



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


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

_lowercase    	=		logging.getLogger(__name__)

_lowercase    	=		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",
)

_lowercase    	=		parser.parse_args()

if args.tokenizer_name:
   _lowercase    	=		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)

_lowercase    	=		args.per_device_eval_batch_size

_lowercase    	=		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowercase    	=		True

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

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

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

   with builder.create_builder_config() as config:
      _lowercase    	=		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)
      _lowercase    	=		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)
      _lowercase    	=		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_					:							int			,							UpperCamelCase_					:							Optional[Any]			,							UpperCamelCase_					:							Dict			,							UpperCamelCase_					:							Dict			,							UpperCamelCase_					:							Optional[int]			,							UpperCamelCase_					:							Dict			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							List[Any]						)->	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()			,							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
    A__					=       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
    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.
_lowercase    	=		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.
   _lowercase    	=		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.

_lowercase    	=		raw_datasets["validation"].column_names

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

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

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	List[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
    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=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.
    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(UpperCamelCase_						)
        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


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

_lowercase    	=		default_data_collator

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[int]			,							UpperCamelCase_					:							str			,							UpperCamelCase_					:							str			,							UpperCamelCase_					:							Dict="eval"						)->	str:
    # Post-processing: we match the start logits and end logits to answers in the original context.
    A__					=       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:
        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=UpperCamelCase_			,							label_ids=UpperCamelCase_						)


_lowercase    	=		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_					:							str						)->	Optional[Any]:
       return trt.volume(engine.get_binding_shape(UpperCamelCase_						)						) * engine.get_binding_dtype(UpperCamelCase_						).itemsize

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

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

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

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

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

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

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

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

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

   _lowercase    	=		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)

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



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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       hidden_act
         A__					=       intermediate_size
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       initializer_range
         A__					=       layer_norm_eps
         A__					=       position_embedding_type
         A__					=       use_cache
         A__					=       classifier_dropout







class      _UpperCAmelCase      (							A__  ):




     @property
     def 		snake_case_		(					self):
         if self.task == "multiple-choice":
             A__					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__					=       {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ])
