






"""simple docstring"""



import os

import numpy
import onnx



def 						lowercase    (				__snake_case				: Tuple		,					__snake_case				: Union[str, Any]	):
		lowercase_ : str							       =   a.name
		lowercase_ : List[str]							       =   b.name

		lowercase_ : List[str]							       =   ''''''
		lowercase_ : int							       =   ''''''

		lowercase_ : Any							       =   a == b

		lowercase_ : Dict							       =   name_a
		lowercase_ : Union[str, Any]							       =   name_b

		return res



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: Any		,					__snake_case				: List[Any]	):
		for i, input_name in enumerate(node_proto.input	):
				if input_name == name:
						node_proto.input.insert(__snake_case		,					__snake_case	)
						node_proto.input.pop(i + 1	)

		if node_proto.op_type == "If":
				_graph_replace_input_with(node_proto.attribute[0].g		,					__snake_case		,					__snake_case	)
				_graph_replace_input_with(node_proto.attribute[1].g		,					__snake_case		,					__snake_case	)
		if node_proto.op_type == "Loop":
				_graph_replace_input_with(node_proto.attribute[0].g		,					__snake_case		,					__snake_case	)



def 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: Dict		,					__snake_case				: Optional[int]	):
		for n in graph_proto.node:
				_node_replace_input_with(__snake_case		,					__snake_case		,					__snake_case	)



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Optional[Any]		,					__snake_case				: Optional[Any]	):
		lowercase_ : Any							       =   list(model.graph.initializer	)
		lowercase_ : List[str]							       =   list(model_without_ext.graph.initializer	)
		for i, ref_i in ind_to_replace:
				assert inits_with_data[i].name == inits[i].name
				assert inits_with_data[ref_i].name == inits[ref_i].name
				assert i > ref_i

				lowercase_ : str							       =   inits[i].name
				lowercase_ : List[Any]							       =   inits[ref_i].name

				model_without_ext.graph.initializer.remove(inits[i]	)

				# for n in model.graph.node:
				_graph_replace_input_with(model_without_ext.graph		,					__snake_case		,					__snake_case	)






def 						lowercase    (				__snake_case				: Union[str, Any]	):
		lowercase_ : str							       =   os.path.dirname(__snake_case	)
		lowercase_ : Optional[int]							       =   os.path.basename(__snake_case	)

		lowercase_ : Optional[Any]							       =   onnx.load(os.path.join(__snake_case		,					__snake_case	)	)

		lowercase_ : Any							       =   list(model.graph.initializer	)

		lowercase_ : List[Any]							       =   set()
		lowercase_ : List[Any]							       =   {}
		lowercase_ : List[str]							       =   []

		lowercase_ : Optional[int]							       =   0

		for i in range(len(__snake_case	)	):
				if i in dup_set:
						continue

				for j in range(i + 1		,					len(__snake_case	)	):
						if j in dup_set:
								continue
						if _is_equal_tensor_proto(inits[i]		,					inits[j]	):
								dup_set.add(__snake_case	)
								dup_set.add(__snake_case	)

								lowercase_ : Dict							       =   inits[j].data_type
								lowercase_ : Dict							       =   numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('''unexpected data type: '''		,					__snake_case	)
								total_reduced_size += mem_size

								lowercase_ : List[Any]							       =   inits[i].name
								lowercase_ : Dict							       =   inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(__snake_case	)
								else:
										lowercase_ : Optional[int]							       =   [name_j]
								ind_to_replace.append((j, i)	)

		print('''total reduced size: '''		,					total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4		,					'''GB'''	)

		lowercase_ : str							       =   sorted(__snake_case	)
		_remove_dup_initializers_from_model(__snake_case		,					__snake_case		,					__snake_case	)

		lowercase_ : Dict							       =   '''optimized_''' + model_file_name
		lowercase_ : List[Any]							       =   os.path.join(__snake_case		,					__snake_case	)
		onnx.save(__snake_case		,					__snake_case	)

		return new_model











"""simple docstring"""



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


__A		:       Dict  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		{
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   "vit_mae"




					def __init__(				self     :						Dict    ,						A     :						List[str]=7_68    ,						A     :						Any=12    ,						A     :						Union[str, Any]=12    ,						A     :						Tuple=30_72    ,						A     :						Any="gelu"    ,						A     :						Tuple=0.0    ,						A     :						List[str]=0.0    ,						A     :						Tuple=0.02    ,						A     :						Tuple=1e-12    ,						A     :						int=2_24    ,						A     :						Dict=16    ,						A     :						int=3    ,						A     :						Tuple=True    ,						A     :						Tuple=16    ,						A     :						Optional[Any]=5_12    ,						A     :						Union[str, Any]=8    ,						A     :						List[Any]=20_48    ,						A     :						Dict=0.75    ,						A     :						Any=False    ,						**A     :						Optional[int]    ,						)  ->  Union[str, Any]:
							super().__init__(**A   )

							lowercase_ : List[Any]							       =   hidden_size
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : List[Any]							       =   num_attention_heads
							lowercase_ : Any							       =   intermediate_size
							lowercase_ : Optional[int]							       =   hidden_act
							lowercase_ : List[Any]							       =   hidden_dropout_prob
							lowercase_ : int							       =   attention_probs_dropout_prob
							lowercase_ : int							       =   initializer_range
							lowercase_ : Dict							       =   layer_norm_eps
							lowercase_ : Optional[Any]							       =   image_size
							lowercase_ : str							       =   patch_size
							lowercase_ : Dict							       =   num_channels
							lowercase_ : Any							       =   qkv_bias
							lowercase_ : Union[str, Any]							       =   decoder_num_attention_heads
							lowercase_ : Optional[Any]							       =   decoder_hidden_size
							lowercase_ : List[str]							       =   decoder_num_hidden_layers
							lowercase_ : List[Any]							       =   decoder_intermediate_size
							lowercase_ : Optional[Any]							       =   mask_ratio
							lowercase_ : Optional[Any]							       =   norm_pix_loss



