





'''simple docstring'''
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__							:							Tuple =logging.get_logger(__name__)

a__							:							int ={
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="beit"






 def __init__(       self       :							List[Any]							,	__A       :							List[Any]=8_1_9_2							,	__A       :							int=7_6_8							,	__A       :							Tuple=1_2							,	__A       :							Optional[Any]=1_2							,	__A       :							Union[str, Any]=3_0_7_2							,	__A       :							Optional[Any]="gelu"							,	__A       :							Tuple=0.0							,	__A       :							int=0.0							,	__A       :							Optional[int]=0.02							,	__A       :							Tuple=1e-12							,	__A       :							Union[str, Any]=2_2_4							,	__A       :							Tuple=1_6							,	__A       :							Any=3							,	__A       :							List[Any]=False							,	__A       :							str=False							,	__A       :							Any=False							,	__A       :							Optional[Any]=False							,	__A       :							Optional[Any]=0.1							,	__A       :							Optional[int]=0.1							,	__A       :							Optional[Any]=True							,	__A       :							Any=[3, 5, 7, 1_1]							,	__A       :							str=[1, 2, 3, 6]							,	__A       :							List[str]=True							,	__A       :							Union[str, Any]=0.4							,	__A       :							Dict=2_5_6							,	__A       :							Any=1							,	__A       :							List[str]=False							,	__A       :							Tuple=2_5_5							,	**__A       :							List[str]							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  layer_norm_eps

        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  use_mask_token
        __UpperCamelCase       =  use_absolute_position_embeddings
        __UpperCamelCase       =  use_relative_position_bias
        __UpperCamelCase       =  use_shared_relative_position_bias
        __UpperCamelCase       =  layer_scale_init_value
        __UpperCamelCase       =  drop_path_rate
        __UpperCamelCase       =  use_mean_pooling
        # decode head attributes (semantic segmentation)
        __UpperCamelCase       =  out_indices
        __UpperCamelCase       =  pool_scales
        # auxiliary head attributes (semantic segmentation)
        __UpperCamelCase       =  use_auxiliary_head
        __UpperCamelCase       =  auxiliary_loss_weight
        __UpperCamelCase       =  auxiliary_channels
        __UpperCamelCase       =  auxiliary_num_convs
        __UpperCamelCase       =  auxiliary_concat_input
        __UpperCamelCase       =  semantic_loss_ignore_index






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



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






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






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        return 1e-4







'''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
