"""simple docstring"""

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      lowercase		(						a__	:		Any ,  a__	:		Optional[int] ,  a__	:		Tuple ,  a__	:		Any ,  a__	:		Any ,  a__	:		List[Any] ,  a__	:		Dict ,  a__	:		Union[str, Any]     )						->   Optional[int]:
					_UpperCamelCase      						=	np.asarray(inputs['''input_ids'''] ,  dtype=np.intaa     )
					_UpperCamelCase      						=	np.asarray(inputs['''attention_mask'''] ,  dtype=np.intaa     )
					_UpperCamelCase      						=	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
					_UpperCamelCase      						=	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
					_UpperCamelCase      						=	time.time()
					_UpperCamelCase      						=	end_time - start_time
					_UpperCamelCase      						=	(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      lowercase		(						a__	:		Dict     )						->   List[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
					_UpperCamelCase      						=	[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.
					_UpperCamelCase      						=	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.
					_UpperCamelCase      						=	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.
					_UpperCamelCase      						=	[]

					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).
										_UpperCamelCase      						=	tokenized_examples.sequence_ids(_lowercase     )
										_UpperCamelCase      						=	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.
										_UpperCamelCase      						=	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.
										_UpperCamelCase      						=	[
										    (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      lowercase		(						a__	:		Union[str, Any] ,  a__	:		Union[str, Any] ,  a__	:		int ,  a__	:		Optional[Any]="eval"     )						->   Tuple:
					# Post-processing: we match the start logits and end logits to answers in the original context.
					_UpperCamelCase      						=	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:
										_UpperCamelCase      						=	[
										    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
										]
					else:
										_UpperCamelCase      						=	[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


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      lowercase		(						a__	:		Dict     )						->   Union[str, Any]:
							return trt.volume(engine.get_binding_shape(_lowercase     )     ) * engine.get_binding_dtype(_lowercase     ).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       =  model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
				total_time += infer_time
				niter += 1

				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 * 1_000 / niter))
		logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1_000))





		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}''')





'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

