






"""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


A__					: Any=	trt.Logger(trt.Logger.WARNING)
A__					: Any=	absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A__					: Tuple=	logging.getLogger(__name__)

A__					: Optional[int]=	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=3_84,
    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=1_28,
    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""",
)

A__					: Tuple=	parser.parse_args()

if args.tokenizer_name:
  A__					: Optional[Any]=	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)

A__					: Dict=	args.per_device_eval_batch_size

A__					: Union[str, Any]=	(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A__					: List[Any]=	True

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

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

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

  with builder.create_builder_config() as config:
    A__					: str=	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)
    A__					: Union[str, 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)
    A__					: Optional[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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    np.asarray(inputs['input_ids'] ,	dtype=np.intaa					)
    UpperCamelCase__         =    np.asarray(inputs['attention_mask'] ,	dtype=np.intaa					)
    UpperCamelCase__         =    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
    UpperCamelCase__         =    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
    UpperCamelCase__         =    time.time()
    UpperCamelCase__         =    end_time - start_time
    UpperCamelCase__         =    (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.
A__					: Optional[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.
  A__					: 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.

A__					: Optional[int]=	raw_datasets["""validation"""].column_names

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

# Padding side determines if we do (question|context) or (context|question).
A__					: 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}."""
  )

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    UpperCamelCase__         =    [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.
    UpperCamelCase__         =    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.
    UpperCamelCase__         =    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.
    UpperCamelCase__         =    []

    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).
        UpperCamelCase__         =    tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    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.
        UpperCamelCase__         =    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.
        UpperCamelCase__         =    [
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples['offset_mapping'][i]					)
        ]

    return tokenized_examples


A__					: Optional[int]=	raw_datasets["""validation"""]
# Validation Feature Creation
A__					: Optional[int]=	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""",
)

A__					: List[str]=	default_data_collator

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE="eval"					)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    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:
        UpperCamelCase__         =    [
            {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
        ]
    else:
        UpperCamelCase__         =    [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

    UpperCamelCase__         =    [{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
    return EvalPrediction(predictions=SCREAMING_SNAKE_CASE ,	label_ids=SCREAMING_SNAKE_CASE					)


A__					: List[Any]=	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_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
      """simple docstring"""

      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.
  A__					: Union[str, Any]=	[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

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

  # Create a stream in which to copy inputs/outputs and run inference.
  A__					: 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}""")

  A__					: str=	0.0
  A__					: List[Any]=	0
  A__					: Dict=	timeit.default_timer()

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

    A__,     A__					: Union[str, Any]=	outputs
    A__					: Union[str, Any]=	torch.tensor(start_logits)
    A__					: Dict=	torch.tensor(end_logits)

    # necessary to pad predictions and labels for being gathered
    A__					: Any=	accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
    A__					: int=	accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

    A__					: int=	(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
    A__					: Dict=	logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

  A__					: 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 * 10_00 / niter))
  logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 10_00))





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

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





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












"""simple docstring"""





from copy import deepcopy
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None ,				snake_case_ = None      )		->       None:

       if arr is None and size is not None:
           UpperCamelCase__         =    size
           UpperCamelCase__         =    [0] * size
       elif arr is not None:
           self.init(snake_case_      )
       else:
           raise ValueError('Either arr or size must be specified'      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    deepcopy(snake_case_      )
       for i in range(1 ,				self.size      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               self.tree[j] += self.tree[i]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       list[int]:
       UpperCamelCase__         =    self.tree[:]
       for i in range(self.size - 1 ,				0 ,				-1      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               arr[j] -= arr[i]
       return arr

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index + (index & (-index))

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index - (index & (-index))

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if index == 0:
           self.tree[0] += value
           return
       while index < self.size:
           self.tree[index] += value
           UpperCamelCase__         =    self.next_(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       self.add(snake_case_ ,				value - self.get(snake_case_      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       if right == 0:
           return 0
       UpperCamelCase__         =    self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
           result += self.tree[right]
           UpperCamelCase__         =    self.prev(snake_case_      )
       return result

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       return self.prefix(snake_case_      ) - self.prefix(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       return self.query(snake_case_ ,				index + 1      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       value -= self.tree[0]
       if value < 0:
           return -1

       UpperCamelCase__         =    1  # Largest power of 2 <= size
       while j * 2 < self.size:
           j *= 2

       UpperCamelCase__         =    0

       while j > 0:
           if i + j < self.size and self.tree[i + j] <= value:
               value -= self.tree[i + j]
               i += j
           j //= 2
       return i


if __name__ == "__main__":
  import doctest

  doctest.testmod()




