






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   :      Any				=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      List[str]				=		{
    """facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	'''timesformer'''







				def __init__(    self							,       __lowerCAmelCase=2_2_4							,       __lowerCAmelCase=1_6							,       __lowerCAmelCase=3							,       __lowerCAmelCase=8							,       __lowerCAmelCase=7_6_8							,       __lowerCAmelCase=1_2							,       __lowerCAmelCase=1_2							,       __lowerCAmelCase=3_0_7_2							,       __lowerCAmelCase="gelu"							,       __lowerCAmelCase=0.0							,       __lowerCAmelCase=0.0							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=1E-6							,       __lowerCAmelCase=True							,       __lowerCAmelCase="divided_space_time"							,       __lowerCAmelCase=0							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase  )

								__magic_name__		:str   =   image_size
								__magic_name__		:Tuple   =   patch_size
								__magic_name__		:Any   =   num_channels
								__magic_name__		:str   =   num_frames

								__magic_name__		:Optional[Any]   =   hidden_size
								__magic_name__		:Union[str, Any]   =   num_hidden_layers
								__magic_name__		:Optional[Any]   =   num_attention_heads
								__magic_name__		:Optional[int]   =   intermediate_size
								__magic_name__		:List[Any]   =   hidden_act
								__magic_name__		:str   =   hidden_dropout_prob
								__magic_name__		:Optional[Any]   =   attention_probs_dropout_prob
								__magic_name__		:Optional[Any]   =   initializer_range
								__magic_name__		:Dict   =   layer_norm_eps
								__magic_name__		:List[Any]   =   qkv_bias

								__magic_name__		:int   =   attention_type
								__magic_name__		:Optional[Any]   =   drop_path_rate











import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =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...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =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))