



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

_snake_case	=						logging.getLogger(__name__)

_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",
)

_snake_case	=						parser.parse_args()

if args.tokenizer_name:
						_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)

_snake_case	=						args.per_device_eval_batch_size

_snake_case	=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_snake_case	=						True

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

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

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

						with builder.create_builder_config() as config:
												_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)
												_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)
												_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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					np.asarray(inputs["input_ids"]       ,				dtype=np.intaa						)
							_lowerCAmelCase  :							List[str]				     =					np.asarray(inputs["attention_mask"]       ,				dtype=np.intaa						)
							_lowerCAmelCase  :							Dict				     =					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
							_lowerCAmelCase  :							Any				     =					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
							_lowerCAmelCase  :							Union[str, Any]				     =					time.time()
							_lowerCAmelCase  :							Any				     =					end_time - start_time
							_lowerCAmelCase  :							List[Any]				     =					(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	=						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	=						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	=						raw_datasets["validation"].column_names

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

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

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






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




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

							return tokenized_examples


_snake_case	=						raw_datasets["validation"]
# Validation Feature Creation
_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",
)

_snake_case	=						default_data_collator

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase="eval"						):





							'''simple docstring'''




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

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


_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        A       (      _lowerCamelCase						):





													'''simple docstring'''




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

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

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

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

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

						_snake_case	=						None
						for step, batch in enumerate(eval_dataloader):
												_snake_case,		_snake_case	=						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	=						outputs
												_snake_case	=						torch.tensor(start_logits)
												_snake_case	=						torch.tensor(end_logits)

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

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

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

						_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)

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



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





def        A       (      _lowerCamelCase = 1_000_000						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					1
							_lowerCAmelCase  :							Optional[Any]				     =					1
							_lowerCAmelCase  :							List[str]				     =					{1: 1}

							for inputa in range(2       ,				_lowerCamelCase						):
														_lowerCAmelCase  :							int				     =					0
														_lowerCAmelCase  :							Any				     =					inputa

														while True:
																					if number in counters:
																												counter += counters[number]
																												break
																					if number % 2 == 0:
																												number //= 2
																												counter += 1
																					else:
																												_lowerCAmelCase  :							Any				     =					(3 * number) + 1
																												counter += 1

														if inputa not in counters:
																					_lowerCAmelCase  :							Tuple				     =					counter

														if counter > pre_counter:
																					_lowerCAmelCase  :							Union[str, Any]				     =					inputa
																					_lowerCAmelCase  :							Union[str, Any]				     =					counter
							return largest_number


if __name__ == "__main__":
						print(solution(int(input().strip())))
