'''simple docstring'''


import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast






@dataclass
class 					__SCREAMING_SNAKE_CASE						(	datasets.BuilderConfig	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		None







class 					__SCREAMING_SNAKE_CASE						(	datasets.ArrowBasedBuilder	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		PandasConfig


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return datasets.DatasetInfo(features=self.config.features						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if not self.config.data_files:
            raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''						)
       UpperCAmelCase_							:       str       =				dl_manager.download_and_extract(self.config.data_files						)
       if isinstance(a_				,		(str, list, tuple)						):
            UpperCAmelCase_							:       Dict       =				data_files
            if isinstance(a_				,		a_						):
                 UpperCAmelCase_							:       Tuple       =				[files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            UpperCAmelCase_							:       Dict       =				[dl_manager.iter_files(a_						) for file in files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,		gen_kwargs={'files': files}						)]
       UpperCAmelCase_							:       Dict       =				[]
       for split_name, files in data_files.items():
            if isinstance(a_				,		a_						):
                 UpperCAmelCase_							:       Dict       =				[files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            UpperCAmelCase_							:       Any       =				[dl_manager.iter_files(a_						) for file in files]
            splits.append(datasets.SplitGenerator(name=a_				,		gen_kwargs={'files': files}						)						)
       return splits


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if self.config.features is not None:
            # more expensive cast to support nested features with keys in a different order
            # allows str <-> int/float or str to Audio for example
            UpperCAmelCase_							:       Optional[int]       =				table_cast(a_				,		self.config.features.arrow_schema						)
       return pa_table






  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       for i, file in enumerate(itertools.chain.from_iterable(a_						)						):
            with open(a_				,		'rb'						) as f:
                 UpperCAmelCase_							:       Optional[int]       =				pa.Table.from_pandas(pd.read_pickle(a_						)						)
                 yield i, self._cast_table(a_						)


'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

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

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






