_layerdrop
     lowerCamelCase					:   Tuple       =							use_cache
     lowerCamelCase					:   Tuple       =							scale_embedding
     lowerCamelCase					:   Tuple       =							use_learned_position_embeddings
     lowerCamelCase					:   Any       =							layernorm_embedding

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






def 	__a     (      A__			:  int = 1000       ):
    SCREAMING_SNAKE_CASE										=   3
    SCREAMING_SNAKE_CASE										=   0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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