
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     '''dpt'''

					def __init__(						self       :  Union[str, Any] ,       __UpperCamelCase       :  str=7_6_8 ,       __UpperCamelCase       :  Any=1_2 ,       __UpperCamelCase       :  Optional[Any]=1_2 ,       __UpperCamelCase       :  Union[str, Any]=3_0_7_2 ,       __UpperCamelCase       :  Tuple="gelu" ,       __UpperCamelCase       :  Dict=0.0 ,       __UpperCamelCase       :  List[str]=0.0 ,       __UpperCamelCase       :  Any=0.02 ,       __UpperCamelCase       :  List[str]=1E-12 ,       __UpperCamelCase       :  Tuple=3_8_4 ,       __UpperCamelCase       :  str=1_6 ,       __UpperCamelCase       :  int=3 ,       __UpperCamelCase       :  Dict=False ,       __UpperCamelCase       :  Any=True ,       __UpperCamelCase       :  str=[2, 5, 8, 1_1] ,       __UpperCamelCase       :  Union[str, Any]="project" ,       __UpperCamelCase       :  List[str]=[4, 2, 1, 0.5] ,       __UpperCamelCase       :  Optional[Any]=[9_6, 1_9_2, 3_8_4, 7_6_8] ,       __UpperCamelCase       :  Dict=2_5_6 ,       __UpperCamelCase       :  List[str]=-1 ,       __UpperCamelCase       :  Dict=False ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  Optional[int]=0.4 ,       __UpperCamelCase       :  int=2_5_5 ,       __UpperCamelCase       :  Dict=0.1 ,       __UpperCamelCase       :  Any=[1, 1_0_2_4, 2_4, 2_4] ,       __UpperCamelCase       :  Optional[Any]=[0, 1] ,       __UpperCamelCase       :  Optional[Any]=None ,       **__UpperCamelCase       :  Optional[int] ,       ):
												super().__init__(**__UpperCamelCase )

												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       is_hybrid

												if self.is_hybrid:
																			if backbone_config is None:
																										logger.info("""Initializing the config with a `BiT` backbone.""" )
																										lowerCamelCase_					=       {
																										    """global_padding""": """same""",
																										    """layer_type""": """bottleneck""",
																										    """depths""": [3, 4, 9],
																										    """out_features""": ["""stage1""", """stage2""", """stage3"""],
																										    """embedding_dynamic_padding""": True,
																										}
																										lowerCamelCase_					=       BitConfig(**__UpperCamelCase )
																			elif isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																										logger.info("""Initializing the config with a `BiT` backbone.""" )
																										lowerCamelCase_					=       BitConfig(**__UpperCamelCase )
																			elif isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																										lowerCamelCase_					=       backbone_config
																			else:
																										raise ValueError(
																										    F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )

																			lowerCamelCase_					=       backbone_featmap_shape
																			lowerCamelCase_					=       neck_ignore_stages

																			if readout_type != "project":
																										raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""" )
												else:
																			lowerCamelCase_					=       None
																			lowerCamelCase_					=       None
																			lowerCamelCase_					=       []

												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       layer_norm_eps
												lowerCamelCase_					=       image_size
												lowerCamelCase_					=       patch_size
												lowerCamelCase_					=       num_channels
												lowerCamelCase_					=       qkv_bias
												lowerCamelCase_					=       backbone_out_indices
												if readout_type not in ["ignore", "add", "project"]:
																			raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""" )
												lowerCamelCase_					=       readout_type
												lowerCamelCase_					=       reassemble_factors
												lowerCamelCase_					=       neck_hidden_sizes
												lowerCamelCase_					=       fusion_hidden_size
												lowerCamelCase_					=       head_in_index
												lowerCamelCase_					=       use_batch_norm_in_fusion_residual
												# auxiliary head attributes (semantic segmentation)
												lowerCamelCase_					=       use_auxiliary_head
												lowerCamelCase_					=       auxiliary_loss_weight
												lowerCamelCase_					=       semantic_loss_ignore_index
												lowerCamelCase_					=       semantic_classifier_dropout



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       copy.deepcopy(self.__dict__ )

												if output["backbone_config"] is not None:
																			lowerCamelCase_					=       self.backbone_config.to_dict()

												lowerCamelCase_					=       self.__class__.model_type
												return output







import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   str      ,					**UpperCAmelCase__					:   List[str]							)	->     Tuple:
							lowerCamelCase_					=       AutoConfig.from_pretrained(UpperCAmelCase__      ,					**UpperCAmelCase__							)
							lowerCamelCase_					=       AutoModelForSeqaSeqLM.from_config(UpperCAmelCase__							)
							model.save_pretrained(UpperCAmelCase__							)
							AutoTokenizer.from_pretrained(UpperCAmelCase__							).save_pretrained(UpperCAmelCase__							)
							return model


if __name__ == "__main__":
		fire.Fire(save_randomly_initialized_version)





