




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


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

_lowerCamelCase      			=						logging.getLogger(__name__)

_lowerCamelCase      			=						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_84,
    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_28,
    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',
)

_lowerCamelCase      			=						parser.parse_args()

if args.tokenizer_name:
 _lowerCamelCase      			=						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)

_lowerCamelCase      			=						args.per_device_eval_batch_size

_lowerCamelCase      			=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCamelCase      			=						True

_lowerCamelCase      			=						'temp_engine/bert-fp32.engine'
if args.fpaa:
 _lowerCamelCase      			=						'temp_engine/bert-fp16.engine'
if args.inta:
 _lowerCamelCase      			=						'temp_engine/bert-int8.engine'

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

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

 with builder.create_builder_config() as config:
  _lowerCamelCase      			=						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)
  _lowerCamelCase      			=						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)
  _lowerCamelCase      			=						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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Any				)  ->							List[str]:
    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()	,					__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
    UpperCAmelCase_										=					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
    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.
_lowerCamelCase      			=						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.
 _lowerCamelCase      			=						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.

_lowerCamelCase      			=						raw_datasets['validation'].column_names

_lowerCamelCase      			=						'question' if 'question' in column_names else column_names[0]
_lowerCamelCase      			=						'context' if 'context' in column_names else column_names[1]
_lowerCamelCase      			=						'answers' if 'answers' in column_names else column_names[2]

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

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict				)  ->							List[str]:
    # 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=__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.
    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(__UpperCamelCase				)
        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


_lowerCamelCase      			=						raw_datasets['validation']
# Validation Feature Creation
_lowerCamelCase      			=						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',
)

_lowerCamelCase      			=						default_data_collator

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Any="eval"				)  ->							Union[str, Any]:
    # Post-processing: we match the start logits and end logits to answers in the original context.
    UpperCAmelCase_										=					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:
        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=__UpperCamelCase	,					label_ids=__UpperCamelCase				)


_lowerCamelCase      			=						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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Union[str, Any]:
     return trt.volume(engine.get_binding_shape(__UpperCamelCase				)				) * engine.get_binding_dtype(__UpperCamelCase				).itemsize

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

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

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

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

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

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

  # necessary to pad predictions and labels for being gathered
  _lowerCamelCase      			=						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
  _lowerCamelCase      			=						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

  _lowerCamelCase      			=						(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
  _lowerCamelCase      			=						logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

 _lowerCamelCase      			=						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 * 10_00 / niter))
 logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 10_00))


 logger.info('Total Number of Inference =  %d', niter)

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


logger.info(F"Evaluation metrics: {eval_metric}")











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    while cur > 1:
        # Find the maximum number in arr
        UpperCAmelCase_										=					arr.index(max(arr[0:cur]				)				)
        # Reverse from 0 to mi
        UpperCAmelCase_										=					arr[mi::-1] + arr[mi + 1 : len(__UpperCamelCase				)]
        # Reverse whole list
        UpperCAmelCase_										=					arr[cur - 1 :: -1] + arr[cur : len(__UpperCamelCase				)]
        cur -= 1
    return arr


if __name__ == "__main__":
 _lowerCamelCase      			=						input('Enter numbers separated by a comma:\n').strip()
 _lowerCamelCase      			=						[int(item) for item in user_input.split(',')]
 print(pancake_sort(unsorted))





