





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


a__: str							   =						trt.Logger(trt.Logger.WARNING)
a__: List[Any]							   =						absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

a__: Any							   =						logging.getLogger(__name__)

a__: Optional[int]							   =						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',
)

a__: List[str]							   =						parser.parse_args()

if args.tokenizer_name:
       a__: Any							   =						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)

a__: List[Any]							   =						args.per_device_eval_batch_size

a__: Tuple							   =						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a__: Any							   =						True

a__: str							   =						'temp_engine/bert-fp32.engine'
if args.fpaa:
       a__: Optional[int]							   =						'temp_engine/bert-fp16.engine'
if args.inta:
       a__: int							   =						'temp_engine/bert-int8.engine'

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

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

       with builder.create_builder_config() as config:
              a__: int							   =						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)
              a__: List[Any]							   =						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)
              a__: List[Any]							   =						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__(			UpperCamelCase__     :			int					,	UpperCamelCase__     :			Tuple					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			List[str]    )->Tuple:
       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.
a__: Any							   =						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.
       a__: Any							   =						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.

a__: Tuple							   =						raw_datasets['validation'].column_names

a__: Tuple							   =						'question' if 'question' in column_names else column_names[0]
a__: Optional[int]							   =						'context' if 'context' in column_names else column_names[1]
a__: Optional[Any]							   =						'answers' if 'answers' in column_names else column_names[2]

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

a__: Optional[int]							   =						min(args.max_seq_length, tokenizer.model_max_length)


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]    )->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
       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


a__: Optional[Any]							   =						raw_datasets['validation']
# Validation Feature Creation
a__: Dict							   =						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',
)

a__: str							   =						default_data_collator

a__: Dict							   =						eval_dataset.remove_columns(['example_id', 'offset_mapping'])
a__: Any							   =						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)


def       UpperCamelCase__(			UpperCamelCase__     :			Tuple					,	UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Dict="eval"    )->List[Any]:
       # 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__    )


a__: Any							   =						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__(			UpperCamelCase__     :			List[str]    )->List[Any]:
              return trt.volume(engine.get_binding_shape(UpperCamelCase__    )    ) * engine.get_binding_dtype(UpperCamelCase__    ).itemsize

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

       # Allocate output buffer
       a__: Union[str, Any]							   =						cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
       a__: Tuple							   =						cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
       a__: List[Any]							   =						cuda.mem_alloc(h_outputa.nbytes)
       a__: List[Any]							   =						cuda.mem_alloc(h_outputa.nbytes)

       # Create a stream in which to copy inputs/outputs and run inference.
       a__: List[Any]							   =						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}")

       a__: str							   =						0.0
       a__: int							   =						0
       a__: Tuple							   =						timeit.default_timer()

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

              a__   ,     a__: Optional[int]							   =						outputs
              a__: List[str]							   =						torch.tensor(start_logits)
              a__: Tuple							   =						torch.tensor(end_logits)

              # necessary to pad predictions and labels for being gathered
              a__: Optional[int]							   =						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
              a__: str							   =						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

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

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

       a__: List[Any]							   =						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)

a__: Optional[Any]							   =						post_processing_function(eval_examples, eval_dataset, all_preds)
a__: Tuple							   =						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F"Evaluation metrics: {eval_metric}")












def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		set()

       # Replace all the whitespace in our sentence
       A__		   =		input_str.replace(''' '''					,	''''''    )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()    )
       return len(UpperCamelCase__    ) == 26


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		[False] * 26
       for char in input_str:
              if char.islower():
                     A__		   =		True
              elif char.isupper():
                     A__		   =		True
       return all(UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       return len({char for char in input_str.lower() if char.isalpha()}    ) == 26


def       UpperCamelCase__(			)->None:
       from timeit import timeit

       A__		   =		'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
       print(timeit('''is_pangram()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_faster()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_fastest()'''					,	setup=UpperCamelCase__    )    )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()





