





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 lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  Dict       ,      snake_case_	:  Tuple       ,      snake_case_	:  Any       ,      snake_case_	:  Tuple       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[Any]       )      ->				Optional[int]:
   __snake_case							       =   np.asarray(inputs['''input_ids''']       ,      dtype=np.intaa       )
   __snake_case							       =   np.asarray(inputs['''attention_mask''']       ,      dtype=np.intaa       )
   __snake_case							       =   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
   __snake_case							       =   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
   __snake_case							       =   time.time()
   __snake_case							       =   end_time - start_time
   __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.
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 lowerCamelCase__    (							snake_case_	:  Dict       )      ->				str:
   # Some of the questions have lots of whitespace on the left, which is not useful and will make the
   # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
   # left whitespace
   __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.
   __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=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.
   __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.
   __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).
      __snake_case							       =   tokenized_examples.sequence_ids(snake_case_       )
      __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.
      __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.
      __snake_case							       =   [
          (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 lowerCamelCase__    (							snake_case_	:  Tuple       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[Any]="eval"       )      ->				int:
   # Post-processing: we match the start logits and end logits to answers in the original context.
   __snake_case							       =   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:
      __snake_case							       =   [
          {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
      ]
   else:
      __snake_case							       =   [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

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


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 lowerCamelCase__    (							snake_case_	:  int       )      ->				List[Any]:
       return trt.volume(engine.get_binding_shape(snake_case_       )       ) * engine.get_binding_dtype(snake_case_       ).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}')








def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   if not isinstance(snake_case_       ,      snake_case_       ) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''       )

   __snake_case							       =   0
   while number:
      # This way we arrive at next set bit (next 1) instead of looping
      # through each bit and checking for 1s hence the
      # loop won't run 32 times it will only run the number of `1` times
      number &= number - 1
      count += 1
   return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()

