





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							:			Dict         =			trt.Logger(trt.Logger.WARNING)
__A							:			Dict         =			absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__A							:			List[str]         =			logging.getLogger(__name__)

__A							:			str         =			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							:			Tuple         =			parser.parse_args()

if args.tokenizer_name:
 __A							:			List[str]         =			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							:			Any         =			args.per_device_eval_batch_size

__A							:			Tuple         =			(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__A							:			str         =			True

__A							:			List[str]         =			"temp_engine/bert-fp32.engine"
if args.fpaa:
 __A							:			Union[str, Any]         =			"temp_engine/bert-fp16.engine"
if args.inta:
 __A							:			int         =			"temp_engine/bert-int8.engine"

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

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

 with builder.create_builder_config() as config:
  __A							:			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)
  __A							:			int         =			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							:			str         =			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(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    _A     =							np.asarray(inputs['input_ids']							,     dtype=np.intaa				)
    _A     =							np.asarray(inputs['attention_mask']							,     dtype=np.intaa				)
    _A     =							np.asarray(inputs['token_type_ids']							,     dtype=np.intaa				)

    # Copy inputs
    cuda.memcpy_htod_async(d_inputs[0]							,     input_ids.ravel()							,     _SCREAMING_SNAKE_CASE				)
    cuda.memcpy_htod_async(d_inputs[1]							,     attention_mask.ravel()							,     _SCREAMING_SNAKE_CASE				)
    cuda.memcpy_htod_async(d_inputs[2]							,     token_type_ids.ravel()							,     _SCREAMING_SNAKE_CASE				)
    # start time
    _A     =							time.time()
    # Run inference
    context.execute_async(
        bindings=[int(_SCREAMING_SNAKE_CASE				) for d_inp in d_inputs] + [int(_SCREAMING_SNAKE_CASE				), int(_SCREAMING_SNAKE_CASE				)]							,     stream_handle=stream.handle				)
    # Transfer predictions back from GPU
    cuda.memcpy_dtoh_async(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    cuda.memcpy_dtoh_async(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    # Synchronize the stream and take time
    stream.synchronize()
    # end time
    _A     =							time.time()
    _A     =							end_time - start_time
    _A     =							(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							:			Union[str, Any]         =			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							:			List[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.

__A							:			Any         =			raw_datasets["validation"].column_names

__A							:			Any         =			"question" if "question" in column_names else column_names[0]
__A							:			List[Any]         =			"context" if "context" in column_names else column_names[1]
__A							:			List[str]         =			"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__A							:			Dict         =			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							:			Union[str, Any]         =			min(args.max_seq_length, tokenizer.model_max_length)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    _A     =							[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.
    _A     =							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=_SCREAMING_SNAKE_CASE							,     stride=args.doc_stride							,     return_overflowing_tokens=_SCREAMING_SNAKE_CASE							,     return_offsets_mapping=_SCREAMING_SNAKE_CASE							,     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.
    _A     =							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.
    _A     =							[]

    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).
        _A     =							tokenized_examples.sequence_ids(_SCREAMING_SNAKE_CASE				)
        _A     =							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.
        _A     =							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.
        _A     =							[
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples['offset_mapping'][i]				)
        ]

    return tokenized_examples


__A							:			List[str]         =			raw_datasets["validation"]
# Validation Feature Creation
__A							:			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",
)

__A							:			str         =			default_data_collator

__A							:			List[Any]         =			eval_dataset.remove_columns(["example_id", "offset_mapping"])
__A							:			int         =			DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE="eval"				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							postprocess_qa_predictions(
        examples=_SCREAMING_SNAKE_CASE							,     features=_SCREAMING_SNAKE_CASE							,     predictions=_SCREAMING_SNAKE_CASE							,     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=_SCREAMING_SNAKE_CASE							,     )
    # Format the result to the format the metric expects.
    if args.version_2_with_negative:
        _A     =							[
            {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
        ]
    else:
        _A     =							[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

    _A     =							[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
    return EvalPrediction(predictions=_SCREAMING_SNAKE_CASE							,     label_ids=_SCREAMING_SNAKE_CASE				)


__A							:			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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Tuple:




     """simple docstring"""




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

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

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

 # Create a stream in which to copy inputs/outputs and run inference.
 __A							:			int         =			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							:			int         =			0.0
 __A							:			Optional[Any]         =			0
 __A							:			Union[str, Any]         =			timeit.default_timer()

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

  __A     ,						__A							:			Any         =			outputs
  __A							:			Any         =			torch.tensor(start_logits)
  __A							:			Tuple         =			torch.tensor(end_logits)

  # necessary to pad predictions and labels for being gathered
  __A							:			Optional[int]         =			accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
  __A							:			Optional[Any]         =			accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

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

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

 __A							:			Optional[Any]         =			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)

__A							:			Optional[Any]         =			post_processing_function(eval_examples, eval_dataset, all_preds)
__A							:			Optional[int]         =			metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



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












from collections import defaultdict
from math import ceil, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 1_000_000							,     _SCREAMING_SNAKE_CASE = 10				)							-> int:




    """simple docstring"""




    _A     =							defaultdict(_SCREAMING_SNAKE_CASE				)

    for outer_width in range(3							,     (t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            _A     =							max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)							,     1				)
        else:
            _A     =							1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_SCREAMING_SNAKE_CASE							,     outer_width - 1							,     2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


if __name__ == "__main__":
 print(f"{solution() = }")





