





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


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

_lowerCamelCase   				=			logging.getLogger(__name__)

_lowerCamelCase   				=			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',
)

_lowerCamelCase   				=			parser.parse_args()

if args.tokenizer_name:
	_lowerCamelCase   				=			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)

_lowerCamelCase   				=			args.per_device_eval_batch_size

_lowerCamelCase   				=			(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCamelCase   				=			True

_lowerCamelCase   				=			'''temp_engine/bert-fp32.engine'''
if args.fpaa:
	_lowerCamelCase   				=			'''temp_engine/bert-fp16.engine'''
if args.inta:
	_lowerCamelCase   				=			'''temp_engine/bert-int8.engine'''

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

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

	with builder.create_builder_config() as config:
		_lowerCamelCase   				=			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)
		_lowerCamelCase   				=			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)
		_lowerCamelCase   				=			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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Tuple    ):
		SCREAMING_SNAKE_CASE__			=      np.asarray(inputs["""input_ids"""]					,   dtype=np.intaa    )
		SCREAMING_SNAKE_CASE__			=      np.asarray(inputs["""attention_mask"""]					,   dtype=np.intaa    )
		SCREAMING_SNAKE_CASE__			=      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
		SCREAMING_SNAKE_CASE__			=      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
		SCREAMING_SNAKE_CASE__			=      time.time()
		SCREAMING_SNAKE_CASE__			=      end_time - start_time
		SCREAMING_SNAKE_CASE__			=      (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.
_lowerCamelCase   				=			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.
	_lowerCamelCase   				=			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.

_lowerCamelCase   				=			raw_datasets['''validation'''].column_names

_lowerCamelCase   				=			'''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase   				=			'''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase   				=			'''answers''' if '''answers''' in column_names else column_names[2]

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

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      [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.
		SCREAMING_SNAKE_CASE__			=      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.
		SCREAMING_SNAKE_CASE__			=      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.
		SCREAMING_SNAKE_CASE__			=      []

		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).
				SCREAMING_SNAKE_CASE__			=      tokenized_examples.sequence_ids(_lowercase    )
				SCREAMING_SNAKE_CASE__			=      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.
				SCREAMING_SNAKE_CASE__			=      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.
				SCREAMING_SNAKE_CASE__			=      [
				    (o if sequence_ids[k] == context_index else None)
				    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]    )
				]

		return tokenized_examples


_lowerCamelCase   				=			raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase   				=			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',
)

_lowerCamelCase   				=			default_data_collator

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							str					,   UpperCamelCase__:							int					,   UpperCamelCase__:							int="eval"    ):
		SCREAMING_SNAKE_CASE__			=      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:
				SCREAMING_SNAKE_CASE__			=      [
				    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
				]
		else:
				SCREAMING_SNAKE_CASE__			=      [{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


_lowerCamelCase   				=			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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
			return trt.volume(engine.get_binding_shape(_lowercase    )    ) * engine.get_binding_dtype(_lowercase    ).itemsize

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

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

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

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

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

		_lowerCamelCase   				=			outputs
		_lowerCamelCase   				=			torch.tensor(start_logits)
		_lowerCamelCase   				=			torch.tensor(end_logits)

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

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

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

	_lowerCamelCase   				=			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)

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

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for data in source_data:
				for i, el in enumerate(UpperCamelCase__    ):
						if len(UpperCamelCase__    ) < i + 1:
								data_lists.append([]    )
						data_lists[i].append(float(UpperCamelCase__    )    )
		return data_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for dlist, weight in zip(UpperCamelCase__					,   UpperCamelCase__    ):
				SCREAMING_SNAKE_CASE__			=      min(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      max(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))    )
								except ZeroDivisionError:
										score.append(1    )

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)    )
								except ZeroDivisionError:
										score.append(0    )

        # weight not 0 or 1
				else:
						SCREAMING_SNAKE_CASE__			=      f'''Invalid weight of {weight:f} provided'''
						raise ValueError(UpperCamelCase__    )

				score_lists.append(UpperCamelCase__    )

		return score_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      [0 for i in range(len(score_lists[0]    )    )]

		for slist in score_lists:
				for j, ele in enumerate(UpperCamelCase__    ):
						SCREAMING_SNAKE_CASE__			=      final_scores[j] + ele

		return final_scores






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      get_data(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      calculate_each_score(UpperCamelCase__					,   UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      generate_final_scores(UpperCamelCase__    )

		# append scores to source data
		for i, ele in enumerate(UpperCamelCase__    ):
				source_data[i].append(UpperCamelCase__    )

		return source_data