




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


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

SCREAMING_SNAKE_CASE    =	logging.getLogger(__name__)

SCREAMING_SNAKE_CASE    =	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",
)

SCREAMING_SNAKE_CASE    =	parser.parse_args()

if args.tokenizer_name:
      SCREAMING_SNAKE_CASE    =	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)

SCREAMING_SNAKE_CASE    =	args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE    =	(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE    =	True

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

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

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

      with builder.create_builder_config() as config:
            SCREAMING_SNAKE_CASE    =	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)
            SCREAMING_SNAKE_CASE    =	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)
            SCREAMING_SNAKE_CASE    =	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       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[Any]:
 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()      ,			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
 A__	=    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
 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.
SCREAMING_SNAKE_CASE    =	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.
      SCREAMING_SNAKE_CASE    =	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.

SCREAMING_SNAKE_CASE    =	raw_datasets["validation"].column_names

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

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

SCREAMING_SNAKE_CASE    =	min(args.max_seq_length, tokenizer.model_max_length)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 # 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
 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=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.
 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(lowercase_						)
  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


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

SCREAMING_SNAKE_CASE    =	default_data_collator

SCREAMING_SNAKE_CASE    =	eval_dataset.remove_columns(["example_id", "offset_mapping"])
SCREAMING_SNAKE_CASE    =	DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_="eval"						)						->		Dict:
 # Post-processing: we match the start logits and end logits to answers in the original context.
 A__	=    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:
  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=lowercase_      ,			label_ids=lowercase_						)


SCREAMING_SNAKE_CASE    =	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       (							lowercase_						)						->		List[str]:
       return trt.volume(engine.get_binding_shape(lowercase_						)						) * engine.get_binding_dtype(lowercase_						).itemsize

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

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

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

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

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

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

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

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

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

      SCREAMING_SNAKE_CASE    =	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)

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






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











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

 A__	=    Stack()
 A__	=    Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(lowercase_						)						)
  elif i in operators:
   # RULE 2
   operator_stack.push(lowercase_						)
  elif i == ")":
   # RULE 4
   A__	=    operator_stack.peek()
   operator_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()

   A__	=    operators[opr](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	"(5 + ((4 * 2) * (2 + 3)))"
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





