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

_a  :  Any=				logging.getLogger(__name__)

_a  :  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",
)

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

if args.tokenizer_name:
	_a  :  Union[str, 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  :  Tuple=				args.per_device_eval_batch_size

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

# TRT Engine properties
_a  :  int=				True

_a  :  Dict=				"temp_engine/bert-fp32.engine"
if args.fpaa:
	_a  :  Optional[Any]=				"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  :  Tuple=				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  :  Optional[Any]=				[network.get_input(i) for i in range(network.num_inputs)]
	_a  :  Any=				[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

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

	'''simple docstring'''





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

_a  :  List[Any]=				raw_datasets["validation"].column_names

_a  :  Optional[Any]=				"question" if "question" in column_names else column_names[0]
_a  :  Optional[int]=				"context" if "context" in column_names else column_names[1]
_a  :  Union[str, Any]=				"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_a  :  Dict=				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  :  List[str]=				min(args.max_seq_length, tokenizer.model_max_length)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    Optional[int]:

	'''simple docstring'''





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

	return tokenized_examples


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

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				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    :							str   	=				[
		    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
		]
	else:
		__snake_case    :							List[str]   	=				[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


_a  :  Tuple=				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_					:       str						)							->    List[Any]:

		'''simple docstring'''





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

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

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

	# Create a stream in which to copy inputs/outputs and run inference.
	_a  :  List[str]=				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  :  Optional[int]=				0.0
	_a  :  Dict=				0
	_a  :  Any=				timeit.default_timer()

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

		_a,	_a  :  Union[str, Any]=				outputs
		_a  :  Optional[Any]=				torch.tensor(start_logits)
		_a  :  Tuple=				torch.tensor(end_logits)

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

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

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

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

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






"""simple docstring"""







import math

import tensorflow as tf
from packaging import version
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    Any:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)    ,							x.dtype						)						))

	return x * cdf
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Any   	=				tf.cast(math.pi    ,							x.dtype						)
	__snake_case    :							List[Any]   	=				tf.cast(0.044_715    ,							x.dtype						)
	__snake_case    :							Union[str, Any]   	=				0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(UpperCAmelCase_    ,							3						))						))

	return x * cdf
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				tf.convert_to_tensor(UpperCAmelCase_						)

	return x * tf.tanh(tf.math.softplus(UpperCAmelCase_						)						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    List[str]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				tf.cast(0.044_715    ,							x.dtype						)
	__snake_case    :							int   	=				tf.cast(0.7_978_845_608    ,							x.dtype						)

	return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Any   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				tf.cast(1.702    ,							x.dtype						)
	return x * tf.math.sigmoid(coeff * x						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    List[Any]:

	'''simple docstring'''





	return tf.clip_by_value(_gelu(UpperCAmelCase_						)    ,							-10    ,							10						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       Any=-1						)							->    int:

	'''simple docstring'''





	__snake_case	,       __snake_case    :							str   	=				tf.split(UpperCAmelCase_    ,							2    ,							axis=UpperCAmelCase_						)
	return a * tf.math.sigmoid(UpperCAmelCase_						)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):
	def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    int:

		'''simple docstring'''





		return tf.keras.activations.gelu(UpperCAmelCase_    ,							approximate=UpperCAmelCase_						)

	_a  :  Tuple=				tf.keras.activations.gelu

	_a  :  Tuple=				approximate_gelu_wrap
else:
	_a  :  Any=				_gelu
	_a  :  str=				_gelu_new


_a  :  List[str]=				{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    List[Any]:

	'''simple docstring'''





	if activation_string in ACTaFN:
		return ACTaFN[activation_string]
	else:
		raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"						)





