





'''simple docstring'''

import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Tuple		     ="▁"

__lowerCAmelCase							:     Dict		     ={"vocab_file": "prophetnet.tokenizer"}

__lowerCAmelCase							:     Dict		     ={
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__lowerCAmelCase							:     Dict		     ={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__lowerCAmelCase							:     Dict		     ={
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}





def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]	):
    A__    =      collections.OrderedDict()
    with open(_lowerCamelCase						,	"r"						,	encoding="utf-8"	) as reader:
        A__    =      reader.readlines()
    for index, token in enumerate(_lowerCamelCase	):
        A__    =      token.rstrip("\n"	)
        A__    =      index
    return vocab






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    VOCAB_FILES_NAMES
 __lowercase       		=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase       		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase       		=    ["""input_ids""", """attention_mask"""]
 def __init__(    self				:str						,	lowercase_				:str						,	lowercase_				:List[str]="[SEP]"						,	lowercase_				:Tuple="[SEP]"						,	lowercase_				:Tuple="[SEP]"						,	lowercase_				:Tuple="[UNK]"						,	lowercase_				:int="[PAD]"						,	lowercase_				:Optional[Any]="[CLS]"						,	lowercase_				:int="[MASK]"						,	lowercase_				:Optional[Dict[str, Any]] = None						,	**lowercase_				:Optional[int]						,	)->							None:
     A__    =      {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=lowercase_						,	eos_token=lowercase_						,	sep_token=lowercase_						,	unk_token=lowercase_						,	pad_token=lowercase_						,	cls_token=lowercase_						,	mask_token=lowercase_						,	sp_model_kwargs=self.sp_model_kwargs						,	**lowercase_						,	)

     try:
         import sentencepiece as spm
     except ImportError:
         logger.warning(
             "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
             " pip install sentencepiece"							)
         raise

     A__    =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(str(lowercase_							)							)
     A__    =      vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # put special tokens and [unused] tokens into the vocab
     A__    =      {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

     for i in range(10							):
         A__    =      F"[unused{i}]"
         A__    =      5 + i

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     A__    =      12
     A__    =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
     for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(lowercase_							)
 def __getstate__(    self				:Union[str, Any]							)->							Tuple:
     A__    =      self.__dict__.copy()
     A__    =      None
     return state
 def __setstate__(    self				:Union[str, Any]						,	lowercase_				:Dict							)->							List[str]:
     A__    =      d
     try:
         import sentencepiece as spm
     except ImportError:
         logger.warning(
             "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
             " pip install sentencepiece"							)
         raise

     # for backward compatibility
     if not hasattr(self						,	"sp_model_kwargs"							):
         A__    =      {}

     A__    =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(self.vocab_file							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:List[int]						,	lowercase_				:Optional[List[int]] = None						,	lowercase_				:bool = False							)->							List[int]:

     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_						,	token_ids_a=lowercase_						,	already_has_special_tokens=lowercase_							)

     if token_ids_a is None:
         return ([0] * len(lowercase_							)) + [1]
     return ([0] * len(lowercase_							)) + [1] + ([0] * len(lowercase_							)) + [1]
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:List[int]						,	lowercase_				:Optional[List[int]] = None							)->							List[int]:
     A__    =      [self.sep_token_id]

     if token_ids_a is None:
         return len(token_ids_a + sep							) * [0]
     return len(token_ids_a + sep + sep + token_ids_a + sep							) * [0]
 @property
 def 					UpperCAmelCase_							(    self				:Any							)->							List[Any]:
     return len(self.sp_model							) + self.fairseq_offset
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Dict:
     A__    =      {self.convert_ids_to_tokens(lowercase_							): i for i in range(self.vocab_size							)}
     vocab.update(self.added_tokens_encoder							)
     return vocab
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:str							)->							str:
     return self.sp_model.encode(lowercase_						,	out_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:Dict							)->							Optional[int]:
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     A__    =      self.sp_model.PieceToId(lowercase_							)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:Optional[Any]							)->							str:
     if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Union[str, Any]							)->							Tuple:
     A__    =      "".join(lowercase_							).replace(lowercase_						,	" "							).strip()
     return out_string
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str						,	lowercase_				:Optional[str] = None							)->							Tuple[str]:
     if not os.path.isdir(lowercase_							):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
         return
     A__    =      os.path.join(
         lowercase_						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

     if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase_							) and os.path.isfile(self.vocab_file							):
         copyfile(self.vocab_file						,	lowercase_							)
     elif not os.path.isfile(self.vocab_file							):
         with open(lowercase_						,	"wb"							) as fi:
             A__    =      self.sp_model.serialized_model_proto()
             fi.write(lowercase_							)

     return (out_vocab_file,)



 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:List[int]						,	lowercase_				:Optional[List[int]] = None							)->							List[int]:

     if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
     A__    =      [self.sep_token_id]
     return token_ids_a + sep + token_ids_a + sep










'''simple docstring'''

import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__lowerCAmelCase							:     Tuple		     =trt.Logger(trt.Logger.WARNING)
__lowerCAmelCase							:     Optional[Any]		     =absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__lowerCAmelCase							:     List[Any]		     =logging.getLogger(__name__)

