




print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))







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


_lowercase: str      			=		logging.get_logger(__name__)

_lowercase: 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 	_lowercase							(   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		,       lowerCamelCase_=50265		,       lowerCamelCase_=1024		,       lowerCamelCase_=12		,       lowerCamelCase_=16		,       lowerCamelCase_=4096		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=512		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=0.0		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       lowerCamelCase_=2		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						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=lowerCamelCase_		,       bos_token_id=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		,       decoder_start_token_id=lowerCamelCase_		,       **lowerCamelCase_		,       )
