






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			:					Union[str, Any]         =	trt.Logger(trt.Logger.WARNING)
lowerCamelCase			:					Dict         =	absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowerCamelCase			:					List[Any]         =	logging.getLogger(__name__)

lowerCamelCase			:					Dict         =	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			:					Optional[Any]         =	parser.parse_args()

if args.tokenizer_name:
	lowerCamelCase			:					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)

lowerCamelCase			:					List[Any]         =	args.per_device_eval_batch_size

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

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

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

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

lowerCamelCase			:					Optional[Any]         =	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			:					Optional[Any]         =	[network.get_input(i) for i in range(network.num_inputs)]
	lowerCamelCase			:					List[str]         =	[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

	with builder.create_builder_config() as config:
		lowerCamelCase			:					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)
		lowerCamelCase			:					int         =	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			:					Any         =	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      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
					__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.
lowerCamelCase			:					Dict         =	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			:					Optional[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.

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

lowerCamelCase			:					Optional[Any]         =	'''question''' if '''question''' in column_names else column_names[0]
lowerCamelCase			:					str         =	'''context''' if '''context''' in column_names else column_names[1]
lowerCamelCase			:					int         =	'''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowerCamelCase			:					Tuple         =	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			:					Any         =	min(args.max_seq_length, tokenizer.model_max_length)





def       __lowerCAmelCase      (						__snake_case       ):
					__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


lowerCamelCase			:					Optional[int]         =	raw_datasets['''validation''']
# Validation Feature Creation
lowerCamelCase			:					Tuple         =	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			:					List[Any]         =	default_data_collator

lowerCamelCase			:					str         =	eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
lowerCamelCase			:					Dict         =	DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case="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__       )


lowerCamelCase			:					str         =	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      (						__snake_case       ):
						return trt.volume(engine.get_binding_shape(lowerCAmelCase__       )       ) * engine.get_binding_dtype(lowerCAmelCase__       ).itemsize

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

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

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

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

		lowerCamelCase      ,					lowerCamelCase			:					Tuple         =	outputs
		lowerCamelCase			:					str         =	torch.tensor(start_logits)
		lowerCamelCase			:					List[str]         =	torch.tensor(end_logits)

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

		lowerCamelCase			:					Tuple         =	(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
		lowerCamelCase			:					Optional[int]         =	logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

	lowerCamelCase			:					int         =	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			:					Union[str, Any]         =	post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCamelCase			:					str         =	metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



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














def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					len(__snake_case       )
     while cur > 1:
          # Find the maximum number in arr
          __lowerCAmelCase								=					arr.index(max(arr[0:cur]       )       )
          # Reverse from 0 to mi
          __lowerCAmelCase								=					arr[mi::-1] + arr[mi + 1 : len(__snake_case       )]
          # Reverse whole list
          __lowerCAmelCase								=					arr[cur - 1 :: -1] + arr[cur : len(__snake_case       )]
          cur -= 1
     return arr


if __name__ == "__main__":
 lowerCamelCase			:					List[str]         =	input('''Enter numbers separated by a comma:\n''').strip()
 lowerCamelCase			:					Tuple         =	[int(item) for item in user_input.split(''',''')]
 print(pancake_sort(unsorted))