__lowerCAmelCase							:     Optional[Any]		     =argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=384,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=128,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=20,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=30,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

__lowerCAmelCase							:     Tuple		     =parser.parse_args()

if args.tokenizer_name:
  __lowerCAmelCase							:     int		     =AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
  raise ValueError(
      "You are instantiating a new tokenizer from scratch. This is not supported by this script."
      "You can do it from another script, save it, and load it from here, using --tokenizer_name."
  )

logger.info("Training/evaluation parameters %s", args)

__lowerCAmelCase							:     Union[str, Any]		     =args.per_device_eval_batch_size

__lowerCAmelCase							:     List[Any]		     =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCAmelCase							:     Tuple		     =True

__lowerCAmelCase							:     int		     ="temp_engine/bert-fp32.engine"
if args.fpaa:
  __lowerCAmelCase							:     Tuple		     ="temp_engine/bert-fp16.engine"
if args.inta:
  __lowerCAmelCase							:     Optional[int]		     ="temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
  os.makedirs("temp_engine")

__lowerCAmelCase							:     Tuple		     =1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
  with open(args.onnx_model_path, "rb") as model:
    if not parser.parse(model.read()):
      for error in range(parser.num_errors):
        print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
  __lowerCAmelCase							:     Optional[Any]		     =[network.get_input(i) for i in range(network.num_inputs)]
  __lowerCAmelCase							:     Any		     =[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

  with builder.create_builder_config() as config:
    __lowerCAmelCase							:     Optional[Any]		     =1 << 50
    if STRICT_TYPES:
      config.set_flag(trt.BuilderFlag.STRICT_TYPES)
    if args.fpaa:
      config.set_flag(trt.BuilderFlag.FPaa)
    if args.inta:
      config.set_flag(trt.BuilderFlag.INTa)
    __lowerCAmelCase							:     int		     =builder.create_optimization_profile()
    config.add_optimization_profile(profile)
    for i in range(len(input_names)):
      profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
    __lowerCAmelCase							:     Dict		     =builder.build_engine(network, config)

    # serialize_engine and store in file (can be directly loaded and deserialized):
    with open(engine_name, "wb") as f:
      f.write(engine.serialize())





def 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Dict	):
    A__    =      np.asarray(inputs["input_ids"]						,	dtype=np.intaa	)
    A__    =      np.asarray(inputs["attention_mask"]						,	dtype=np.intaa	)
    A__    =      np.asarray(inputs["token_type_ids"]						,	dtype=np.intaa	)

    # Copy inputs
    cuda.memcpy_htod_async(d_inputs[0]						,	input_ids.ravel()						,	_lowerCamelCase	)
    cuda.memcpy_htod_async(d_inputs[1]						,	attention_mask.ravel()						,	_lowerCamelCase	)
    cuda.memcpy_htod_async(d_inputs[2]						,	token_type_ids.ravel()						,	_lowerCamelCase	)
    # start time
    A__    =      time.time()
    # Run inference
    context.execute_async(
        bindings=[int(_lowerCamelCase	) for d_inp in d_inputs] + [int(_lowerCamelCase	), int(_lowerCamelCase	)]						,	stream_handle=stream.handle	)
    # Transfer predictions back from GPU
    cuda.memcpy_dtoh_async(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    cuda.memcpy_dtoh_async(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    # Synchronize the stream and take time
    stream.synchronize()
    # end time
    A__    =      time.time()
    A__    =      end_time - start_time
    A__    =      (h_outputa, h_outputa)
    # print(outputs)
    return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__lowerCAmelCase							:     str		     =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
  datasets.utils.logging.set_verbosity_warning()
  transformers.utils.logging.set_verbosity_info()
else:
  datasets.utils.logging.set_verbosity_error()
  transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
  set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  __lowerCAmelCase							:     List[Any]		     =load_dataset(args.dataset_name, args.dataset_config_name)
else:
  raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__lowerCAmelCase							:     Optional[Any]		     =raw_datasets["validation"].column_names

__lowerCAmelCase							:     Optional[Any]		     ="question" if "question" in column_names else column_names[0]
__lowerCAmelCase							:     str		     ="context" if "context" in column_names else column_names[1]
__lowerCAmelCase							:     Optional[Any]		     ="answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__lowerCAmelCase							:     Any		     =tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
  logger.warning(
      f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
      f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
  )

__lowerCAmelCase							:     Any		     =min(args.max_seq_length, tokenizer.model_max_length)





def 						UpperCamelCase    (							_lowerCamelCase : Optional[int]	):
    # Some of the questions have lots of whitespace on the left, which is not useful and will make the
    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
    # left whitespace
    A__    =      [q.lstrip() for q in examples[question_column_name]]

    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    A__    =      tokenizer(
        examples[question_column_name if pad_on_right else context_column_name]						,	examples[context_column_name if pad_on_right else question_column_name]						,	truncation="only_second" if pad_on_right else "only_first"						,	max_length=_lowerCamelCase						,	stride=args.doc_stride						,	return_overflowing_tokens=_lowerCamelCase						,	return_offsets_mapping=_lowerCamelCase						,	padding="max_length"						,	)

