

'''simple docstring'''
import os

import numpy
import onnx


def 	_lowerCAmelCase (						_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					int    )				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =a.name
     _SCREAMING_SNAKE_CASE							   =b.name

     _SCREAMING_SNAKE_CASE							   =''''''
     _SCREAMING_SNAKE_CASE							   =''''''

     _SCREAMING_SNAKE_CASE							   =a == b

     _SCREAMING_SNAKE_CASE							   =name_a
     _SCREAMING_SNAKE_CASE							   =name_b

     return res


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					Dict    )				->      Union[str, Any]:
     """simple docstring"""




     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						:					int							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Any    )				->      Tuple:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Optional[Any]    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =list(model.graph.initializer    )
     _SCREAMING_SNAKE_CASE							   =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							   =inits[i].name
          _SCREAMING_SNAKE_CASE							   =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						:					List[str]    )				->      Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =os.path.dirname(UpperCamelCase__    )
     _SCREAMING_SNAKE_CASE							   =os.path.basename(UpperCamelCase__    )

     _SCREAMING_SNAKE_CASE							   =onnx.load(os.path.join(UpperCamelCase__							,			UpperCamelCase__    )    )

     _SCREAMING_SNAKE_CASE							   =list(model.graph.initializer    )

     _SCREAMING_SNAKE_CASE							   =set()
     _SCREAMING_SNAKE_CASE							   ={}
     _SCREAMING_SNAKE_CASE							   =[]

     _SCREAMING_SNAKE_CASE							   =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__    )

                    _SCREAMING_SNAKE_CASE							   =inits[j].data_type
                    _SCREAMING_SNAKE_CASE							   =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

                    _SCREAMING_SNAKE_CASE							   =inits[i].name
                    _SCREAMING_SNAKE_CASE							   =inits[j].name

                    if name_i in dup_map:
                         dup_map[name_i].append(UpperCamelCase__    )
                    else:
                         _SCREAMING_SNAKE_CASE							   =[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							   =sorted(UpperCamelCase__    )
     _remove_dup_initializers_from_model(UpperCamelCase__							,			UpperCamelCase__							,			UpperCamelCase__    )

     _SCREAMING_SNAKE_CASE							   ='''optimized_''' + model_file_name
     _SCREAMING_SNAKE_CASE							   =os.path.join(UpperCamelCase__							,			UpperCamelCase__    )
     onnx.save(UpperCamelCase__							,			UpperCamelCase__    )

     return new_model



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase			:				List[str] 			=					{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Union[str, Any] 			=					[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				str 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
