



"""simple docstring"""





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

lowerCamelCase       	=						logging.getLogger(__name__)

lowerCamelCase       	=						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       	=						parser.parse_args()

if args.tokenizer_name:
  lowerCamelCase       	=						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       	=						args.per_device_eval_batch_size

lowerCamelCase       	=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCamelCase       	=						True

lowerCamelCase       	=						"""temp_engine/bert-fp32.engine"""
if args.fpaa:
  lowerCamelCase       	=						"""temp_engine/bert-fp16.engine"""
if args.inta:
  lowerCamelCase       	=						"""temp_engine/bert-int8.engine"""

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

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

  with builder.create_builder_config() as config:
    lowerCamelCase       	=						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       	=						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       	=						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        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
     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()     ,      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
     UpperCAmelCase_      			=							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
     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.
lowerCamelCase       	=						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       	=						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       	=						raw_datasets["""validation"""].column_names

lowerCamelCase       	=						"""question""" if """question""" in column_names else column_names[0]
lowerCamelCase       	=						"""context""" if """context""" in column_names else column_names[1]
lowerCamelCase       	=						"""answers""" if """answers""" in column_names else column_names[2]

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


lowerCamelCase       	=						raw_datasets["""validation"""]
# Validation Feature Creation
lowerCamelCase       	=						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       	=						default_data_collator

lowerCamelCase       	=						eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
lowerCamelCase       	=						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__="eval"				):
     # Post-processing: we match the start logits and end logits to answers in the original context.
     UpperCAmelCase_      			=							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:
          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=lowerCAmelCase__     ,      label_ids=lowerCAmelCase__				)


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

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

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

  # Create a stream in which to copy inputs/outputs and run inference.
  lowerCamelCase       	=						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       	=						0.0
  lowerCamelCase       	=						0
  lowerCamelCase       	=						timeit.default_timer()

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

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

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

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

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

  lowerCamelCase       	=						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 * 1_000 / niter))
  logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1_000))


  logger.info("""Total Number of Inference =  %d""", niter)

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


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









"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @slow
       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )







       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )




