





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

_A :   Optional[int]  			=  logging.getLogger(__name__)

_A :   List[str]  			=  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',
)

_A :   Optional[int]  			=  parser.parse_args()

if args.tokenizer_name:
       _A :   List[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 :   List[Any]  			=  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_A :   Optional[int]  			=  True

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

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

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

       with builder.create_builder_config() as config:
              _A :   List[str]  			=  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 :   Optional[int]  			=  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 :   Dict  			=  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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							np.asarray(inputs['''input_ids''']      ,  dtype=np.intaa					)
   lowerCamelCase__       :						List[Any]        =							np.asarray(inputs['''attention_mask''']      ,  dtype=np.intaa					)
   lowerCamelCase__       :						List[Any]        =							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
   lowerCamelCase__       :						int        =							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
   lowerCamelCase__       :						Union[str, Any]        =							time.time()
   lowerCamelCase__       :						List[str]        =							end_time - start_time
   lowerCamelCase__       :						List[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.
_A :   Tuple  			=  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 :   Union[str, 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 :   Optional[Any]  			=  raw_datasets['validation'].column_names

_A :   List[str]  			=  'question' if 'question' in column_names else column_names[0]
_A :   int  			=  'context' if 'context' in column_names else column_names[1]
_A :   int  			=  'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_A :   Optional[Any]  			=  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 	_a						(   UpperCAmelCase					)    ->    Any:







   """simple docstring"""
   # 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__       :						Tuple        =							[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[Any]        =							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.
   lowerCamelCase__       :						Dict        =							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__       :						Union[str, Any]        =							[]

   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__       :						Optional[int]        =							tokenized_examples.sequence_ids(UpperCAmelCase					)
      lowerCamelCase__       :						List[str]        =							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__       :						int        =							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__       :						int        =							[
          (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 :   Any  			=  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 :   int  			=  default_data_collator

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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase="eval"					)    ->    List[Any]:







   """simple docstring"""
   # Post-processing: we match the start logits and end logits to answers in the original context.
   lowerCamelCase__       :						Union[str, Any]        =							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:
      lowerCamelCase__       :						Tuple        =							[
          {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
      ]
   else:
      lowerCamelCase__       :						List[str]        =							[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

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


_A :   List[str]  			=  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 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







          """simple docstring"""
          return trt.volume(engine.get_binding_shape(UpperCAmelCase					)					) * engine.get_binding_dtype(UpperCAmelCase					).itemsize

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

       # Allocate output buffer
       _A :   List[Any]  			=  cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
       _A :   Any  			=  cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
       _A :   Dict  			=  cuda.mem_alloc(h_outputa.nbytes)
       _A :   Optional[int]  			=  cuda.mem_alloc(h_outputa.nbytes)

       # Create a stream in which to copy inputs/outputs and run inference.
       _A :   str  			=  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 :   Optional[Any]  			=  0.0
       _A :   Optional[Any]  			=  0
       _A :   int  			=  timeit.default_timer()

       _A :   int  			=  None
       for step, batch in enumerate(eval_dataloader):
              _A			,  _A :   Optional[Any]  			=  model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
              total_time += infer_time
              niter += 1

              _A			,  _A :   List[Any]  			=  outputs
              _A :   str  			=  torch.tensor(start_logits)
              _A :   Tuple  			=  torch.tensor(end_logits)

              # necessary to pad predictions and labels for being gathered
              _A :   Optional[Any]  			=  accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
              _A :   List[str]  			=  accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

              _A :   Optional[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=-1_00)

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

       _A :   str  			=  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)

_A :   int  			=  post_processing_function(eval_examples, eval_dataset, all_preds)
_A :   Optional[Any]  			=  metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




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










from math import factorial


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]		, A					:     Dict		, A					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							real
          if isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[1] * rank
          else:
             lowerCamelCase__       :						List[Any]        =							rank







       def __repr__(				self					:     Tuple      )       ->str:
          return (
              F"{self.real}+"
              F"{'+'.join(str(A      )+'E'+str(n+1      )for n,dual in enumerate(self.duals      )      )}"
          )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							self.duals.copy()
          while cur[-1] == 0:
             cur.pop(-1      )
          return Dual(self.real		, A      )







       def __add__(				self					:     Union[str, Any]		, A					:     int      )       ->str:
          if not isinstance(A		, A      ):
             return Dual(self.real + other		, self.duals      )
          lowerCamelCase__       :						int        =							self.duals.copy()
          lowerCamelCase__       :						int        =							other.duals.copy()
          if len(A      ) > len(A      ):
             o_dual.extend([1] * (len(A      ) - len(A      ))      )
          elif len(A      ) < len(A      ):
             s_dual.extend([1] * (len(A      ) - len(A      ))      )
          lowerCamelCase__       :						Optional[Any]        =							[]
          for i in range(len(A      )      ):
             new_duals.append(s_dual[i] + o_dual[i]      )
          return Dual(self.real + other.real		, A      )

       _UpperCAmelCase				:  List[Any]			   =   __add__







       def __sub__(				self					:     Any		, A					:     Dict      )       ->int:
          return self + other * -1







       def __mul__(				self					:     Optional[Any]		, A					:     List[Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[]
             for i in self.duals:
                new_duals.append(i * other      )
             return Dual(self.real * other		, A      )
          lowerCamelCase__       :						Tuple        =							[0] * (len(self.duals      ) + len(other.duals      ) + 1)
          for i, item in enumerate(self.duals      ):
             for j, jtem in enumerate(other.duals      ):
                new_duals[i + j + 1] += item * jtem
          for k in range(len(self.duals      )      ):
             new_duals[k] += self.duals[k] * other.real
          for index in range(len(other.duals      )      ):
             new_duals[index] += other.duals[index] * self.real
          return Dual(self.real * other.real		, A      )

       _UpperCAmelCase				:  Optional[Any]			   =   __mul__







       def __truediv__(				self					:     int		, A					:     List[Any]      )       ->Dict:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i / other      )
             return Dual(self.real / other		, A      )
          raise ValueError







       def __floordiv__(				self					:     Dict		, A					:     Union[str, Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i // other      )
             return Dual(self.real // other		, A      )
          raise ValueError







       def __pow__(				self					:     Any		, A					:     List[Any]      )       ->Tuple:
          if n < 0 or isinstance(A		, A      ):
             raise ValueError('''power must be a positive integer'''      )
          if n == 0:
             return 1
          if n == 1:
             return self
          lowerCamelCase__       :						Union[str, Any]        =							self
          for _ in range(n - 1      ):
             x *= self
          return x



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if not callable(UpperCAmelCase					):
      raise ValueError('''differentiate() requires a function as input for func'''					)
   if not isinstance(UpperCAmelCase      ,  (float, int)					):
      raise ValueError('''differentiate() requires a float as input for position'''					)
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise ValueError('''differentiate() requires an int as input for order'''					)
   lowerCamelCase__       :						List[str]        =							Dual(UpperCAmelCase      ,  1					)
   lowerCamelCase__       :						Any        =							func(UpperCAmelCase					)
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(UpperCAmelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



       def 	_a						(   UpperCAmelCase					)    ->    int:







          """simple docstring"""
          return y**2 * y**4




       print(differentiate(f, 9, 2))



