


"""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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "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    lowerCamelCase__						(		_a    ):
      a :     Optional[int]											=		"""segformer"""
      def __init__(		self	:	str      ,       A_	:	Optional[int]=3      ,       A_	:	Any=4      ,       A_	:	Optional[int]=[2, 2, 2, 2]      ,       A_	:	str=[8, 4, 2, 1]      ,       A_	:	Any=[3_2, 6_4, 1_6_0, 2_5_6]      ,       A_	:	Union[str, Any]=[7, 3, 3, 3]      ,       A_	:	Any=[4, 2, 2, 2]      ,       A_	:	int=[1, 2, 5, 8]      ,       A_	:	Union[str, Any]=[4, 4, 4, 4]      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	List[str]=0.0      ,       A_	:	Any=0.0      ,       A_	:	Optional[Any]=0.1      ,       A_	:	List[str]=0.02      ,       A_	:	Tuple=0.1      ,       A_	:	Optional[Any]=1e-6      ,       A_	:	Optional[int]=2_5_6      ,       A_	:	Optional[Any]=2_5_5      ,       **A_	:	Union[str, Any]      ,       ):






       '''simple docstring'''


       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_      ,       )

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






class    lowerCamelCase__						(		_a    ):
      a :     List[str]											=		version.parse("""1.11"""    )
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       return 1e-4






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       return 1_2




"""simple docstring"""

import os

import numpy
import onnx


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	a.name
 __lowercase									=	b.name

 __lowercase									=	""""""
 __lowercase									=	""""""

 __lowercase									=	a == b

 __lowercase									=	name_a
 __lowercase									=	name_b

 return res


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict ):






 """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__	: Optional[Any]				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: str ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 __lowercase									=	list(model.graph.initializer )
 __lowercase									=	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

  __lowercase									=	inits[i].name
  __lowercase									=	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[Any] ):






 """simple docstring"""







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

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

 __lowercase									=	list(model.graph.initializer )

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

 __lowercase									=	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__ )

    __lowercase									=	inits[j].data_type
    __lowercase									=	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

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

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

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

 __lowercase									=	sorted(UpperCamelCase__ )
 _remove_dup_initializers_from_model(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	"""optimized_""" + model_file_name
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )
 onnx.save(UpperCamelCase__				,   UpperCamelCase__ )

 return new_model
