




'''simple docstring'''


import math
def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       0
							_a			  =       0
							while num > 0:
														_a			  =       num % 8
														_a			  =       octal + (remainder * math.floor(math.pow(10				,		UpperCamelCase   )   ))
														counter += 1
														_a			  =       math.floor(num / 8   )  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return f'0o{int(UpperCamelCase   )}'
def 	snake_case_					():





							'''simple docstring'''


							print('''\n2 in octal is:'''   )
							print(decimal_to_octal(2   )   )  # = 2
							print('''\n8 in octal is:'''   )
							print(decimal_to_octal(8   )   )  # = 10
							print('''\n65 in octal is:'''   )
							print(decimal_to_octal(65   )   )  # = 101
							print('''\n216 in octal is:'''   )
							print(decimal_to_octal(216   )   )  # = 330
							print('''\n512 in octal is:'''   )
							print(decimal_to_octal(512   )   )  # = 1000
							print('''\n'''   )


if __name__ == "__main__":
				main()







'''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


_snake_case   :   int     							=	trt.Logger(trt.Logger.WARNING)
_snake_case   :   List[Any]     							=	absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_snake_case   :   Optional[int]     							=	logging.getLogger(__name__)

_snake_case   :   Dict     							=	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',
)

_snake_case   :   Any     							=	parser.parse_args()

if args.tokenizer_name:
				_snake_case   :   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)

_snake_case   :   Any     							=	args.per_device_eval_batch_size

_snake_case   :   List[str]     							=	(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_snake_case   :   Any     							=	True

_snake_case   :   Optional[Any]     							=	'temp_engine/bert-fp32.engine'
if args.fpaa:
				_snake_case   :   Optional[int]     							=	'temp_engine/bert-fp16.engine'
if args.inta:
				_snake_case   :   Optional[Any]     							=	'temp_engine/bert-int8.engine'

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

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

				with builder.create_builder_config() as config:
								_snake_case   :   Union[str, 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)
								_snake_case   :   Optional[int]     							=	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)
								_snake_case   :   str     							=	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 	snake_case_					(UpperCamelCase :						Tuple				,		UpperCamelCase :						Dict				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						Any				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_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()				,		UpperCamelCase   )
							cuda.memcpy_htod_async(d_inputs[1]				,		attention_mask.ravel()				,		UpperCamelCase   )
							cuda.memcpy_htod_async(d_inputs[2]				,		token_type_ids.ravel()				,		UpperCamelCase   )
							# start time
							_a			  =       time.time()
							# Run inference
							context.execute_async(
							    bindings=[int(UpperCamelCase   ) for d_inp in d_inputs] + [int(UpperCamelCase   ), int(UpperCamelCase   )]				,		stream_handle=stream.handle   )
							# Transfer predictions back from GPU
							cuda.memcpy_dtoh_async(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							cuda.memcpy_dtoh_async(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							# 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.
_snake_case   :   Optional[int]     							=	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.
				_snake_case   :   int     							=	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.

_snake_case   :   Dict     							=	raw_datasets['validation'].column_names

_snake_case   :   Optional[Any]     							=	'question' if 'question' in column_names else column_names[0]
_snake_case   :   Optional[Any]     							=	'context' if 'context' in column_names else column_names[1]
_snake_case   :   Tuple     							=	'answers' if 'answers' in column_names else column_names[2]

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

_snake_case   :   int     							=	min(args.max_seq_length, tokenizer.model_max_length)
def 	snake_case_					(UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_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=UpperCamelCase				,		stride=args.doc_stride				,		return_overflowing_tokens=UpperCamelCase				,		return_offsets_mapping=UpperCamelCase				,		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(UpperCamelCase   )
														_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


_snake_case   :   List[Any]     							=	raw_datasets['validation']
# Validation Feature Creation
_snake_case   :   List[Any]     							=	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',
)

_snake_case   :   Optional[Any]     							=	default_data_collator

_snake_case   :   Any     							=	eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_snake_case   :   Any     							=	DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						str				,		UpperCamelCase :						Tuple="eval"   ):





							'''simple docstring'''


							_a			  =       postprocess_qa_predictions(
							    examples=UpperCamelCase				,		features=UpperCamelCase				,		predictions=UpperCamelCase				,		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=UpperCamelCase				,		)
							# 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=UpperCamelCase				,		label_ids=UpperCamelCase   )


_snake_case   :   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 	snake_case_					(UpperCamelCase :						Optional[int]   ):





											'''simple docstring'''


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

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

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

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

				_snake_case   :   List[str]     							=	0.0
				_snake_case   :   Union[str, Any]     							=	0
				_snake_case   :   List[Any]     							=	timeit.default_timer()

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

								_snake_case							,       _snake_case   :   Tuple     							=	outputs
								_snake_case   :   List[Any]     							=	torch.tensor(start_logits)
								_snake_case   :   List[str]     							=	torch.tensor(end_logits)

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

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

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

				_snake_case   :   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 * 1000 / niter))
				logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))

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

_snake_case   :   Union[str, Any]     							=	post_processing_function(eval_examples, eval_dataset, all_preds)
_snake_case   :   Dict     							=	metric.compute(predictions=prediction.predictions, references=prediction.label_ids)

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

