

"""simple docstring"""



import os

import numpy
import onnx





def  _snake_case     (    UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Dict			):
     A__										=  a.name
     A__										=  b.name

     A__										=  """"""
     A__										=  """"""

     A__										=  a == b

     A__										=  name_a
     A__										=  name_b

     return res





def  _snake_case     (    UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Any			):
     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  _snake_case     (    UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Union[str, Any]					,   UpperCAmelCase_  :					Optional[Any]			):
     for n in graph_proto.node:
          _node_replace_input_with(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)





def  _snake_case     (    UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					int			):
     A__										=  list(model.graph.initializer			)
     A__										=  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

          A__										=  inits[i].name
          A__										=  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  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]			):
     A__										=  os.path.dirname(UpperCAmelCase_			)
     A__										=  os.path.basename(UpperCAmelCase_			)

     A__										=  onnx.load(os.path.join(UpperCAmelCase_					,   UpperCAmelCase_			)			)

     A__										=  list(model.graph.initializer			)

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

     A__										=  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_			)

                    A__										=  inits[j].data_type
                    A__										=  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

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

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

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

     A__										=  sorted(UpperCAmelCase_			)
     _remove_dup_initializers_from_model(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)

     A__										=  """optimized_""" + model_file_name
     A__										=  os.path.join(UpperCAmelCase_					,   UpperCAmelCase_			)
     onnx.save(UpperCAmelCase_					,   UpperCAmelCase_			)

     return new_model









"""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


SCREAMING_SNAKE_CASE_					:	Any							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"segformer"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, Any]=3    ,						UpperCamelCase: int=4    ,						UpperCamelCase: int=[2, 2, 2, 2]    ,						UpperCamelCase: List[str]=[8, 4, 2, 1]    ,						UpperCamelCase: List[Any]=[32, 64, 1_60, 2_56]    ,						UpperCamelCase: Any=[7, 3, 3, 3]    ,						UpperCamelCase: Union[str, Any]=[4, 2, 2, 2]    ,						UpperCamelCase: Tuple=[1, 2, 5, 8]    ,						UpperCamelCase: Optional[int]=[4, 4, 4, 4]    ,						UpperCamelCase: Dict="gelu"    ,						UpperCamelCase: Optional[Any]=0.0    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: List[Any]=0.02    ,						UpperCamelCase: int=0.1    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=2_56    ,						UpperCamelCase: Union[str, Any]=2_55    ,						**UpperCamelCase: Optional[Any]    ,						):






         """simple docstring"""


         super().__init__(**UpperCamelCase       )

         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."""    ,						UpperCamelCase    ,						)

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



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					version.parse("1.11"				)

    @property
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


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

    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 1e-4





    @property
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         return 12