    # Since one example might give us several features if it has a long context, we need a map from a feature to
    # its corresponding example. This key gives us just that.
    A__    =      tokenized_examples.pop("overflow_to_sample_mapping"	)

    # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
    # corresponding example_id and we will store the offset mappings.
    A__    =      []

    for i in range(len(tokenized_examples["input_ids"]	)	):
        # Grab the sequence corresponding to that example (to know what is the context and what is the question).
        A__    =      tokenized_examples.sequence_ids(_lowerCamelCase	)
        A__    =      1 if pad_on_right else 0

        # One example can give several spans, this is the index of the example containing this span of text.
        A__    =      sample_mapping[i]
        tokenized_examples["example_id"].append(examples["id"][sample_index]	)

        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
        # position is part of the context or not.
        A__    =      [
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples["offset_mapping"][i]	)
        ]

    return tokenized_examples


__lowerCAmelCase							:     str		     =raw_datasets["validation"]
# Validation Feature Creation
__lowerCAmelCase							:     Union[str, Any]		     =eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

__lowerCAmelCase							:     List[Any]		     =default_data_collator

__lowerCAmelCase							:     List[Any]		     =eval_dataset.remove_columns(["example_id", "offset_mapping"])
__lowerCAmelCase							:     List[str]		     =DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]="eval"	):
    # Post-processing: we match the start logits and end logits to answers in the original context.
    A__    =      postprocess_qa_predictions(
        examples=_lowerCamelCase						,	features=_lowerCamelCase						,	predictions=_lowerCamelCase						,	version_2_with_negative=args.version_2_with_negative						,	n_best_size=args.n_best_size						,	max_answer_length=args.max_answer_length						,	null_score_diff_threshold=args.null_score_diff_threshold						,	output_dir=args.output_dir						,	prefix=_lowerCamelCase						,	)
    # Format the result to the format the metric expects.
    if args.version_2_with_negative:
        A__    =      [
            {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
        ]
    else:
        A__    =      [{"id": k, "prediction_text": v} for k, v in predictions.items()]

    A__    =      [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
    return EvalPrediction(predictions=_lowerCamelCase						,	label_ids=_lowerCamelCase	)


__lowerCAmelCase							:     Tuple		     =load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
  # setup for TRT inferrence
  for i in range(len(input_names)):
    context.set_binding_shape(i, INPUT_SHAPE)
  assert context.all_binding_shapes_specified





  def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]	):
      return trt.volume(engine.get_binding_shape(_lowerCamelCase	)	) * engine.get_binding_dtype(_lowerCamelCase	).itemsize

  # Allocate device memory for inputs and outputs.
  __lowerCAmelCase							:     Any		     =[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

  # Allocate output buffer
  __lowerCAmelCase							:     List[Any]		     =cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
  __lowerCAmelCase							:     List[str]		     =cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
  __lowerCAmelCase							:     List[str]		     =cuda.mem_alloc(h_outputa.nbytes)
  __lowerCAmelCase							:     int		     =cuda.mem_alloc(h_outputa.nbytes)

  # Create a stream in which to copy inputs/outputs and run inference.
  __lowerCAmelCase							:     Optional[Any]		     =cuda.Stream()

  # Evaluation
  logger.info("***** Running Evaluation *****")
  logger.info(f"""  Num examples = {len(eval_dataset)}""")
  logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

  __lowerCAmelCase							:     str		     =0.0
  __lowerCAmelCase							:     Tuple		     =0
  __lowerCAmelCase							:     List[str]		     =timeit.default_timer()

  __lowerCAmelCase							:     Union[str, Any]		     =None
  for step, batch in enumerate(eval_dataloader):
    __lowerCAmelCase     ,					__lowerCAmelCase							:     Dict		     =model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
    total_time += infer_time
    niter += 1

    __lowerCAmelCase     ,					__lowerCAmelCase							:     List[Any]		     =outputs
    __lowerCAmelCase							:     Tuple		     =torch.tensor(start_logits)
    __lowerCAmelCase							:     Tuple		     =torch.tensor(end_logits)

    # necessary to pad predictions and labels for being gathered
    __lowerCAmelCase							:     Tuple		     =accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
    __lowerCAmelCase							:     Union[str, Any]		     =accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

    __lowerCAmelCase							:     int		     =(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
    __lowerCAmelCase							:     List[Any]		     =logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

  if all_preds is not None:
    __lowerCAmelCase							:     Dict		     =nested_truncate(all_preds, len(eval_dataset))

  __lowerCAmelCase							:     Optional[int]		     =timeit.default_timer() - start_time
  logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
  # Inference time from TRT
  logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1000 / niter))
  logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1000))



  logger.info("Total Number of Inference =  %d", niter)

__lowerCAmelCase							:     Optional[Any]		     =post_processing_function(eval_examples, eval_dataset, all_preds)
__lowerCAmelCase							:     Optional[Any]		     =metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



logger.info(f"""Evaluation metrics: {eval_metric}""")



