



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


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

_snake_case	=						logging.getLogger(__name__)

_snake_case	=						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",
)

_snake_case	=						parser.parse_args()

if args.tokenizer_name:
						_snake_case	=						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)

_snake_case	=						args.per_device_eval_batch_size

_snake_case	=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_snake_case	=						True

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

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

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

						with builder.create_builder_config() as config:
												_snake_case	=						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)
												_snake_case	=						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)
												_snake_case	=						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						):





							'''simple docstring'''




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

_snake_case	=						raw_datasets["validation"].column_names

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

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

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






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					[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  :							Union[str, 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=_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  :							List[Any]				     =					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  :							Optional[int]				     =					tokenized_examples.sequence_ids(_lowerCamelCase						)
														_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  :							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.
														_lowerCAmelCase  :							int				     =					[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples["offset_mapping"][i]						)
														]

							return tokenized_examples


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

_snake_case	=						default_data_collator

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase="eval"						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					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  :							Any				     =					[
														    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
														]
							else:
														_lowerCAmelCase  :							Any				     =					[{"id": k, "prediction_text": v} for k, v in predictions.items()]

							_lowerCAmelCase  :							int				     =					[{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
							return EvalPrediction(predictions=_lowerCamelCase       ,				label_ids=_lowerCamelCase						)


_snake_case	=						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						):





													'''simple docstring'''




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

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

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

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

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

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

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

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

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

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

						_snake_case	=						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)

_snake_case	=						post_processing_function(eval_examples, eval_dataset, all_preds)
_snake_case	=						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



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





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_snake_case	=						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

_snake_case	=						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

_snake_case	=						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,      _KWARGS_DESCRIPTION)
class 						UpperCAmelCase_					( datasets.Metric):







							def      snake_case__   (     self):

														'''simple docstring'''

														return datasets.MetricInfo(
														    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
														        {
														            "predictions": datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),
														            "references": datasets.Sequence(
														                datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),    id="references"),
														        }),    codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"],    reference_urls=[
														        "https://en.wikipedia.org/wiki/BLEU",
														        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
														    ],    )







							def      snake_case__   (     self,    __a,    __a,    __a=4,    __a=False):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					compute_bleu(
														    reference_corpus=__a,    translation_corpus=__a,    max_order=__a,    smooth=__a)
														((_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase))  :							Dict				     =					score
														return {
														    "bleu": bleu,
														    "precisions": precisions,
														    "brevity_penalty": bp,
														    "length_ratio": ratio,
														    "translation_length": translation_length,
														    "reference_length": reference_length,
														}
