





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=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""",
)

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 << 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)
												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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE	=		np.asarray(inputs["""input_ids"""]				,		dtype=np.intaa			)
						SCREAMING_SNAKE_CASE	=		np.asarray(inputs["""attention_mask"""]				,		dtype=np.intaa			)
						SCREAMING_SNAKE_CASE	=		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
						SCREAMING_SNAKE_CASE	=		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
						SCREAMING_SNAKE_CASE	=		time.time()
						SCREAMING_SNAKE_CASE	=		end_time - start_time
						SCREAMING_SNAKE_CASE	=		(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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Optional[Any]:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE	=		[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.
						SCREAMING_SNAKE_CASE	=		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.
						SCREAMING_SNAKE_CASE	=		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.
						SCREAMING_SNAKE_CASE	=		[]

						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).
												SCREAMING_SNAKE_CASE	=		tokenized_examples.sequence_ids(_SCREAMING_SNAKE_CASE			)
												SCREAMING_SNAKE_CASE	=		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.
												SCREAMING_SNAKE_CASE	=		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.
												SCREAMING_SNAKE_CASE	=		[
												    (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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE="eval"			)				->     Optional[int]:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE	=		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:
												SCREAMING_SNAKE_CASE	=		[
												    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
												]
						else:
												SCREAMING_SNAKE_CASE	=		[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Optional[int]:


												'''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.
						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=-1_0_0)
												SCREAMING_SNAKE_CASE_		     = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

												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=-1_0_0)

						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 * 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)

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}''')









import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""				,		set()			)



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      class 	UpperCamelCase__				:



           '''simple docstring'''

           def __init__(							self		:					int		,lowerCamelCase__		:					Dict )  ->	List[Any]:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE	=		metric_id

      class 	UpperCamelCase__				:



           '''simple docstring'''

           __snake_case					:			List[str]				=    [MetricMock(lowerCAmelCase_     ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]






           def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




                 '''simple docstring'''


                 return self._metrics
      monkeypatch.setattr("""datasets.inspect.huggingface_hub"""				,		HfhMock()			)



@pytest.mark.parametrize(
    """func, args"""				,		[(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))]			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      if "tmp_path" in args:
            SCREAMING_SNAKE_CASE	=		tuple(arg if arg != """tmp_path""" else tmp_path for arg in args			)
      with pytest.warns(_SCREAMING_SNAKE_CASE				,		match="""https://huggingface.co/docs/evaluate"""			):
            func(*_SCREAMING_SNAKE_CASE			)


