



def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 while a != 0:
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    str       =	b % a, a
 return b






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if gcd(__lowerCamelCase		,	__lowerCamelCase     ) != 1:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	F"""mod inverse of {a!r} and {m!r} does not exist"""
  raise ValueError(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	1, 0, a
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	0, 1, m
 while va != 0:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	ua // va
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
 return ua % m











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						    =  trt.Logger(trt.Logger.WARNING)
_lowerCamelCase						    =  absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_lowerCamelCase						    =  logging.getLogger(__name__)

_lowerCamelCase						    =  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						    =  parser.parse_args()

if args.tokenizer_name:
      _lowerCamelCase						    =  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						    =  args.per_device_eval_batch_size

_lowerCamelCase						    =  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCamelCase						    =  True

_lowerCamelCase						    =  """temp_engine/bert-fp32.engine"""
if args.fpaa:
      _lowerCamelCase						    =  """temp_engine/bert-fp16.engine"""
if args.inta:
      _lowerCamelCase						    =  """temp_engine/bert-int8.engine"""

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

_lowerCamelCase						    =  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						    =  [network.get_input(i) for i in range(network.num_inputs)]
      _lowerCamelCase						    =  [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

      with builder.create_builder_config() as config:
            _lowerCamelCase						    =  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						    =  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						    =  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 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	np.asarray(inputs["input_ids"]		,	dtype=np.intaa     )
 __SCREAMING_SNAKE_CASE       :    Any       =	np.asarray(inputs["attention_mask"]		,	dtype=np.intaa     )
 __SCREAMING_SNAKE_CASE       :    str       =	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
 __SCREAMING_SNAKE_CASE       :    str       =	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
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	time.time()
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	end_time - start_time
 __SCREAMING_SNAKE_CASE       :    Tuple       =	(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						    =  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						    =  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						    =  raw_datasets["""validation"""].column_names

_lowerCamelCase						    =  """question""" if """question""" in column_names else column_names[0]
_lowerCamelCase						    =  """context""" if """context""" in column_names else column_names[1]
_lowerCamelCase						    =  """answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase						    =  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						    =  min(args.max_seq_length, tokenizer.model_max_length)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[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.
 __SCREAMING_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=__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.
 __SCREAMING_SNAKE_CASE       :    Union[str, 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.
 __SCREAMING_SNAKE_CASE       :    List[str]       =	[]

 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).
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tokenized_examples.sequence_ids(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, 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.
  __SCREAMING_SNAKE_CASE       :    str       =	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.
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[
      (o if sequence_ids[k] == context_index else None)
      for k, o in enumerate(tokenized_examples["offset_mapping"][i]     )
  ]

 return tokenized_examples


_lowerCamelCase						    =  raw_datasets["""validation"""]
# Validation Feature Creation
_lowerCamelCase						    =  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						    =  default_data_collator

_lowerCamelCase						    =  eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
_lowerCamelCase						    =  DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int="eval"     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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:
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[
      {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
  ]
 else:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


_lowerCamelCase						    =  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 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]     ):



       """simple docstring"""





       return trt.volume(engine.get_binding_shape(__lowerCamelCase     )     ) * engine.get_binding_dtype(__lowerCamelCase     ).itemsize

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

      # Allocate output buffer
      _lowerCamelCase						    =  cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
      _lowerCamelCase						    =  cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
      _lowerCamelCase						    =  cuda.mem_alloc(h_outputa.nbytes)
      _lowerCamelCase						    =  cuda.mem_alloc(h_outputa.nbytes)

      # Create a stream in which to copy inputs/outputs and run inference.
      _lowerCamelCase						    =  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						    =  0.0
      _lowerCamelCase						    =  0
      _lowerCamelCase						    =  timeit.default_timer()

      _lowerCamelCase						    =  None
      for step, batch in enumerate(eval_dataloader):
            _lowerCamelCase    ,       _lowerCamelCase						    =  model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
            total_time += infer_time
            niter += 1

            _lowerCamelCase    ,       _lowerCamelCase						    =  outputs
            _lowerCamelCase						    =  torch.tensor(start_logits)
            _lowerCamelCase						    =  torch.tensor(end_logits)

            # necessary to pad predictions and labels for being gathered
            _lowerCamelCase						    =  accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
            _lowerCamelCase						    =  accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

            _lowerCamelCase						    =  (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
            _lowerCamelCase						    =  logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

      _lowerCamelCase						    =  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						    =  post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase						    =  metric.compute(predictions=prediction.predictions, references=prediction.label_ids)






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






