


import os

import numpy
import onnx




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Tuple   )   ->			List[Any]:
    UpperCamelCase__							:       str      =   a.name
    UpperCamelCase__							:       str      =   b.name

    UpperCamelCase__							:       Tuple      =   ''''''
    UpperCamelCase__							:       Optional[Any]      =   ''''''

    UpperCamelCase__							:       str      =   a == b

    UpperCamelCase__							:       int      =   name_a
    UpperCamelCase__							:       Any      =   name_b

    return res




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[Any]   )   ->			Tuple:
    for i, input_name in enumerate(node_proto.input   ):
        if input_name == name:
            node_proto.input.insert(__UpperCAmelCase	,     __UpperCAmelCase   )
            node_proto.input.pop(i + 1   )

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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    Dict   )   ->			Tuple:
    for n in graph_proto.node:
        _node_replace_input_with(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Tuple   )   ->			int:
    UpperCamelCase__							:       Union[str, Any]      =   list(model.graph.initializer   )
    UpperCamelCase__							:       Union[str, Any]      =   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

        UpperCamelCase__							:       List[Any]      =   inits[i].name
        UpperCamelCase__							:       Optional[int]      =   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	,     __UpperCAmelCase	,     __UpperCAmelCase   )





def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any   )   ->			List[str]:
    UpperCamelCase__							:       Any      =   os.path.dirname(__UpperCAmelCase   )
    UpperCamelCase__							:       int      =   os.path.basename(__UpperCAmelCase   )

    UpperCamelCase__							:       Tuple      =   onnx.load(os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )   )

    UpperCamelCase__							:       int      =   list(model.graph.initializer   )

    UpperCamelCase__							:       Union[str, Any]      =   set()
    UpperCamelCase__							:       Optional[int]      =   {}
    UpperCamelCase__							:       Any      =   []

    UpperCamelCase__							:       Tuple      =   0

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

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

                UpperCamelCase__							:       Optional[Any]      =   inits[j].data_type
                UpperCamelCase__							:       List[str]      =   numpy.prod(inits[j].dims   )
                if dtype == 1:
                    mem_size *= 4
                elif dtype == 6:
                    mem_size *= 4
                elif dtype == 7 or dtype == 11:
                    mem_size *= 8
                else:
                    print('''unexpected data type: '''	,     __UpperCAmelCase   )
                total_reduced_size += mem_size

                UpperCamelCase__							:       int      =   inits[i].name
                UpperCamelCase__							:       int      =   inits[j].name

                if name_i in dup_map:
                    dup_map[name_i].append(__UpperCAmelCase   )
                else:
                    UpperCamelCase__							:       Any      =   [name_j]
                ind_to_replace.append((j, i)   )

    print('''total reduced size: '''	,     total_reduced_size / 1024 / 1024 / 1024	,     '''GB'''   )

    UpperCamelCase__							:       str      =   sorted(__UpperCAmelCase   )
    _remove_dup_initializers_from_model(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    UpperCamelCase__							:       List[Any]      =   '''optimized_''' + model_file_name
    UpperCamelCase__							:       Tuple      =   os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )
    onnx.save(__UpperCAmelCase	,     __UpperCAmelCase   )

    return new_model








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Any     	=	"upernet"




   def __init__(					self,	__magic_name__=None,	__magic_name__=512,	__magic_name__=0.02,	__magic_name__=[1, 2, 3, 6],	__magic_name__=True,	__magic_name__=0.4,	__magic_name__=384,	__magic_name__=256,	__magic_name__=1,	__magic_name__=False,	__magic_name__=255,	**__magic_name__,	)				->   Dict:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
           UpperCamelCase__							:       str      =   CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
       elif isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Tuple      =   backbone_config.get('''model_type'''							)
           UpperCamelCase__							:       List[str]      =   CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__							:       Optional[int]      =   config_class.from_dict(__magic_name__							)

       UpperCamelCase__							:       Dict      =   backbone_config
       UpperCamelCase__							:       int      =   hidden_size
       UpperCamelCase__							:       int      =   initializer_range
       UpperCamelCase__							:       Dict      =   pool_scales
       UpperCamelCase__							:       Tuple      =   use_auxiliary_head
       UpperCamelCase__							:       Tuple      =   auxiliary_loss_weight
       UpperCamelCase__							:       Optional[int]      =   auxiliary_in_channels
       UpperCamelCase__							:       Optional[Any]      =   auxiliary_channels
       UpperCamelCase__							:       Any      =   auxiliary_num_convs
       UpperCamelCase__							:       Union[str, Any]      =   auxiliary_concat_input
       UpperCamelCase__							:       List[str]      =   loss_ignore_index




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   copy.deepcopy(self.__dict__							)
       UpperCamelCase__							:       int      =   self.backbone_config.to_dict()
       UpperCamelCase__							:       Any      =   self.__class__.model_type
       return output

