




"""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


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

lowercase__			=		logging.getLogger(__name__)

lowercase__			=		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""",
)

lowercase__			=		parser.parse_args()

if args.tokenizer_name:
			lowercase__			=		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)

lowercase__			=		args.per_device_eval_batch_size

lowercase__			=		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowercase__			=		True

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

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

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

			with builder.create_builder_config() as config:
						lowercase__			=		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)
						lowercase__			=		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)
						lowercase__			=		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     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     List[str]       =       np.asarray(inputs['input_ids']				,       dtype=np.intaa						)
				_lowerCamelCase	:     List[str]       =       np.asarray(inputs['attention_mask']				,       dtype=np.intaa						)
				_lowerCamelCase	:     List[str]       =       np.asarray(inputs['token_type_ids']				,       dtype=np.intaa						)

				# Copy inputs
				cuda.memcpy_htod_async(d_inputs[0]				,       input_ids.ravel()				,       lowercase__						)
				cuda.memcpy_htod_async(d_inputs[1]				,       attention_mask.ravel()				,       lowercase__						)
				cuda.memcpy_htod_async(d_inputs[2]				,       token_type_ids.ravel()				,       lowercase__						)
				# start time
				_lowerCamelCase	:     Any       =       time.time()
				# Run inference
				context.execute_async(
				    bindings=[int(lowercase__						) for d_inp in d_inputs] + [int(lowercase__						), int(lowercase__						)]				,       stream_handle=stream.handle						)
				# Transfer predictions back from GPU
				cuda.memcpy_dtoh_async(lowercase__				,       lowercase__				,       lowercase__						)
				cuda.memcpy_dtoh_async(lowercase__				,       lowercase__				,       lowercase__						)
				# Synchronize the stream and take time
				stream.synchronize()
				# end time
				_lowerCamelCase	:     str       =       time.time()
				_lowerCamelCase	:     List[Any]       =       end_time - start_time
				_lowerCamelCase	:     Tuple       =       (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.
lowercase__			=		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.
			lowercase__			=		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.

lowercase__			=		raw_datasets["""validation"""].column_names

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

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

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







def 	_snake_case     (							lowercase__						):
				# 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
				_lowerCamelCase	:     Optional[int]       =       [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	:     Optional[int]       =       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=lowercase__				,       stride=args.doc_stride				,       return_overflowing_tokens=lowercase__				,       return_offsets_mapping=lowercase__				,       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	:     int       =       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	:     str       =       tokenized_examples.sequence_ids(lowercase__						)
								_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	:     Tuple       =       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	:     List[Any]       =       [
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples['offset_mapping'][i]						)
								]

				return tokenized_examples


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

lowercase__			=		default_data_collator

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







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__="eval"						):
				# Post-processing: we match the start logits and end logits to answers in the original context.
				_lowerCamelCase	:     List[Any]       =       postprocess_qa_predictions(
				    examples=lowercase__				,       features=lowercase__				,       predictions=lowercase__				,       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=lowercase__				,       )
				# Format the result to the format the metric expects.
				if args.version_2_with_negative:
								_lowerCamelCase	:     Tuple       =       [
								    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
								]
				else:
								_lowerCamelCase	:     Dict       =       [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

				_lowerCamelCase	:     Optional[Any]       =       [{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
				return EvalPrediction(predictions=lowercase__				,       label_ids=lowercase__						)


lowercase__			=		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     (							lowercase__						):
							return trt.volume(engine.get_binding_shape(lowercase__						)						) * engine.get_binding_dtype(lowercase__						).itemsize

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

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

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

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

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

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

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

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

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

			lowercase__			=		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)

lowercase__			=		post_processing_function(eval_examples, eval_dataset, all_preds)
lowercase__			=		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F"Evaluation metrics: {eval_metric}")





"""simple docstring"""






import operator as op







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     Dict       =       []
				_lowerCamelCase	:     List[str]       =       lambda lowercase__				,       lowercase__						: int(x / y						)  # noqa: E731 integer division operation
				_lowerCamelCase	:     Optional[int]       =       {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8						)				,       'Action'.center(12						)				,       'Stack'				,       sep=' | '						)
				print('-' * (30 + len(lowercase__						))						)

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowercase__						)  # append x to stack
												# output in tabular format
												print(x.rjust(8						)				,       ('push(' + x + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)
								else:
												_lowerCamelCase	:     int       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												_lowerCamelCase	:     Tuple       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + a + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												stack.append(
												    str(opr[x](int(lowercase__						)				,       int(lowercase__						)						)						)						)  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8						)				,       ('push(' + a + x + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '				,       )

				return int(stack[0]						)


if __name__ == "__main__":
			lowercase__			=		input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
			print("""\n\tResult = """, solve(Postfix))