




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase__ :   Any						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Union[str, Any]						=     "detr"
				__a     :       Dict						=     ["past_key_values"]
				__a     :       Optional[int]						=     {
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}






				def __init__(			self      ,snake_case__=True      ,snake_case__=None      ,snake_case__=3      ,snake_case__=100      ,snake_case__=6      ,snake_case__=2048      ,snake_case__=8      ,snake_case__=6      ,snake_case__=2048      ,snake_case__=8      ,snake_case__=0.0      ,snake_case__=0.0      ,snake_case__=True      ,snake_case__="relu"      ,snake_case__=256      ,snake_case__=0.1      ,snake_case__=0.0      ,snake_case__=0.0      ,snake_case__=0.02      ,snake_case__=1.0      ,snake_case__=False      ,snake_case__="sine"      ,snake_case__="resnet50"      ,snake_case__=True      ,snake_case__=False      ,snake_case__=1      ,snake_case__=5      ,snake_case__=2      ,snake_case__=1      ,snake_case__=1      ,snake_case__=5      ,snake_case__=2      ,snake_case__=0.1      ,**snake_case__      ,):
							if backbone_config is not None and use_timm_backbone:
										raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )

							if not use_timm_backbone:
										if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      CONFIG_MAPPING['resnet'](out_features=['stage4'] )
										elif isinstance(snake_case__      ,snake_case__ ):
													SCREAMING_SNAKE_CASE_   : List[str]					  =      backbone_config.get('model_type' )
													SCREAMING_SNAKE_CASE_   : Any					  =      CONFIG_MAPPING[backbone_model_type]
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      config_class.from_dict(snake_case__ )
										# set timm attributes to None
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : int					  =      None, None, None

							SCREAMING_SNAKE_CASE_   : List[str]					  =      use_timm_backbone
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      backbone_config
							SCREAMING_SNAKE_CASE_   : int					  =      num_channels
							SCREAMING_SNAKE_CASE_   : Dict					  =      num_queries
							SCREAMING_SNAKE_CASE_   : int					  =      d_model
							SCREAMING_SNAKE_CASE_   : List[str]					  =      encoder_ffn_dim
							SCREAMING_SNAKE_CASE_   : Any					  =      encoder_layers
							SCREAMING_SNAKE_CASE_   : Tuple					  =      encoder_attention_heads
							SCREAMING_SNAKE_CASE_   : Any					  =      decoder_ffn_dim
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      decoder_layers
							SCREAMING_SNAKE_CASE_   : Dict					  =      decoder_attention_heads
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dropout
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      attention_dropout
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      activation_dropout
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      activation_function
							SCREAMING_SNAKE_CASE_   : Any					  =      init_std
							SCREAMING_SNAKE_CASE_   : Dict					  =      init_xavier_std
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      encoder_layerdrop
							SCREAMING_SNAKE_CASE_   : Dict					  =      decoder_layerdrop
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      encoder_layers
							SCREAMING_SNAKE_CASE_   : str					  =      auxiliary_loss
							SCREAMING_SNAKE_CASE_   : Tuple					  =      position_embedding_type
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      backbone
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      use_pretrained_backbone
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      dilation
							# Hungarian matcher
							SCREAMING_SNAKE_CASE_   : Dict					  =      class_cost
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      bbox_cost
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      giou_cost
							# Loss coefficients
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      mask_loss_coefficient
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      dice_loss_coefficient
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      bbox_loss_coefficient
							SCREAMING_SNAKE_CASE_   : Dict					  =      giou_loss_coefficient
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      eos_coefficient
							super().__init__(is_encoder_decoder=snake_case__      ,**snake_case__ )






				@property
				def 		snake_case	(			self ):
							return self.encoder_attention_heads






				@property
				def 		snake_case	(			self ):
							return self.d_model






				@classmethod
				def 		snake_case	(			cls      ,snake_case__      ,**snake_case__ ):
							return cls(backbone_config=snake_case__      ,**snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      copy.deepcopy(self.__dict__ )
							if output["backbone_config"] is not None:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.backbone_config.to_dict()
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.__class__.model_type
							return output







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       int						=     version.parse("1.11"							)






				@property
				def 		snake_case	(			self ):
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							        ('pixel_mask', {0: 'batch'}),
							    ] )






				@property
				def 		snake_case	(			self ):
							return 1E-5






				@property
				def 		snake_case	(			self ):
							return 12









import os

import numpy
import onnx





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      a.name
			SCREAMING_SNAKE_CASE_   : Dict					  =      b.name

			SCREAMING_SNAKE_CASE_   : str					  =      ''
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ''

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      a == b

			SCREAMING_SNAKE_CASE_   : str					  =      name_a
			SCREAMING_SNAKE_CASE_   : str					  =      name_b

			return res





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Dict  )       ->  Optional[int]:







			"""simple docstring"""





			for i, input_name in enumerate(node_proto.input  ):
						if input_name == name:
									node_proto.input.insert(lowerCamelCase_  ,							lowerCamelCase_  )
									node_proto.input.pop(i + 1  )

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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  List[Any]:







			"""simple docstring"""





			for n in graph_proto.node:
						_node_replace_input_with(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Any  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      list(model.graph.initializer  )
			SCREAMING_SNAKE_CASE_   : 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

						SCREAMING_SNAKE_CASE_   : List[str]					  =      inits[i].name
						SCREAMING_SNAKE_CASE_   : str					  =      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  ,							lowerCamelCase_  ,							lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.dirname(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.basename(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : int					  =      onnx.load(os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )  )

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

			SCREAMING_SNAKE_CASE_   : int					  =      set()
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {}
			SCREAMING_SNAKE_CASE_   : Dict					  =      []

			SCREAMING_SNAKE_CASE_   : int					  =      0

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

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

												SCREAMING_SNAKE_CASE_   : int					  =      inits[j].data_type
												SCREAMING_SNAKE_CASE_   : 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: '  ,							lowerCamelCase_  )
												total_reduced_size += mem_size

												SCREAMING_SNAKE_CASE_   : int					  =      inits[i].name
												SCREAMING_SNAKE_CASE_   : List[Any]					  =      inits[j].name

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

			print('total reduced size: '  ,							total_reduced_size / 10_24 / 10_24 / 10_24  ,							'GB'  )

			SCREAMING_SNAKE_CASE_   : int					  =      sorted(lowerCamelCase_  )
			_remove_dup_initializers_from_model(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'optimized_' + model_file_name
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )
			onnx.save(lowerCamelCase_  ,							lowerCamelCase_  )

			return new_model



