
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowercase  :					str						    =							"""\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowercase  :					Optional[Any]						    =							"""\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

lowercase  :					List[str]						    =							"""
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if version.parse(scb.__version__      ) < version.parse("""1.4.12"""      ):
                  raise ImportWarning(
                      """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
                      """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""      )
           return datasets.MetricInfo(
               description=_DESCRIPTION		,citation=_CITATION		,homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""		,inputs_description=_KWARGS_DESCRIPTION		,features=datasets.Features(
                   {
                       """predictions""": datasets.Value("""string"""		,id="""sequence"""      ),
                       """references""": datasets.Sequence(datasets.Value("""string"""		,id="""sequence"""      )		,id="""references"""      ),
                   }      )		,codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""]		,reference_urls=[
                   """https://github.com/m-popovic/chrF""",
               ]		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = CHRF.CHAR_ORDER		,snake_case = CHRF.WORD_ORDER		,snake_case = CHRF.BETA		,snake_case = False		,snake_case = False		,snake_case = False		,):



           '''simple docstring'''







           lowercase							:     Any       	=      len(references[0]      )
           if any(len(snake_case      ) != references_per_prediction for refs in references      ):
                  raise ValueError("""Sacrebleu requires the same number of references for each prediction"""      )
           lowercase							:     int       	=      [[refs[i] for refs in references] for i in range(snake_case      )]

           lowercase							:     Tuple       	=      CHRF(snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      )
           lowercase							:     Optional[Any]       	=      sb_chrf.corpus_score(snake_case		,snake_case      )

           return {
               "score": output.score,
               "char_order": output.char_order,
               "word_order": output.word_order,
               "beta": output.beta,
           }







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


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

__UpperCAmelCase       :   Optional[Any]				       =							logging.getLogger(__name__)

__UpperCAmelCase       :   List[str]				       =							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",
)

__UpperCAmelCase       :   Tuple				       =							parser.parse_args()

if args.tokenizer_name:
      __UpperCAmelCase       :   Tuple				       =							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)

__UpperCAmelCase       :   Optional[Any]				       =							args.per_device_eval_batch_size

__UpperCAmelCase       :   Dict				       =							(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__UpperCAmelCase       :   Optional[int]				       =							True

__UpperCAmelCase       :   str				       =							"temp_engine/bert-fp32.engine"
if args.fpaa:
      __UpperCAmelCase       :   Tuple				       =							"temp_engine/bert-fp16.engine"
if args.inta:
      __UpperCAmelCase       :   List[Any]				       =							"temp_engine/bert-int8.engine"

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

__UpperCAmelCase       :   Optional[int]				       =							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
      __UpperCAmelCase       :   int				       =							[network.get_input(i) for i in range(network.num_inputs)]
      __UpperCAmelCase       :   List[Any]				       =							[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

      with builder.create_builder_config() as config:
            __UpperCAmelCase       :   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)
            __UpperCAmelCase       :   Any				       =							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)
            __UpperCAmelCase       :   Union[str, Any]				       =							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 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    str:
  __snake_case:						Tuple   =						np.asarray(inputs["""input_ids"""]					,					dtype=np.intaa)
  __snake_case:						Union[str, Any]   =						np.asarray(inputs["""attention_mask"""]					,					dtype=np.intaa)
  __snake_case:						List[str]   =						np.asarray(inputs["""token_type_ids"""]					,					dtype=np.intaa)

  # Copy inputs
  cuda.memcpy_htod_async(d_inputs[0]					,					input_ids.ravel()					,					SCREAMING_SNAKE_CASE__)
  cuda.memcpy_htod_async(d_inputs[1]					,					attention_mask.ravel()					,					SCREAMING_SNAKE_CASE__)
  cuda.memcpy_htod_async(d_inputs[2]					,					token_type_ids.ravel()					,					SCREAMING_SNAKE_CASE__)
  # start time
  __snake_case:						int   =						time.time()
  # Run inference
  context.execute_async(
      bindings=[int(SCREAMING_SNAKE_CASE__) for d_inp in d_inputs] + [int(SCREAMING_SNAKE_CASE__), int(SCREAMING_SNAKE_CASE__)]					,					stream_handle=stream.handle)
  # Transfer predictions back from GPU
  cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
  cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
  # Synchronize the stream and take time
  stream.synchronize()
  # end time
  __snake_case:						Optional[Any]   =						time.time()
  __snake_case:						Dict   =						end_time - start_time
  __snake_case:						Any   =						(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.
__UpperCAmelCase       :   Union[str, Any]				       =							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.
      __UpperCAmelCase       :   Union[str, 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.

__UpperCAmelCase       :   str				       =							raw_datasets["validation"].column_names

__UpperCAmelCase       :   Dict				       =							"question" if "question" in column_names else column_names[0]
__UpperCAmelCase       :   List[Any]				       =							"context" if "context" in column_names else column_names[1]
__UpperCAmelCase       :   List[str]				       =							"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__UpperCAmelCase       :   List[str]				       =							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}.'
      )

__UpperCAmelCase       :   Union[str, Any]				       =							min(args.max_seq_length, tokenizer.model_max_length)







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    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
  __snake_case:						Optional[int]   =						[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.
  __snake_case:						List[str]   =						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=SCREAMING_SNAKE_CASE__					,					stride=args.doc_stride					,					return_overflowing_tokens=SCREAMING_SNAKE_CASE__					,					return_offsets_mapping=SCREAMING_SNAKE_CASE__					,					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.
  __snake_case:						Optional[Any]   =						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.
  __snake_case:						int   =						[]

  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).
    __snake_case:						int   =						tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE__)
    __snake_case:						List[Any]   =						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.
    __snake_case:						Any   =						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.
    __snake_case:						Dict   =						[
        (o if sequence_ids[k] == context_index else None)
        for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
    ]

  return tokenized_examples


__UpperCAmelCase       :   int				       =							raw_datasets["validation"]
# Validation Feature Creation
__UpperCAmelCase       :   Dict				       =							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",
)

__UpperCAmelCase       :   Dict				       =							default_data_collator

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







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

  __snake_case:						Optional[Any]   =						[{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=SCREAMING_SNAKE_CASE__					,					label_ids=SCREAMING_SNAKE_CASE__)


__UpperCAmelCase       :   List[str]				       =							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 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
        return trt.volume(engine.get_binding_shape(SCREAMING_SNAKE_CASE__)) * engine.get_binding_dtype(SCREAMING_SNAKE_CASE__).itemsize

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

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

      # Create a stream in which to copy inputs/outputs and run inference.
      __UpperCAmelCase       :   Optional[int]				       =							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}')

      __UpperCAmelCase       :   Optional[Any]				       =							0.0
      __UpperCAmelCase       :   Optional[int]				       =							0
      __UpperCAmelCase       :   Any				       =							timeit.default_timer()

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

            __UpperCAmelCase       ,  __UpperCAmelCase       :   str				       =							outputs
            __UpperCAmelCase       :   Any				       =							torch.tensor(start_logits)
            __UpperCAmelCase       :   Tuple				       =							torch.tensor(end_logits)

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

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

      if all_preds is not None:
            __UpperCAmelCase       :   Union[str, Any]				       =							nested_truncate(all_preds, len(eval_dataset))

      __UpperCAmelCase       :   List[str]				       =							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 * 1_000 / niter))
      logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1_000))



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

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



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






