




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=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""",
)

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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	str:
  lowerCAmelCase__				:  str		       =     np.asarray(inputs['''input_ids''']	,      dtype=np.intaa      )
  lowerCAmelCase__				:  int		       =     np.asarray(inputs['''attention_mask''']	,      dtype=np.intaa      )
  lowerCAmelCase__				:  str		       =     np.asarray(inputs['''token_type_ids''']	,      dtype=np.intaa      )

  # Copy inputs
  cuda.memcpy_htod_async(d_inputs[0]	,      input_ids.ravel()	,      _lowercase      )
  cuda.memcpy_htod_async(d_inputs[1]	,      attention_mask.ravel()	,      _lowercase      )
  cuda.memcpy_htod_async(d_inputs[2]	,      token_type_ids.ravel()	,      _lowercase      )
  # start time
  lowerCAmelCase__				:  Dict		       =     time.time()
  # Run inference
  context.execute_async(
      bindings=[int(_lowercase      ) for d_inp in d_inputs] + [int(_lowercase      ), int(_lowercase      )]	,      stream_handle=stream.handle      )
  # Transfer predictions back from GPU
  cuda.memcpy_dtoh_async(_lowercase	,      _lowercase	,      _lowercase      )
  cuda.memcpy_dtoh_async(_lowercase	,      _lowercase	,      _lowercase      )
  # Synchronize the stream and take time
  stream.synchronize()
  # end time
  lowerCAmelCase__				:  Tuple		       =     time.time()
  lowerCAmelCase__				:  List[Any]		       =     end_time - start_time
  lowerCAmelCase__				:  List[str]		       =     (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      __lowerCAmelCase			(					UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  Tuple		       =     [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.
  lowerCAmelCase__				:  int		       =     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=_lowercase	,      stride=args.doc_stride	,      return_overflowing_tokens=_lowercase	,      return_offsets_mapping=_lowercase	,      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.
  lowerCAmelCase__				:  Optional[Any]		       =     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.
  lowerCAmelCase__				:  List[str]		       =     []

  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).
    lowerCAmelCase__				:  List[Any]		       =     tokenized_examples.sequence_ids(_lowercase      )
    lowerCAmelCase__				:  Dict		       =     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.
    lowerCAmelCase__				:  int		       =     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.
    lowerCAmelCase__				:  Dict		       =     [
        (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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase="eval"      )     ->	int:
  lowerCAmelCase__				:  str		       =     postprocess_qa_predictions(
      examples=_lowercase	,      features=_lowercase	,      predictions=_lowercase	,      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=_lowercase	,      )
  # Format the result to the format the metric expects.
  if args.version_2_with_negative:
    lowerCAmelCase__				:  Any		       =     [
        {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
    ]
  else:
    lowerCAmelCase__				:  Any		       =     [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

  lowerCAmelCase__				:  Optional[Any]		       =     [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=_lowercase	,      label_ids=_lowercase      )


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      __lowerCAmelCase			(					UpperCamelCase      )     ->	int:
       return trt.volume(engine.get_binding_shape(_lowercase      )      ) * engine.get_binding_dtype(_lowercase      ).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=-1_00)
          lowerCAmelCase_					    = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

          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=-1_00)

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


     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}""")







def      __lowerCAmelCase			(					UpperCamelCase      )     ->	None:
  lowerCAmelCase__				:  Dict		       =     generate_pascal_triangle(UpperCamelCase      )
  for row_idx in range(UpperCamelCase      ):
    # Print left spaces
    for _ in range(num_rows - row_idx - 1      ):
      print(end=''' '''      )
    # Print row values
    for col_idx in range(row_idx + 1      ):
      if col_idx != row_idx:
        print(triangle[row_idx][col_idx]	,      end=''' '''      )
      else:
        print(triangle[row_idx][col_idx]	,      end=''''''      )
    print()



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	list[list[int]]:

  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    raise TypeError('''The input value of \'num_rows\' should be \'int\''''      )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        '''The input value of \'num_rows\' should be greater than or equal to 0'''      )

  lowerCAmelCase__				:  list[list[int]]		       =     []
  for current_row_idx in range(UpperCamelCase      ):
    lowerCAmelCase__				:  List[str]		       =     populate_current_row(UpperCamelCase	,      UpperCamelCase      )
    triangle.append(UpperCamelCase      )
  return triangle



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	list[int]:
  lowerCAmelCase__				:  Optional[Any]		       =     [-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     1, 1
  for current_col_idx in range(1	,      UpperCamelCase      ):
    calculate_current_element(
        UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )
  return current_row



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      )     ->	None:
  lowerCAmelCase__				:  Optional[int]		       =     triangle[current_row_idx - 1][current_col_idx - 1]
  lowerCAmelCase__				:  List[str]		       =     triangle[current_row_idx - 1][current_col_idx]
  lowerCAmelCase__				:  List[Any]		       =     above_to_left_elt + above_to_right_elt



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	list[list[int]]:

  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    raise TypeError('''The input value of \'num_rows\' should be \'int\''''      )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        '''The input value of \'num_rows\' should be greater than or equal to 0'''      )

  lowerCAmelCase__				:  list[list[int]]		       =     [[1]]

  for row_index in range(1	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[int]		       =     [0] + result[-1] + [0]
    lowerCAmelCase__				:  List[str]		       =     row_index + 1
    # Calculate the number of distinct elements in a row
    lowerCAmelCase__				:  Tuple		       =     sum(divmod(UpperCamelCase	,      2      )      )
    lowerCAmelCase__				:  Tuple		       =     [
        temp_row[i - 1] + temp_row[i] for i in range(1	,      distinct_elements + 1      )
    ]
    lowerCAmelCase__				:  int		       =     row_first_half[: (row_index + 1) // 2]
    row_second_half.reverse()
    lowerCAmelCase__				:  Dict		       =     row_first_half + row_second_half
    result.append(UpperCamelCase      )

  return result



def      __lowerCAmelCase			(					)     ->	None:
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(UpperCamelCase	,      UpperCamelCase      ) -> None:
    lowerCAmelCase__				:  Tuple		       =     F"""{func.__name__}({value})"""
    lowerCAmelCase__				:  Dict		       =     timeit(F"""__main__.{call}"""	,      setup='''import __main__'''      )
    # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
    print(F"""{call:38} -- {timing:.4f} seconds"""      )

  for value in range(15      ):  # (1, 7, 14):
    for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
      benchmark_a_function(UpperCamelCase	,      UpperCamelCase      )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

