



'''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


_UpperCamelCase					:  Optional[int]        =trt.Logger(trt.Logger.WARNING)
_UpperCamelCase					:  List[str]        =absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_UpperCamelCase					:  str        =logging.getLogger(__name__)

_UpperCamelCase					:  Optional[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=3_84,
    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=1_28,
    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",
)

_UpperCamelCase					:  List[Any]        =parser.parse_args()

if args.tokenizer_name:
				_UpperCamelCase					:  Optional[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)

_UpperCamelCase					:  Union[str, Any]        =args.per_device_eval_batch_size

_UpperCamelCase					:  str        =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_UpperCamelCase					:  List[Any]        =True

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

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

_UpperCamelCase					:  Optional[int]        =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
				_UpperCamelCase					:  Dict        =[network.get_input(i) for i in range(network.num_inputs)]
				_UpperCamelCase					:  Union[str, Any]        =[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								_UpperCamelCase					:  Optional[int]        =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)
								_UpperCamelCase					:  str        =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)
								_UpperCamelCase					:  Dict        =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  lowerCamelCase_					(       A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_				):
	__lowerCamelCase		       =		np.asarray(inputs['''input_ids''']    ,				dtype=np.intaa				)
	__lowerCamelCase		       =		np.asarray(inputs['''attention_mask''']    ,				dtype=np.intaa				)
	__lowerCamelCase		       =		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		       =		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		       =		time.time()
	__lowerCamelCase		       =		end_time - start_time
	__lowerCamelCase		       =		(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.
_UpperCamelCase					:  Optional[Any]        =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.
				_UpperCamelCase					:  Union[str, Any]        =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.

_UpperCamelCase					:  Optional[int]        =raw_datasets['validation'].column_names

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

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

_UpperCamelCase					:  Optional[Any]        =min(args.max_seq_length, tokenizer.model_max_length)

def  lowerCamelCase_					(       A_				):
	__lowerCamelCase		       =		[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		       =		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		       =		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		       =		[]

	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		       =		tokenized_examples.sequence_ids(lowerCamelCase_				)
		__lowerCamelCase		       =		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		       =		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		       =		[
		    (o if sequence_ids[k] == context_index else None)
		    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]				)
		]

	return tokenized_examples


_UpperCamelCase					:  List[Any]        =raw_datasets['validation']
# Validation Feature Creation
_UpperCamelCase					:  Optional[int]        =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",
)

_UpperCamelCase					:  Optional[int]        =default_data_collator

_UpperCamelCase					:  Union[str, Any]        =eval_dataset.remove_columns(["example_id", "offset_mapping"])
_UpperCamelCase					:  List[Any]        =DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def  lowerCamelCase_					(       A_    ,				A_    ,				A_    ,				A_="eval"				):
	__lowerCamelCase		       =		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		       =		[
		    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
		]
	else:
		__lowerCamelCase		       =		[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

	__lowerCamelCase		       =		[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
	return EvalPrediction(predictions=lowerCamelCase_    ,				label_ids=lowerCamelCase_				)


_UpperCamelCase					:  Dict        =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  lowerCamelCase_					(       A_				):
					return trt.volume(engine.get_binding_shape(lowerCamelCase_				)				) * engine.get_binding_dtype(lowerCamelCase_				).itemsize

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

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

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

				_UpperCamelCase					:  Tuple        =0.0
				_UpperCamelCase					:  int        =0
				_UpperCamelCase					:  int        =timeit.default_timer()

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

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

								# necessary to pad predictions and labels for being gathered
								_UpperCamelCase					:  List[str]        =accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
								_UpperCamelCase					:  Union[str, Any]        =accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

								_UpperCamelCase					:  Dict        =(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								_UpperCamelCase					:  Optional[int]        =logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

				_UpperCamelCase					:  Dict        =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 * 10_00 / niter))
				logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 10_00))







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

_UpperCamelCase					:  Any        =post_processing_function(eval_examples, eval_dataset, all_preds)
_UpperCamelCase					:  Any        =metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







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









from __future__ import annotations

from typing import Any






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if not postfix_notation:
								return 0

				lowercase__  					=     {'''+''', '''-''', '''*''', '''/'''}
				lowercase__  					=     []

				for token in postfix_notation:
								if token in operations:
												lowercase__					,		lowercase__  					=     stack.pop(), stack.pop()
												if token == "+":
																stack.append(a + b							)
												elif token == "-":
																stack.append(a - b							)
												elif token == "*":
																stack.append(a * b							)
												else:
																if a * b < 0 and a % b != 0:
																				stack.append(a // b + 1							)
																else:
																				stack.append(a // b							)
								else:
												stack.append(int(lowerCamelCase_							)							)

				return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




