


import os

import numpy
import onnx


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	List[Any]   	=		a.name
       UpperCamelCase    :	List[Any]   	=		b.name

       UpperCamelCase    :	Optional[int]   	=		''''''
       UpperCamelCase    :	Any   	=		''''''

       UpperCamelCase    :	Any   	=		a == b

       UpperCamelCase    :	Tuple   	=		name_a
       UpperCamelCase    :	List[Any]   	=		name_b

       return res


def      a	(							SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





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

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


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





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


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		list(model.graph.initializer       )
       UpperCamelCase    :	Optional[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    :	Union[str, Any]   	=		inits[i].name
              UpperCamelCase    :	List[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							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


def      a	(							SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		os.path.dirname(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Any   	=		os.path.basename(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	List[Any]   	=		onnx.load(os.path.join(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )       )

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

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

       UpperCamelCase    :	Dict   	=		0

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

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

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

                            UpperCamelCase    :	Optional[Any]   	=		inits[i].name
                            UpperCamelCase    :	Tuple   	=		inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(SCREAMING_SNAKE_CASE_       )
                            else:
                                   UpperCamelCase    :	List[Any]   	=		[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'''       )

       UpperCamelCase    :	Optional[Any]   	=		sorted(SCREAMING_SNAKE_CASE_       )
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	int   	=		'''optimized_''' + model_file_name
       UpperCamelCase    :	int   	=		os.path.join(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       onnx.save(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       return new_model





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__UpperCAmelCase : str 			=				logging.get_logger(__name__)

__UpperCAmelCase : Dict 			=				{
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					"yolos"






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=768 ,					__SCREAMING_SNAKE_CASE=12 ,					__SCREAMING_SNAKE_CASE=12 ,					__SCREAMING_SNAKE_CASE=3_072 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1e-12 ,					__SCREAMING_SNAKE_CASE=[512, 864] ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=100 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.1 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Tuple   	=		hidden_size
              UpperCamelCase    :	List[Any]   	=		num_hidden_layers
              UpperCamelCase    :	int   	=		num_attention_heads
              UpperCamelCase    :	Dict   	=		intermediate_size
              UpperCamelCase    :	Dict   	=		hidden_act
              UpperCamelCase    :	int   	=		hidden_dropout_prob
              UpperCamelCase    :	Any   	=		attention_probs_dropout_prob
              UpperCamelCase    :	Optional[Any]   	=		initializer_range
              UpperCamelCase    :	List[Any]   	=		layer_norm_eps
              UpperCamelCase    :	int   	=		image_size
              UpperCamelCase    :	Any   	=		patch_size
              UpperCamelCase    :	str   	=		num_channels
              UpperCamelCase    :	str   	=		qkv_bias
              UpperCamelCase    :	Tuple   	=		num_detection_tokens
              UpperCamelCase    :	List[Any]   	=		use_mid_position_embeddings
              UpperCamelCase    :	Dict   	=		auxiliary_loss
              # Hungarian matcher
              UpperCamelCase    :	Optional[Any]   	=		class_cost
              UpperCamelCase    :	Union[str, Any]   	=		bbox_cost
              UpperCamelCase    :	Any   	=		giou_cost
              # Loss coefficients
              UpperCamelCase    :	List[Any]   	=		bbox_loss_coefficient
              UpperCamelCase    :	Union[str, Any]   	=		giou_loss_coefficient
              UpperCamelCase    :	Dict   	=		eos_coefficient




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					version.parse("1.11")






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 1e-4






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 12

