

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    bool:
				return numa ^ numa < 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()






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


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

SCREAMING_SNAKE_CASE_            = logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_            = 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',
)

SCREAMING_SNAKE_CASE_            = parser.parse_args()

if args.tokenizer_name:
					SCREAMING_SNAKE_CASE_            = 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)

SCREAMING_SNAKE_CASE_            = args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE_            = (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE_            = True

SCREAMING_SNAKE_CASE_            = 'temp_engine/bert-fp32.engine'
if args.fpaa:
					SCREAMING_SNAKE_CASE_            = 'temp_engine/bert-fp16.engine'
if args.inta:
					SCREAMING_SNAKE_CASE_            = 'temp_engine/bert-int8.engine'

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

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

					with builder.create_builder_config() as config:
										SCREAMING_SNAKE_CASE_            = 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)
										SCREAMING_SNAKE_CASE_            = 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)
										SCREAMING_SNAKE_CASE_            = 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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Any					, lowerCAmelCase:					str					, lowerCAmelCase:					str					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Dict					, lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						Dict							=      np.asarray(inputs["input_ids"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						List[str]							=      np.asarray(inputs["attention_mask"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						Union[str, Any]							=      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
				_UpperCAmelCase    :						Dict							=      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
				_UpperCAmelCase    :						Any							=      time.time()
				_UpperCAmelCase    :						Any							=      end_time - start_time
				_UpperCAmelCase    :						Optional[int]							=      (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.
SCREAMING_SNAKE_CASE_            = 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.
					SCREAMING_SNAKE_CASE_            = 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.

SCREAMING_SNAKE_CASE_            = raw_datasets['validation'].column_names

SCREAMING_SNAKE_CASE_            = 'question' if 'question' in column_names else column_names[0]
SCREAMING_SNAKE_CASE_            = 'context' if 'context' in column_names else column_names[1]
SCREAMING_SNAKE_CASE_            = 'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE_            = 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}.'''
					)

SCREAMING_SNAKE_CASE_            = min(args.max_seq_length, tokenizer.model_max_length)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Any:
				# 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
				_UpperCAmelCase    :						Optional[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.
				_UpperCAmelCase    :						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.
				_UpperCAmelCase    :						List[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.
				_UpperCAmelCase    :						Tuple							=      []

				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    :						Tuple							=      tokenized_examples.sequence_ids(lowerCAmelCase	)
								_UpperCAmelCase    :						Optional[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.
								_UpperCAmelCase    :						List[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.
								_UpperCAmelCase    :						Any							=      [
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples["offset_mapping"][i]	)
								]

				return tokenized_examples


SCREAMING_SNAKE_CASE_            = raw_datasets['validation']
# Validation Feature Creation
SCREAMING_SNAKE_CASE_            = 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',
)

SCREAMING_SNAKE_CASE_            = default_data_collator

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					Dict					, lowerCAmelCase:					str					, lowerCAmelCase:					Optional[Any]="eval"	)				->    Union[str, Any]:
				# Post-processing: we match the start logits and end logits to answers in the original context.
				_UpperCAmelCase    :						Tuple							=      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:
								_UpperCAmelCase    :						Optional[int]							=      [
								    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
								]
				else:
								_UpperCAmelCase    :						Optional[Any]							=      [{"id": k, "prediction_text": v} for k, v in predictions.items()]

				_UpperCAmelCase    :						Optional[int]							=      [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
				return EvalPrediction(predictions=lowerCAmelCase					, label_ids=lowerCAmelCase	)


SCREAMING_SNAKE_CASE_            = 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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    List[str]:
									return trt.volume(engine.get_binding_shape(lowerCAmelCase	)	) * engine.get_binding_dtype(lowerCAmelCase	).itemsize

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

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

					# Create a stream in which to copy inputs/outputs and run inference.
					SCREAMING_SNAKE_CASE_            = 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}''')

					SCREAMING_SNAKE_CASE_            = 0.0
					SCREAMING_SNAKE_CASE_            = 0
					SCREAMING_SNAKE_CASE_            = timeit.default_timer()

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

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

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

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

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

					SCREAMING_SNAKE_CASE_            = 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)

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





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


