


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


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

__a	 =      logging.getLogger(__name__)

__a	 =      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""",
)

__a	 =      parser.parse_args()

if args.tokenizer_name:
 __a	 =      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)

__a	 =      args.per_device_eval_batch_size

__a	 =      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__a	 =      True

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

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

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

 with builder.create_builder_config() as config:
  __a	 =      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)
  __a	 =      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)
  __a	 =      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					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Any:
   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.
__a	 =      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.
 __a	 =      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.

__a	 =      raw_datasets["""validation"""].column_names

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

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

__a	 =      min(args.max_seq_length, tokenizer.model_max_length)
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->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
   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


__a	 =      raw_datasets["""validation"""]
# Validation Feature Creation
__a	 =      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""",
)

__a	 =      default_data_collator

__a	 =      eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
__a	 =      DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_="eval" )    ->Tuple:
   # 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_ )


__a	 =      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					(    lowerCAmelCase_ )    ->List[Any]:
    return trt.volume(engine.get_binding_shape(lowerCAmelCase_ ) ) * engine.get_binding_dtype(lowerCAmelCase_ ).itemsize

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

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

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

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

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

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

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

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

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

 __a	 =      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)

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







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







def 	_UpperCamelCase					(    )    ->list[list[int]]:
   return [list(range(1_0_0_0 - i	,    -1_0_0_0 - i	,    -1 ) ) for i in range(1_0_0_0 )]


__a	 =      generate_large_matrix()
__a	 =      (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->None:
   assert all(row == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for row in grid )
   assert all(list(lowerCAmelCase_ ) == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for col in zip(*lowerCAmelCase_ ) )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(lowerCAmelCase_ ) - 1

   # Edge cases such as no values or all numbers are negative.
   if not array or array[0] < 0:
      return 0

   while right + 1 > left:
      UpperCAmelCase      					=  (left + right) // 2
      UpperCAmelCase      					=  array[mid]

      # Num must be negative and the index must be greater than or equal to 0.
      if num < 0 and array[mid - 1] >= 0:
         return mid

      if num >= 0:
         UpperCAmelCase      					=  mid + 1
      else:
         UpperCAmelCase      					=  mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
   return len(lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(grid[0] )

   for i in range(len(lowerCAmelCase_ ) ):
      UpperCAmelCase      					=  find_negative_index(grid[i][:bound] )
      total += bound
   return (len(lowerCAmelCase_ ) * len(grid[0] )) - total
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   return len([number for row in grid for number in row if number < 0] )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   for row in grid:
      for i, number in enumerate(lowerCAmelCase_ ):
         if number < 0:
            total += len(lowerCAmelCase_ ) - i
            break
   return total
def 	_UpperCamelCase					(    )    ->None:
   from timeit import timeit

   print("""Running benchmarks""" )
   UpperCAmelCase      					=  (
       """from __main__ import count_negatives_binary_search, """
       """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
   )
   for func in (
       "count_negatives_binary_search",  # took 0.7727 seconds
       "count_negatives_brute_force_with_break",  # took 4.6505 seconds
       "count_negatives_brute_force",  # took 12.8160 seconds
   ):
      UpperCAmelCase      					=  timeit(F"""{func}(grid=grid)"""	,    setup=lowerCAmelCase_	,    number=5_0_0 )
      print(F"""{func}() took {time:0.4f} seconds""" )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()



