"""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


__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=3_8_4,
    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_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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 << 5_0
              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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[int]:
   lowercase__:   Any					=      np.asarray(inputs['''input_ids''']       ,    dtype=np.intaa )
   lowercase__:   int					=      np.asarray(inputs['''attention_mask''']       ,    dtype=np.intaa )
   lowercase__:   Union[str, Any]					=      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
   lowercase__:   int					=      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
   lowercase__:   Any					=      time.time()
   lowercase__:   Dict					=      end_time - start_time
   lowercase__:   int					=      (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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[Any]:
   # 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
   lowercase__:   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.
   lowercase__:   str					=      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.
   lowercase__:   List[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.
   lowercase__:   Dict					=      []

   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).
      lowercase__:   int					=      tokenized_examples.sequence_ids(__UpperCAmelCase )
      lowercase__:   Optional[int]					=      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.
      lowercase__:   Union[str, Any]					=      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.
      lowercase__:   int					=      [
          (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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase="eval" )  ->  List[Any]:
   # Post-processing: we match the start logits and end logits to answers in the original context.
   lowercase__:   str					=      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:
      lowercase__:   int					=      [
          {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
      ]
   else:
      lowercase__:   Tuple					=      [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

   lowercase__:   Optional[int]					=      [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
   return EvalPrediction(predictions=__UpperCAmelCase       ,    label_ids=__UpperCAmelCase )


__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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[str]:
          return trt.volume(engine.get_binding_shape(__UpperCAmelCase ) ) * engine.get_binding_dtype(__UpperCAmelCase ).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=-1_0_0)
              __A			   = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

              __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=-1_0_0)

       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 * 1_0_0_0 / niter))
       logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1_0_0_0))




       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}''')

"""simple docstring"""






__A			   = [
    (1_0_0_0, "M"),
    (9_0_0, "CM"),
    (5_0_0, "D"),
    (4_0_0, "CD"),
    (1_0_0, "C"),
    (9_0, "XC"),
    (5_0, "L"),
    (4_0, "XL"),
    (1_0, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Dict					=      {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
   lowercase__:   List[str]					=      0
   lowercase__:   List[Any]					=      0
   while place < len(__UpperCAmelCase ):
      if (place + 1 < len(__UpperCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   lowercase__:   Optional[Any]					=      []
   for arabic, roman in ROMAN:
      ((lowercase__), (lowercase__)):   Tuple					=      divmod(__UpperCAmelCase       ,    __UpperCAmelCase )
      result.append(roman * factor )
      if number == 0:
         break
   return "".join(__UpperCAmelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
