




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCamelCase__					    =    logging.get_logger(__name__)

UpperCamelCase__					    =    {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}
class     a__					(      UpperCamelCase_   ):
						_a							:					List[str]      =					"""deberta-v2"""






						def __init__(     self	,    _A=1_2_8_1_0_0	,    _A=1_5_3_6	,    _A=2_4	,    _A=2_4	,    _A=6_1_4_4	,    _A="gelu"	,    _A=0.1	,    _A=0.1	,    _A=5_1_2	,    _A=0	,    _A=0.02	,    _A=1E-7	,    _A=False	,    _A=-1	,    _A=0	,    _A=True	,    _A=None	,    _A=0	,    _A="gelu"	,    **_A	,    ):




											"""simple docstring"""



											super().__init__(**__A    )

											__lowerCAmelCase            = hidden_size
											__lowerCAmelCase            = num_hidden_layers
											__lowerCAmelCase            = num_attention_heads
											__lowerCAmelCase            = intermediate_size
											__lowerCAmelCase            = hidden_act
											__lowerCAmelCase            = hidden_dropout_prob
											__lowerCAmelCase            = attention_probs_dropout_prob
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = type_vocab_size
											__lowerCAmelCase            = initializer_range
											__lowerCAmelCase            = relative_attention
											__lowerCAmelCase            = max_relative_positions
											__lowerCAmelCase            = pad_token_id
											__lowerCAmelCase            = position_biased_input

											# Backwards compatibility
											if type(__A    ) == str:
																__lowerCAmelCase            = [x.strip() for x in pos_att_type.lower().split("|"    )]

											__lowerCAmelCase            = pos_att_type
											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = layer_norm_eps

											__lowerCAmelCase            = kwargs.get("pooler_hidden_size"	,    __A    )
											__lowerCAmelCase            = pooler_dropout
											__lowerCAmelCase            = pooler_hidden_act






class     a__					(      UpperCamelCase_   ):






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											if self.task == "multiple-choice":
																__lowerCAmelCase            = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																__lowerCAmelCase            = {0: "batch", 1: "sequence"}
											if self._config.type_vocab_size > 0:
																return OrderedDict(
																    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]    )
											else:
																return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]    )






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return 1_2






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = -1	,    _A = -1	,    _A = -1	,    _A = False	,    _A = None	,    _A = 3	,    _A = 4_0	,    _A = 4_0	,    _A = None	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = super().generate_dummy_inputs(preprocessor=__A	,    framework=__A    )
											if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
											return dummy_inputs










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


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

UpperCamelCase__					    =    logging.getLogger(__name__)

UpperCamelCase__					    =    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""",
)

UpperCamelCase__					    =    parser.parse_args()

if args.tokenizer_name:
					UpperCamelCase__					    =    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)

UpperCamelCase__					    =    args.per_device_eval_batch_size

UpperCamelCase__					    =    (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCamelCase__					    =    True

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

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

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

					with builder.create_builder_config() as config:
										UpperCamelCase__					    =    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)
										UpperCamelCase__					    =    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)
										UpperCamelCase__					    =    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 			_a   (		SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ):
					__lowerCAmelCase            = np.asarray(inputs["input_ids"]   ,   dtype=np.intaa  )
					__lowerCAmelCase            = np.asarray(inputs["attention_mask"]   ,   dtype=np.intaa  )
					__lowerCAmelCase            = 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
					__lowerCAmelCase            = 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
					__lowerCAmelCase            = time.time()
					__lowerCAmelCase            = end_time - start_time
					__lowerCAmelCase            = (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.
UpperCamelCase__					    =    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.
					UpperCamelCase__					    =    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.

UpperCamelCase__					    =    raw_datasets["""validation"""].column_names

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

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

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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       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
					__lowerCAmelCase            = [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            = 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.
					__lowerCAmelCase            = 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            = []

					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            = tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = 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            = 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            = [
										    (o if sequence_ids[k] == context_index else None)
										    for k, o in enumerate(tokenized_examples["offset_mapping"][i]  )
										]

					return tokenized_examples


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

UpperCamelCase__					    =    default_data_collator

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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[int]   ,   SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       Union[str, Any]="eval"  ):
					# Post-processing: we match the start logits and end logits to answers in the original context.
					__lowerCAmelCase            = 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:
										__lowerCAmelCase            = [
										    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
										]
					else:
										__lowerCAmelCase            = [{"id": k, "prediction_text": v} for k, v in predictions.items()]

					__lowerCAmelCase            = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
					return EvalPrediction(predictions=SCREAMING_SNAKE_CASE_   ,   label_ids=SCREAMING_SNAKE_CASE_  )


UpperCamelCase__					    =    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 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
										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.
					UpperCamelCase__					    =    [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

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

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

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

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

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

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

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

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

					UpperCamelCase__					    =    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)

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


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




