





'''simple docstring'''
import warnings
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


a__							:							Optional[Any] =logging.get_logger(__name__)

a__							:							Optional[Any] ={
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					int					="segformer"






 def __init__(       self       :							Optional[Any]							,	__A       :							Union[str, Any]=3							,	__A       :							List[str]=4							,	__A       :							Tuple=[2, 2, 2, 2]							,	__A       :							str=[8, 4, 2, 1]							,	__A       :							List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]							,	__A       :							str=[7, 3, 3, 3]							,	__A       :							int=[4, 2, 2, 2]							,	__A       :							List[Any]=[1, 2, 5, 8]							,	__A       :							int=[4, 4, 4, 4]							,	__A       :							List[Any]="gelu"							,	__A       :							str=0.0							,	__A       :							Tuple=0.0							,	__A       :							Union[str, Any]=0.1							,	__A       :							str=0.02							,	__A       :							Any=0.1							,	__A       :							Dict=1e-6							,	__A       :							Optional[Any]=2_5_6							,	__A       :							Optional[int]=2_5_5							,	**__A       :							List[str]							,	):
        super().__init__(**__A	)

        if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
               warnings.warn(
                   'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                   ' removed, as the behaviour will default to that of reshape_last_stage = True.'							,	__A							,	)

        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  num_encoder_blocks
        __UpperCamelCase       =  depths
        __UpperCamelCase       =  sr_ratios
        __UpperCamelCase       =  hidden_sizes
        __UpperCamelCase       =  patch_sizes
        __UpperCamelCase       =  strides
        __UpperCamelCase       =  mlp_ratios
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  classifier_dropout_prob
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  drop_path_rate
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  decoder_hidden_size
        __UpperCamelCase       =  kwargs.get('reshape_last_stage'							,	__A	)
        __UpperCamelCase       =  semantic_loss_ignore_index






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					=version.parse("1.11"       )






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






 @property
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return 1e-4






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        return 1_2







'''simple docstring'''
import os

import numpy
import onnx
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Union[str, Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  a.name
       __UpperCamelCase       =  b.name

       __UpperCamelCase       =  ''
       __UpperCamelCase       =  ''

       __UpperCamelCase       =  a == b

       __UpperCamelCase       =  name_a
       __UpperCamelCase       =  name_b

       return res
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  List[Any]     ) ->      Optional[int]:



       """simple docstring"""






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

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
              _graph_replace_input_with(node_proto.attribute[1].g				,  __lowercase				,  __lowercase     )
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[Any]				,  __lowercase						:  Dict     ) ->      int:



       """simple docstring"""






       for n in graph_proto.node:
              _node_replace_input_with(__lowercase				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  str     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  list(model.graph.initializer     )
       __UpperCamelCase       =  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       =  inits[i].name
              __UpperCamelCase       =  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				,  __lowercase				,  __lowercase     )


def 				lowercase__   (   __lowercase						:  Dict     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  os.path.dirname(__lowercase     )
       __UpperCamelCase       =  os.path.basename(__lowercase     )

       __UpperCamelCase       =  onnx.load(os.path.join(__lowercase				,  __lowercase     )     )

       __UpperCamelCase       =  list(model.graph.initializer     )

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

       __UpperCamelCase       =  0

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

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

                            __UpperCamelCase       =  inits[j].data_type
                            __UpperCamelCase       =  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: '				,  __lowercase     )
                            total_reduced_size += mem_size

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

                            if name_i in dup_map:
                                   dup_map[name_i].append(__lowercase     )
                            else:
                                   __UpperCamelCase       =  [name_j]
                            ind_to_replace.append((j, i)     )

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

       __UpperCamelCase       =  sorted(__lowercase     )
       _remove_dup_initializers_from_model(__lowercase				,  __lowercase				,  __lowercase     )

       __UpperCamelCase       =  'optimized_' + model_file_name
       __UpperCamelCase       =  os.path.join(__lowercase				,  __lowercase     )
       onnx.save(__lowercase				,  __lowercase     )

       return new_model
