'''simple docstring'''





from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url




class        __A      ( A_	):
    a__       :						Optional[Any]          =  """"""
    a__       :						Tuple          =  """hf-legacy"""  # "hf://"" is reserved for hffs



    def __init__(self    :    Union[str, Any]   ,			__a    :    Optional[DatasetInfo] = None   ,			__a    :    Optional[str] = None   ,			**__a    :    Optional[int]   ,			):
         super().__init__(self   ,			**_lowerCamelCase )
         UpperCAmelCase_           =   repo_info
         UpperCAmelCase_           =   token
         UpperCAmelCase_           =   None



    def 					_lowercase						(self    :    int ):
         if self.dir_cache is None:
              UpperCAmelCase_           =   {}
              for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   UpperCAmelCase_           =   {
                       "name": hf_file.rfilename,
                       "size": None,
                       "type": "file",
                   }
                   self.dir_cache.update(
                       {
                           str(_lowerCamelCase ): {"name": str(_lowerCamelCase ), "size": None, "type": "directory"}
                           for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
                       } )



    def 					_lowercase						(self    :    Any   ,			__a    :    str   ,			__a    :    str = "rb"   ,			**__a    :    int   ,			):
         if not isinstance(self.repo_info   ,			_lowerCamelCase ):
              raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
         UpperCAmelCase_           =   hf_hub_url(self.repo_info.id   ,			_lowerCamelCase   ,			revision=self.repo_info.sha )
         return fsspec.open(
             _lowerCamelCase   ,			mode=_lowerCamelCase   ,			headers=get_authentication_headers_for_url(_lowerCamelCase   ,			use_auth_token=self.token )   ,			client_kwargs={"trust_env": True}   ,			).open()



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Union[str, Any]   ,			**__a    :    int ):
         self._get_dirs()
         UpperCAmelCase_           =   self._strip_protocol(_lowerCamelCase )
         if path in self.dir_cache:
              return self.dir_cache[path]
         else:
              raise FileNotFoundError(_lowerCamelCase )






    def 					_lowercase						(self    :    List[str]   ,			__a    :    int   ,			__a    :    str=False   ,			**__a    :    int ):
         self._get_dirs()
         UpperCAmelCase_           =   PurePosixPath(path.strip("/" ) )
         UpperCAmelCase_           =   {}
         for p, f in self.dir_cache.items():
              UpperCAmelCase_           =   PurePosixPath(p.strip("/" ) )
              UpperCAmelCase_           =   p.parent
              if root == path:
                   UpperCAmelCase_           =   f
         UpperCAmelCase_           =   list(paths.values() )
         if detail:
              return out
         else:
              return sorted(f["name"] for f in out )






"""simple docstring"""



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__          = 128
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__          = 2000
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) * 1000 / max_iters))



