






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


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

__UpperCAmelCase     							=						logging.getLogger(__name__)

__UpperCAmelCase     							=						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',
)

__UpperCAmelCase     							=						parser.parse_args()

if args.tokenizer_name:
		__UpperCAmelCase     							=						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)

__UpperCAmelCase     							=						args.per_device_eval_batch_size

__UpperCAmelCase     							=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__UpperCAmelCase     							=						True

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

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

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

		with builder.create_builder_config() as config:
				__UpperCAmelCase     							=						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)
				__UpperCAmelCase     							=						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)
				__UpperCAmelCase     							=						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 					lowercase__				(					__snake_case   :							List[Any]					,	__snake_case   :							List[str]					,	__snake_case   :							List[Any]					,	__snake_case   :							Dict					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							int					,	__snake_case   :							int					,	__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						np.asarray(inputs['input_ids']					,	dtype=np.intaa  )
					UpperCAmelCase_   :				Any						=						np.asarray(inputs['attention_mask']					,	dtype=np.intaa  )
					UpperCAmelCase_   :				List[str]						=						np.asarray(inputs['token_type_ids']					,	dtype=np.intaa  )

					# Copy inputs
					cuda.memcpy_htod_async(d_inputs[0]					,	input_ids.ravel()					,	__snake_case  )
					cuda.memcpy_htod_async(d_inputs[1]					,	attention_mask.ravel()					,	__snake_case  )
					cuda.memcpy_htod_async(d_inputs[2]					,	token_type_ids.ravel()					,	__snake_case  )
					# start time
					UpperCAmelCase_   :				List[str]						=						time.time()
					# Run inference
					context.execute_async(
					    bindings=[int(__snake_case  ) for d_inp in d_inputs] + [int(__snake_case  ), int(__snake_case  )]					,	stream_handle=stream.handle  )
					# Transfer predictions back from GPU
					cuda.memcpy_dtoh_async(__snake_case					,	__snake_case					,	__snake_case  )
					cuda.memcpy_dtoh_async(__snake_case					,	__snake_case					,	__snake_case  )
					# Synchronize the stream and take time
					stream.synchronize()
					# end time
					UpperCAmelCase_   :				Dict						=						time.time()
					UpperCAmelCase_   :				int						=						end_time - start_time
					UpperCAmelCase_   :				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.
__UpperCAmelCase     							=						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.
		__UpperCAmelCase     							=						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.

__UpperCAmelCase     							=						raw_datasets['validation'].column_names

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

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

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




def 					lowercase__				(					__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


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

					return tokenized_examples


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

__UpperCAmelCase     							=						default_data_collator

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




def 					lowercase__				(					__snake_case   :							Any					,	__snake_case   :							Any					,	__snake_case   :							Tuple					,	__snake_case   :							Optional[Any]="eval"  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[str]						=						postprocess_qa_predictions(
					    examples=__snake_case					,	features=__snake_case					,	predictions=__snake_case					,	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=__snake_case					,	)
					# Format the result to the format the metric expects.
					if args.version_2_with_negative:
										UpperCAmelCase_   :				Optional[Any]						=						[
										    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
										]
					else:
										UpperCAmelCase_   :				str						=						[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


__UpperCAmelCase     							=						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 					lowercase__				(					__snake_case   :							Union[str, Any]  ):


							'''simple docstring'''


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

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

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

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

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

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

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

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

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

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

		__UpperCAmelCase     							=						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)

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






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














def 					lowercase__				(					__snake_case   :							Tuple					,	__snake_case   :							List[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						''
					for i in table:
										res += inp[i - 1]
					return res




def 					lowercase__				(					__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					return data[1:] + data[0]




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						''
					for i in range(len(__snake_case  )  ):
										if a[i] == b[i]:
															res += "0"
										else:
															res += "1"
					return res




def 					lowercase__				(					__snake_case   :							Dict					,	__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						int('0b' + data[0] + data[-1]					,	2  )
					UpperCAmelCase_   :				Any						=						int('0b' + data[1:3]					,	2  )
					return bin(s[row][col]  )[2:]




def 					lowercase__				(					__snake_case   :							Tuple					,	__snake_case   :							List[Any]					,	__snake_case   :							List[Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						message[:4]
					UpperCAmelCase_   :				Optional[int]						=						message[4:]
					UpperCAmelCase_   :				Union[str, Any]						=						apply_table(__snake_case					,	__snake_case  )
					UpperCAmelCase_   :				Union[str, Any]						=						xor(__snake_case					,	__snake_case  )
					UpperCAmelCase_   :				Dict						=						apply_sbox(__snake_case					,	temp[:4]  )  # noqa: E741
					UpperCAmelCase_   :				Any						=						apply_sbox(__snake_case					,	temp[4:]  )
					UpperCAmelCase_   :				str						=						'0' * (2 - len(__snake_case  )) + l  # noqa: E741
					UpperCAmelCase_   :				int						=						'0' * (2 - len(__snake_case  )) + r
					UpperCAmelCase_   :				int						=						apply_table(l + r					,	__snake_case  )
					UpperCAmelCase_   :				int						=						xor(__snake_case					,	__snake_case  )
					return temp + right


if __name__ == "__main__":
		__UpperCAmelCase     							=						input('Enter 10 bit key: ')
		__UpperCAmelCase     							=						input('Enter 8 bit message: ')

		__UpperCAmelCase     							=						[6, 3, 7, 4, 8, 5, 10, 9]
		__UpperCAmelCase     							=						[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
		__UpperCAmelCase     							=						[2, 4, 3, 1]
		__UpperCAmelCase     							=						[2, 6, 3, 1, 4, 8, 5, 7]
		__UpperCAmelCase     							=						[4, 1, 3, 5, 7, 2, 8, 6]
		__UpperCAmelCase     							=						[4, 1, 2, 3, 2, 3, 4, 1]
		__UpperCAmelCase     							=						[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
		__UpperCAmelCase     							=						[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

		# key generation
		__UpperCAmelCase     							=						apply_table(key, paa_table)
		__UpperCAmelCase     							=						temp[:5]
		__UpperCAmelCase     							=						temp[5:]
		__UpperCAmelCase     							=						left_shift(left)
		__UpperCAmelCase     							=						left_shift(right)
		__UpperCAmelCase     							=						apply_table(left + right, pa_table)
		__UpperCAmelCase     							=						left_shift(left)
		__UpperCAmelCase     							=						left_shift(right)
		__UpperCAmelCase     							=						left_shift(left)
		__UpperCAmelCase     							=						left_shift(right)
		__UpperCAmelCase     							=						apply_table(left + right, pa_table)

		# encryption
		__UpperCAmelCase     							=						apply_table(message, IP)
		__UpperCAmelCase     							=						function(expansion, sa, sa, keya, temp)
		__UpperCAmelCase     							=						temp[4:] + temp[:4]
		__UpperCAmelCase     							=						function(expansion, sa, sa, keya, temp)
		__UpperCAmelCase     							=						apply_table(temp, IP_inv)
		print('Cipher text is:', CT)

		# decryption
		__UpperCAmelCase     							=						apply_table(CT, IP)
		__UpperCAmelCase     							=						function(expansion, sa, sa, keya, temp)
		__UpperCAmelCase     							=						temp[4:] + temp[:4]
		__UpperCAmelCase     							=						function(expansion, sa, sa, keya, temp)
		__UpperCAmelCase     							=						apply_table(temp, IP_inv)
		print('Plain text after decypting is:', PT)






