"""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


lowerCAmelCase__						:						Dict        =			trt.Logger(trt.Logger.WARNING)
lowerCAmelCase__						:						Optional[int]        =			absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

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

lowerCAmelCase__						:						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=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',
)

lowerCAmelCase__						:						Optional[Any]        =			parser.parse_args()

if args.tokenizer_name:
						lowerCAmelCase__						:						str        =			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)

lowerCAmelCase__						:						Dict        =			args.per_device_eval_batch_size

lowerCAmelCase__						:						Optional[int]        =			(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCAmelCase__						:						int        =			True

lowerCAmelCase__						:						Tuple        =			'temp_engine/bert-fp32.engine'
if args.fpaa:
						lowerCAmelCase__						:						int        =			'temp_engine/bert-fp16.engine'
if args.inta:
						lowerCAmelCase__						:						Any        =			'temp_engine/bert-int8.engine'

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

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

						with builder.create_builder_config() as config:
												lowerCAmelCase__						:						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)
												lowerCAmelCase__						:						Tuple        =			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)
												lowerCAmelCase__						:						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 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				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()      ,    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__    =							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__    =							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.
lowerCAmelCase__						:						List[str]        =			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.
						lowerCAmelCase__						:						Tuple        =			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.

lowerCAmelCase__						:						Tuple        =			raw_datasets['validation'].column_names

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

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

lowerCAmelCase__						:						Dict        =			min(args.max_seq_length, tokenizer.model_max_length)



def 						a_		(      lowerCamelCase ):
				# 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__    =							[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=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__    =							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(lowerCamelCase )
								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


lowerCAmelCase__						:						Optional[int]        =			raw_datasets['validation']
# Validation Feature Creation
lowerCAmelCase__						:						Tuple        =			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',
)

lowerCAmelCase__						:						str        =			default_data_collator

lowerCAmelCase__						:						Union[str, Any]        =			eval_dataset.remove_columns(['example_id', 'offset_mapping'])
lowerCAmelCase__						:						Dict        =			DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase="eval" ):
				# Post-processing: we match the start logits and end logits to answers in the original context.
				UpperCAmelCase__    =							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__    =							[
								    {'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=lowerCamelCase      ,    label_ids=lowerCamelCase )


lowerCAmelCase__						:						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 						a_		(      lowerCamelCase ):
										return trt.volume(engine.get_binding_shape(lowerCamelCase ) ) * engine.get_binding_dtype(lowerCamelCase ).itemsize

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

						# Allocate output buffer
						lowerCAmelCase__						:						Dict        =			cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
						lowerCAmelCase__						:						Dict        =			cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
						lowerCAmelCase__						:						Any        =			cuda.mem_alloc(h_outputa.nbytes)
						lowerCAmelCase__						:						str        =			cuda.mem_alloc(h_outputa.nbytes)

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

						lowerCAmelCase__						:						Any        =			0.0
						lowerCAmelCase__						:						Optional[Any]        =			0
						lowerCAmelCase__						:						List[Any]        =			timeit.default_timer()

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

												lowerCAmelCase__						:						List[Any]        =			outputs
												lowerCAmelCase__						:						Optional[int]        =			torch.tensor(start_logits)
												lowerCAmelCase__						:						List[str]        =			torch.tensor(end_logits)

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

												lowerCAmelCase__						:						Union[str, Any]        =			(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
												lowerCAmelCase__						:						Union[str, Any]        =			logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

						lowerCAmelCase__						:						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 * 1_000 / niter))
						logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_000))







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

lowerCAmelCase__						:						Tuple        =			post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCAmelCase__						:						Dict        =			metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







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




"""simple docstring"""






lowerCAmelCase__						:						Tuple		  =			range(2, 20 + 1)
lowerCAmelCase__						:						Optional[Any]		  =			[10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__						:						dict[int, dict[int, list[list[int]]]]		  =			{}



