




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_   		=     logging.get_logger(__name__)

a_   		=     {
    "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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Any    =					"beit"







      def __init__(     self					: List[str] ,       snake_case					: Any=8192 ,       snake_case					: Optional[int]=768 ,       snake_case					: Optional[Any]=12 ,       snake_case					: Any=12 ,       snake_case					: Optional[Any]=3072 ,       snake_case					: str="gelu" ,       snake_case					: str=0.0 ,       snake_case					: Optional[Any]=0.0 ,       snake_case					: str=0.02 ,       snake_case					: Dict=1E-12 ,       snake_case					: List[Any]=224 ,       snake_case					: Union[str, Any]=16 ,       snake_case					: Dict=3 ,       snake_case					: List[str]=False ,       snake_case					: Any=False ,       snake_case					: Dict=False ,       snake_case					: Dict=False ,       snake_case					: Tuple=0.1 ,       snake_case					: Any=0.1 ,       snake_case					: List[str]=True ,       snake_case					: Dict=[3, 5, 7, 11] ,       snake_case					: str=[1, 2, 3, 6] ,       snake_case					: List[Any]=True ,       snake_case					: int=0.4 ,       snake_case					: List[Any]=256 ,       snake_case					: List[str]=1 ,       snake_case					: Any=False ,       snake_case					: str=255 ,       **snake_case					: Optional[int] ,       ):
          super().__init__(**snake_case				)

          __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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: List[Any]    =					version.parse("1.11"   )







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







      @property
      def       snake_case	(     self					: str				):
          return 1E-4












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

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

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     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...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     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) * 1000 / max_iters))






