




"""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=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''',
)

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 << 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)
								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 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					List[str]      ,       snake_case			:					List[Any]      ,       snake_case			:					List[Any]      ,       snake_case			:					int      ,       snake_case			:					List[Any]      ,       snake_case			:					Optional[Any]      ,       snake_case			:					Optional[Any]    ):
					_lowerCAmelCase:List[str]									=  np.asarray(inputs['''input_ids''']      ,       dtype=np.intaa    )
					_lowerCAmelCase:Optional[Any]									=  np.asarray(inputs['''attention_mask''']      ,       dtype=np.intaa    )
					_lowerCAmelCase:Dict									=  np.asarray(inputs['''token_type_ids''']      ,       dtype=np.intaa    )

					# Copy inputs
					cuda.memcpy_htod_async(d_inputs[0]      ,       input_ids.ravel()      ,       _lowerCAmelCase    )
					cuda.memcpy_htod_async(d_inputs[1]      ,       attention_mask.ravel()      ,       _lowerCAmelCase    )
					cuda.memcpy_htod_async(d_inputs[2]      ,       token_type_ids.ravel()      ,       _lowerCAmelCase    )
					# start time
					_lowerCAmelCase:List[str]									=  time.time()
					# Run inference
					context.execute_async(
					    bindings=[int(_lowerCAmelCase    ) for d_inp in d_inputs] + [int(_lowerCAmelCase    ), int(_lowerCAmelCase    )]      ,       stream_handle=stream.handle    )
					# Transfer predictions back from GPU
					cuda.memcpy_dtoh_async(_lowerCAmelCase      ,       _lowerCAmelCase      ,       _lowerCAmelCase    )
					cuda.memcpy_dtoh_async(_lowerCAmelCase      ,       _lowerCAmelCase      ,       _lowerCAmelCase    )
					# Synchronize the stream and take time
					stream.synchronize()
					# end time
					_lowerCAmelCase:Any									=  time.time()
					_lowerCAmelCase:int									=  end_time - start_time
					_lowerCAmelCase:Union[str, Any]									=  (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 							UpperCAmelCase		(		snake_case			:					Dict    ):
					# 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:Dict									=  [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:List[str]									=  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=_lowerCAmelCase      ,       stride=args.doc_stride      ,       return_overflowing_tokens=_lowerCAmelCase      ,       return_offsets_mapping=_lowerCAmelCase      ,       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:Optional[Any]									=  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:List[str]									=  []

					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:str									=  tokenized_examples.sequence_ids(_lowerCAmelCase    )
										_lowerCAmelCase:Dict									=  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:Tuple									=  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:Optional[Any]									=  [
										    (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 							UpperCAmelCase		(		snake_case			:					Union[str, Any]      ,       snake_case			:					Tuple      ,       snake_case			:					Tuple      ,       snake_case			:					List[str]="eval"    ):
					# Post-processing: we match the start logits and end logits to answers in the original context.
					_lowerCAmelCase:Union[str, Any]									=  postprocess_qa_predictions(
					    examples=_lowerCAmelCase      ,       features=_lowerCAmelCase      ,       predictions=_lowerCAmelCase      ,       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=_lowerCAmelCase      ,       )
					# Format the result to the format the metric expects.
					if args.version_2_with_negative:
										_lowerCAmelCase:Union[str, Any]									=  [
										    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
										]
					else:
										_lowerCAmelCase:Union[str, Any]									=  [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

					_lowerCAmelCase:Any									=  [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
					return EvalPrediction(predictions=_lowerCAmelCase      ,       label_ids=_lowerCAmelCase    )


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 							UpperCAmelCase		(		snake_case			:					int    ):
									return trt.volume(engine.get_binding_shape(_lowerCAmelCase    )    ) * engine.get_binding_dtype(_lowerCAmelCase    ).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=-1_0_0)
								UpperCamelCase__						=							accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

								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=-1_0_0)

				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_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)

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 importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[int]									=  ArgumentParser(
					    description=(
					        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
					    )    )

					# Optional arguments for the launch helper
					parser.add_argument('''--num_cores'''      ,       type=snake_case      ,       default=1      ,       help='''Number of TPU cores to use (1 or 8).'''    )

					# positional
					parser.add_argument(
					    '''training_script'''      ,       type=snake_case      ,       help=(
					        '''The full path to the single TPU training '''
					        '''program/script to be launched in parallel, '''
					        '''followed by all the arguments for the '''
					        '''training script'''
					    )      ,       )

					# rest from the training program
					parser.add_argument('''training_script_args'''      ,       nargs=snake_case    )

					return parser.parse_args()





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:int									=  parse_args()

					# Import training_script as a module.
					_lowerCAmelCase:Optional[int]									=  Path(args.training_script    )
					sys.path.append(str(script_fpath.parent.resolve()    )    )
					_lowerCAmelCase:Optional[int]									=  script_fpath.stem
					_lowerCAmelCase:Any									=  importlib.import_module(snake_case    )

					# Patch sys.argv
					_lowerCAmelCase:Dict									=  [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores    )]

					xmp.spawn(mod._mp_fn      ,       args=()      ,       nprocs=args.num_cores    )


if __name__ == "__main__":
				main()