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							sum(a_i[j] for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ) )
				UpperCAmelCase__    =							sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase )      ,    lowerCamelCase ) ) )

				UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0
				UpperCAmelCase__    =							n - i

				UpperCAmelCase__    =							memo.get(lowerCamelCase )

				if sub_memo is not None:
								UpperCAmelCase__    =							sub_memo.get(lowerCamelCase )

								if jumps is not None and len(lowerCamelCase ) > 0:
												# find and make the largest jump without going over
												UpperCAmelCase__    =							-1
												for _k in range(len(lowerCamelCase ) - 1      ,    -1      ,    -1 ):
																if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																				UpperCAmelCase__    =							_k
																				break

												if max_jump >= 0:
																UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							jumps[max_jump]
																# since the difference between jumps is cached, add c
																UpperCAmelCase__    =							diff + c
																for j in range(min(lowerCamelCase      ,    len(lowerCamelCase ) ) ):
																				UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
																if new_c > 0:
																				add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )

								else:
												UpperCAmelCase__    =							[]
				else:
								UpperCAmelCase__    =							{c: []}
								UpperCAmelCase__    =							sub_memo

				if dn >= max_dn or c + diff >= base[k]:
								return diff, dn

				if k > ks[0]:
								while True:
												# keep doing smaller jumps
												UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    k - 1      ,    i + dn      ,    lowerCamelCase )
												diff += _diff
												dn += terms_jumped

												if dn >= max_dn or c + diff >= base[k]:
																break
				else:
								# would be too small a jump, just compute sequential terms instead
								UpperCAmelCase__   ,			UpperCAmelCase__    =							compute(lowerCamelCase      ,    lowerCamelCase      ,    i + dn      ,    lowerCamelCase )
								diff += _diff
								dn += terms_jumped

				UpperCAmelCase__    =							sub_memo[c]

				# keep jumps sorted by # of terms skipped
				UpperCAmelCase__    =							0
				while j < len(lowerCamelCase ):
								if jumps[j][1] > dn:
												break
								j += 1

				# cache the jump for this value digitsum(b) and c
				sub_memo[c].insert(lowerCamelCase      ,    (diff, dn, k) )
				return (diff, dn)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				if i >= n:
								return 0, i
				if k > len(lowerCamelCase ):
								a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )

				# note: a_i -> b * 10^k + c
				# ds_b -> digitsum(b)
				# ds_c -> digitsum(c)
				UpperCAmelCase__    =							i
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0, 0
				for j in range(len(lowerCamelCase ) ):
								if j >= k:
												ds_b += a_i[j]
								else:
												ds_c += a_i[j]

				while i < n:
								i += 1
								UpperCAmelCase__    =							ds_c + ds_b
								diff += addend
								UpperCAmelCase__    =							0
								for j in range(lowerCamelCase ):
												UpperCAmelCase__    =							a_i[j] + addend
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )

												ds_c += a_i[j]

								if addend > 0:
												break

				if addend > 0:
								add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )
				return diff, i - start_i



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ):
								UpperCAmelCase__    =							digits[j] + addend
								if s >= 1_0:
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
												UpperCAmelCase__    =							addend // 1_0 + quotient
								else:
												UpperCAmelCase__    =							s
												UpperCAmelCase__    =							addend // 1_0

								if addend == 0:
												break

				while addend > 0:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
								digits.append(lowerCamelCase )



def 						a_		(      lowerCamelCase = 1_0**1_5 ):
				UpperCAmelCase__    =							[1]
				UpperCAmelCase__    =							1
				UpperCAmelCase__    =							0
				while True:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    2_0      ,    i + dn      ,    lowerCamelCase )
								dn += terms_jumped
								if dn == n - i:
												break

				UpperCAmelCase__    =							0
				for j in range(len(lowerCamelCase ) ):
								a_n += digits[j] * 1_0**j
				return a_n


if __name__ == "__main__":
						print(F"""{solution() = }""")



