"""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


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

_a					:					Any=			logging.getLogger(__name__)

_a					:					Union[str, Any]=			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",
)

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

if args.tokenizer_name:
		_a					:					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)

_a					:					List[Any]=			args.per_device_eval_batch_size

_a					:					Tuple=			(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_a					:					str=			True

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

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

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

		with builder.create_builder_config() as config:
				_a					:					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)
				_a					:					List[Any]=			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)
				_a					:					int=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    Dict      ,							UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Union[str, Any]  )							->      Any:

	'''simple docstring'''





	__snake_case      :							List[Any]						=			np.asarray(inputs['input_ids']      ,							dtype=np.intaa  )
	__snake_case      :							str						=			np.asarray(inputs['attention_mask']      ,							dtype=np.intaa  )
	__snake_case      :							Union[str, Any]						=			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
	__snake_case      :							str						=			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
	__snake_case      :							str						=			time.time()
	__snake_case      :							int						=			end_time - start_time
	__snake_case      :							List[str]						=			(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.
_a					:					Tuple=			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.
		_a					:					Dict=			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.

_a					:					Union[str, Any]=			raw_datasets["validation"].column_names

_a					:					Any=			"question" if "question" in column_names else column_names[0]
_a					:					List[Any]=			"context" if "context" in column_names else column_names[1]
_a					:					Tuple=			"answers" if "answers" in column_names else column_names[2]

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

_a					:					str=			min(args.max_seq_length, tokenizer.model_max_length)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			[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.
	__snake_case      :							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=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.
	__snake_case      :							Optional[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.
	__snake_case      :							Optional[Any]						=			[]

	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).
		__snake_case      :							Optional[int]						=			tokenized_examples.sequence_ids(UpperCAmelCase_  )
		__snake_case      :							Tuple						=			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.
		__snake_case      :							List[str]						=			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.
		__snake_case      :							Optional[Any]						=			[
		    (o if sequence_ids[k] == context_index else None)
		    for k, o in enumerate(tokenized_examples['offset_mapping'][i]  )
		]

	return tokenized_examples


_a					:					List[Any]=			raw_datasets["validation"]
# Validation Feature Creation
_a					:					Dict=			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",
)

_a					:					Optional[int]=			default_data_collator

_a					:					Tuple=			eval_dataset.remove_columns(["example_id", "offset_mapping"])
_a					:					List[Any]=			DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    str="eval"  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							int						=			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:
		__snake_case      :							Dict						=			[
		    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
		]
	else:
		__snake_case      :							Optional[int]						=			[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

	__snake_case      :							Union[str, Any]						=			[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
	return EvalPrediction(predictions=UpperCAmelCase_      ,							label_ids=UpperCAmelCase_  )


_a					:					List[Any]=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]  )							->      int:

			'''simple docstring'''





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

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

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

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

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

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

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

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

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

		if all_preds is not None:
				_a					:					List[Any]=			nested_truncate(all_preds, len(eval_dataset))

		_a					:					Optional[Any]=			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)

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

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






"""simple docstring"""







def   __UpperCAmelCase    (   UpperCAmelCase_   :    list      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    int = 0      ,							UpperCAmelCase_   :    int = 0  )							->      int:

	'''simple docstring'''





	__snake_case      :							str						=			right or len(UpperCAmelCase_  ) - 1
	if left > right:
		return -1
	elif list_data[left] == key:
		return left
	elif list_data[right] == key:
		return right
	else:
		return search(UpperCAmelCase_      ,							UpperCAmelCase_      ,							left + 1      ,							right - 1  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





