






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


__magic_name__   :  Any        =							trt.Logger(trt.Logger.WARNING)
__magic_name__   :  int        =							absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

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

__magic_name__   :  Union[str, Any]        =							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''',
)

__magic_name__   :  Tuple        =							parser.parse_args()

if args.tokenizer_name:
       __magic_name__   :  Dict        =							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)

__magic_name__   :  List[str]        =							args.per_device_eval_batch_size

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

# TRT Engine properties
__magic_name__   :  Union[str, Any]        =							True

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

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

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

       with builder.create_builder_config() as config:
              __magic_name__   :  Optional[Any]        =							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)
              __magic_name__   :  List[Any]        =							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)
              __magic_name__   :  Optional[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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			List[Any]:
      """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()       ,							_UpperCamelCase)
      cuda.memcpy_htod_async(d_inputs[1]       ,							attention_mask.ravel()       ,							_UpperCamelCase)
      cuda.memcpy_htod_async(d_inputs[2]       ,							token_type_ids.ravel()       ,							_UpperCamelCase)
      # start time
      UpperCamelCase					       =  time.time()
      # Run inference
      context.execute_async(
          bindings=[int(_UpperCamelCase) for d_inp in d_inputs] + [int(_UpperCamelCase), int(_UpperCamelCase)]       ,							stream_handle=stream.handle)
      # Transfer predictions back from GPU
      cuda.memcpy_dtoh_async(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
      cuda.memcpy_dtoh_async(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
      # 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.
__magic_name__   :  Tuple        =							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.
       __magic_name__   :  str        =							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.

__magic_name__   :  Union[str, Any]        =							raw_datasets['''validation'''].column_names

__magic_name__   :  str        =							'''question''' if '''question''' in column_names else column_names[0]
__magic_name__   :  List[str]        =							'''context''' if '''context''' in column_names else column_names[1]
__magic_name__   :  Tuple        =							'''answers''' if '''answers''' in column_names else column_names[2]

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

__magic_name__   :  List[Any]        =							min(args.max_seq_length, tokenizer.model_max_length)
def 		lowercase__  (				_UpperCamelCase) ->			Optional[int]:
      """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=_UpperCamelCase       ,							stride=args.doc_stride       ,							return_overflowing_tokens=_UpperCamelCase       ,							return_offsets_mapping=_UpperCamelCase       ,							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(_UpperCamelCase)
            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


__magic_name__   :  Dict        =							raw_datasets['''validation''']
# Validation Feature Creation
__magic_name__   :  List[str]        =							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''',
)

__magic_name__   :  List[Any]        =							default_data_collator

__magic_name__   :  Optional[Any]        =							eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
__magic_name__   :  Dict        =							DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase="eval") ->			str:
      """simple docstring"""

      UpperCamelCase					       =  postprocess_qa_predictions(
          examples=_UpperCamelCase       ,							features=_UpperCamelCase       ,							predictions=_UpperCamelCase       ,							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=_UpperCamelCase       ,							)
      # 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=_UpperCamelCase       ,							label_ids=_UpperCamelCase)


__magic_name__   :  Optional[Any]        =							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__  (				_UpperCamelCase) ->			Optional[int]:
             """simple docstring"""

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

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

       # Allocate output buffer
       __magic_name__   :  Any        =							cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
       __magic_name__   :  Union[str, Any]        =							cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
       __magic_name__   :  Union[str, Any]        =							cuda.mem_alloc(h_outputa.nbytes)
       __magic_name__   :  int        =							cuda.mem_alloc(h_outputa.nbytes)

       # Create a stream in which to copy inputs/outputs and run inference.
       __magic_name__   :  List[str]        =							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}')

       __magic_name__   :  Dict        =							0.0
       __magic_name__   :  Optional[int]        =							0
       __magic_name__   :  Optional[Any]        =							timeit.default_timer()

       __magic_name__   :  str        =							None
       for step, batch in enumerate(eval_dataloader):
              __magic_name__     ,   __magic_name__   :  Optional[int]        =							model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
              total_time += infer_time
              niter += 1

              __magic_name__     ,   __magic_name__   :  Union[str, Any]        =							outputs
              __magic_name__   :  Optional[int]        =							torch.tensor(start_logits)
              __magic_name__   :  int        =							torch.tensor(end_logits)

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

              __magic_name__   :  List[str]        =							(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
              __magic_name__   :  Union[str, Any]        =							logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

       if all_preds is not None:
              __magic_name__   :  Optional[Any]        =							nested_truncate(all_preds, len(eval_dataset))

       __magic_name__   :  str        =							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)

__magic_name__   :  Dict        =							post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__   :  int        =							metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F'Evaluation metrics: {eval_metric}')











def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			bool:
      """simple docstring"""

      return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()



