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_			    =							{
    '''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  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Union[str, Any]  			=   '''beit'''



   def __init__(	self					:  Optional[int]		,  _lowercase					:  Union[str, Any]=8_192		,  _lowercase					:  List[str]=768		,  _lowercase					:  str=12		,  _lowercase					:  Optional[Any]=12		,  _lowercase					:  Union[str, Any]=3_072		,  _lowercase					:  Dict="gelu"		,  _lowercase					:  Optional[int]=0.0		,  _lowercase					:  Union[str, Any]=0.0		,  _lowercase					:  Union[str, Any]=0.02		,  _lowercase					:  str=1E-12		,  _lowercase					:  str=224		,  _lowercase					:  str=16		,  _lowercase					:  List[Any]=3		,  _lowercase					:  Optional[Any]=False		,  _lowercase					:  Dict=False		,  _lowercase					:  Dict=False		,  _lowercase					:  str=False		,  _lowercase					:  Tuple=0.1		,  _lowercase					:  Any=0.1		,  _lowercase					:  Union[str, Any]=True		,  _lowercase					:  Optional[int]=[3, 5, 7, 11]		,  _lowercase					:  Union[str, Any]=[1, 2, 3, 6]		,  _lowercase					:  str=True		,  _lowercase					:  Any=0.4		,  _lowercase					:  str=256		,  _lowercase					:  Tuple=1		,  _lowercase					:  List[str]=False		,  _lowercase					:  List[str]=255		,  **_lowercase					:  Tuple		,  ):



    """simple docstring"""



    super().__init__(**_lowercase							)

    _UpperCamelCase:     Union[str, Any]							= vocab_size
    _UpperCamelCase:     Dict							= hidden_size
    _UpperCamelCase:     Any							= num_hidden_layers
    _UpperCamelCase:     Dict							= num_attention_heads
    _UpperCamelCase:     List[str]							= intermediate_size
    _UpperCamelCase:     Union[str, Any]							= hidden_act
    _UpperCamelCase:     Dict							= hidden_dropout_prob
    _UpperCamelCase:     Dict							= attention_probs_dropout_prob
    _UpperCamelCase:     Dict							= initializer_range
    _UpperCamelCase:     Dict							= layer_norm_eps

    _UpperCamelCase:     int							= image_size
    _UpperCamelCase:     Any							= patch_size
    _UpperCamelCase:     Dict							= num_channels
    _UpperCamelCase:     Union[str, Any]							= use_mask_token
    _UpperCamelCase:     Optional[Any]							= use_absolute_position_embeddings
    _UpperCamelCase:     str							= use_relative_position_bias
    _UpperCamelCase:     Union[str, Any]							= use_shared_relative_position_bias
    _UpperCamelCase:     Any							= layer_scale_init_value
    _UpperCamelCase:     Any							= drop_path_rate
    _UpperCamelCase:     Tuple							= use_mean_pooling
    # decode head attributes (semantic segmentation)
    _UpperCamelCase:     Optional[Any]							= out_indices
    _UpperCamelCase:     List[str]							= pool_scales
    # auxiliary head attributes (semantic segmentation)
    _UpperCamelCase:     Any							= use_auxiliary_head
    _UpperCamelCase:     int							= auxiliary_loss_weight
    _UpperCamelCase:     Union[str, Any]							= auxiliary_channels
    _UpperCamelCase:     List[Any]							= auxiliary_num_convs
    _UpperCamelCase:     Dict							= auxiliary_concat_input
    _UpperCamelCase:     Dict							= semantic_loss_ignore_index




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   version.parse('''1.11'''    )



   @property
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



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



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



    """simple docstring"""



    return 1E-4
import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase_			    =							'''1'''
UpperCAmelCase_			    =							'''0'''
UpperCAmelCase_			    =							'''1'''

UpperCAmelCase_			    =							ort.SessionOptions()
UpperCAmelCase_			    =							ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
UpperCAmelCase_			    =							['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
UpperCAmelCase_			    =							ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase_			    =							ort.RunOptions()

UpperCAmelCase_			    =							1_2_8
UpperCAmelCase_			    =							1
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
UpperCAmelCase_			    =							time.time()
UpperCAmelCase_			    =							2_0_0_0
UpperCAmelCase_			    =							{}
for iter in range(max_iters):
  UpperCAmelCase_			    =							sess.run(
      None,
      {
          sess.get_inputs()[0].name: input_ids,
          sess.get_inputs()[1].name: attention_mask,
          sess.get_inputs()[2].name: token_type_ids,
      },
      run_options=run_opt,
  )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1_0_0_0 / max_iters))