






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

UpperCAmelCase__							  =						logging.getLogger(__name__)

UpperCAmelCase__							  =						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__							  =						parser.parse_args()

if args.tokenizer_name:
     UpperCAmelCase__							  =						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__							  =						args.per_device_eval_batch_size

UpperCAmelCase__							  =						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCAmelCase__							  =						True

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

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

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

     with builder.create_builder_config() as config:
          UpperCAmelCase__							  =						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__							  =						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__							  =						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       ( a					:str      ,				a					:List[str]      ,				a					:List[str]      ,				a					:Tuple      ,				a					:Optional[Any]      ,				a					:List[str]      ,				a					:Any      ,				a					:List[Any]					)				->     List[str]:
 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()      ,				a					)
 cuda.memcpy_htod_async(d_inputs[1]      ,				attention_mask.ravel()      ,				a					)
 cuda.memcpy_htod_async(d_inputs[2]      ,				token_type_ids.ravel()      ,				a					)
 # start time
 a						    = time.time()
 # Run inference
 context.execute_async(
     bindings=[int(a					) for d_inp in d_inputs] + [int(a					), int(a					)]      ,				stream_handle=stream.handle					)
 # Transfer predictions back from GPU
 cuda.memcpy_dtoh_async(a      ,				a      ,				a					)
 cuda.memcpy_dtoh_async(a      ,				a      ,				a					)
 # 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.
UpperCAmelCase__							  =						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__							  =						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__							  =						raw_datasets["validation"].column_names

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

# Padding side determines if we do (question|context) or (context|question).
UpperCAmelCase__							  =						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__							  =						min(args.max_seq_length, tokenizer.model_max_length)
def 		_a       ( a					:List[Any]					)				->     Dict:
 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=a      ,				stride=args.doc_stride      ,				return_overflowing_tokens=a      ,				return_offsets_mapping=a      ,				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(a					)
  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


UpperCAmelCase__							  =						raw_datasets["validation"]
# Validation Feature Creation
UpperCAmelCase__							  =						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__							  =						default_data_collator

UpperCAmelCase__							  =						eval_dataset.remove_columns(["example_id", "offset_mapping"])
UpperCAmelCase__							  =						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 		_a       ( a					:Dict      ,				a					:List[Any]      ,				a					:List[Any]      ,				a					:Dict="eval"					)				->     Optional[Any]:
 a						    = postprocess_qa_predictions(
     examples=a      ,				features=a      ,				predictions=a      ,				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=a      ,				)
 # 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=a      ,				label_ids=a					)


UpperCAmelCase__							  =						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       ( a					:Dict					)				->     Optional[int]:
      return trt.volume(engine.get_binding_shape(a					)					) * engine.get_binding_dtype(a					).itemsize

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

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

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

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

          UpperCAmelCase__							  =						outputs
          UpperCAmelCase__							  =						torch.tensor(start_logits)
          UpperCAmelCase__							  =						torch.tensor(end_logits)

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

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

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

     UpperCAmelCase__							  =						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)

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

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








def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			if not numbers:
						return 0

			if not isinstance(SCREAMING_SNAKE_CASE       ,    (list, tuple) ) or not all(
			    isinstance(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ) for number in numbers ):
						raise ValueError('numbers must be an iterable of integers' )

			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						numbers[0]

			for i in range(1       ,    len(SCREAMING_SNAKE_CASE ) ):
						# update the maximum and minimum subarray products
						SCREAMING_SNAKE_CASE_								=						numbers[i]
						if number < 0:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						min_till_now, max_till_now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    max_till_now * number )
						SCREAMING_SNAKE_CASE_								=						min(SCREAMING_SNAKE_CASE       ,    min_till_now * number )

						# update the maximum product found till now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			return max_prod
