





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=3_84,
    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=1_28,
    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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=np.asarray(inputs["input_ids"]				,							dtype=np.intaa     )
   UpperCAmelCase__						=np.asarray(inputs["attention_mask"]				,							dtype=np.intaa     )
   UpperCAmelCase__						=np.asarray(inputs["token_type_ids"]				,							dtype=np.intaa     )

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




   '''simple docstring'''

   UpperCAmelCase__						=[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__						=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=A				,							stride=args.doc_stride				,							return_overflowing_tokens=A				,							return_offsets_mapping=A				,							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__						=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__						=[]

   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__						=tokenized_examples.sequence_ids(A     )
      UpperCAmelCase__						=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__						=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__						=[
          (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 				_UpperCAmelCase			(    A				,							A				,							A				,							A="eval"     ):




   '''simple docstring'''

   UpperCAmelCase__						=postprocess_qa_predictions(
       examples=A				,							features=A				,							predictions=A				,							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=A				,							)
   # Format the result to the format the metric expects.
   if args.version_2_with_negative:
      UpperCAmelCase__						=[
          {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
      ]
   else:
      UpperCAmelCase__						=[{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


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 				_UpperCAmelCase			(    A     ):




       '''simple docstring'''

       return trt.volume(engine.get_binding_shape(A     )     ) * engine.get_binding_dtype(A     ).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=-1_00)
        UpperCamelCase_												=	accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

        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=-1_00)

    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 * 10_00 / niter))
    logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 10_00))




    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}""")










from math import factorial






def 				_UpperCAmelCase			(    A = 20     ):




   '''simple docstring'''

   UpperCAmelCase__						=2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
   # 2, 3,...
   UpperCAmelCase__						=n // 2

   return int(factorial(A     ) / (factorial(A     ) * factorial(n - k     ))     )


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution(20))
    else:
        try:
            UpperCamelCase_												=	int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print('Invalid entry - please enter a number.')



