

'''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


_a					:			Tuple							=    trt.Logger(trt.Logger.WARNING)
_a					:			Optional[Any]							=    absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_a					:			Optional[Any]							=    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=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					:			Optional[Any]							=    parser.parse_args()

if args.tokenizer_name:
  _a					:			Dict							=    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					:			Optional[Any]							=    args.per_device_eval_batch_size

_a					:			Optional[int]							=    (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_a					:			List[str]							=    True

_a					:			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					:			List[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					:			Tuple							=    [network.get_input(i) for i in range(network.num_inputs)]
  _a					:			Optional[int]							=    [_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					:			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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : 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()							, SCREAMING_SNAKE_CASE    )
  cuda.memcpy_htod_async(d_inputs[1]							, attention_mask.ravel()							, SCREAMING_SNAKE_CASE    )
  cuda.memcpy_htod_async(d_inputs[2]							, token_type_ids.ravel()							, SCREAMING_SNAKE_CASE    )
  # start time
  UpperCAmelCase    				=						time.time()
  # Run inference
  context.execute_async(
      bindings=[int(SCREAMING_SNAKE_CASE    ) for d_inp in d_inputs] + [int(SCREAMING_SNAKE_CASE    ), int(SCREAMING_SNAKE_CASE    )]							, stream_handle=stream.handle    )
  # Transfer predictions back from GPU
  cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  # 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.
_a					:			Dict							=    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					:			List[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					:			Union[str, Any]							=    'question' if 'question' in column_names else column_names[0]
_a					:			List[Any]							=    'context' if 'context' in column_names else column_names[1]
_a					:			List[str]							=    '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					:			Tuple							=    min(args.max_seq_length, tokenizer.model_max_length)


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[int]    ):
  # 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=SCREAMING_SNAKE_CASE							, stride=args.doc_stride							, return_overflowing_tokens=SCREAMING_SNAKE_CASE							, return_offsets_mapping=SCREAMING_SNAKE_CASE							, 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(SCREAMING_SNAKE_CASE    )
    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


_a					:			List[str]							=    raw_datasets['validation']
# Validation Feature Creation
_a					:			List[str]							=    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					:			Any							=    default_data_collator

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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : int="eval"    ):
  # Post-processing: we match the start logits and end logits to answers in the original context.
  UpperCAmelCase    				=						postprocess_qa_predictions(
      examples=SCREAMING_SNAKE_CASE							, features=SCREAMING_SNAKE_CASE							, predictions=SCREAMING_SNAKE_CASE							, 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=SCREAMING_SNAKE_CASE							, )
  # 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=SCREAMING_SNAKE_CASE							, label_ids=SCREAMING_SNAKE_CASE    )


_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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
    return trt.volume(engine.get_binding_shape(SCREAMING_SNAKE_CASE    )    ) * engine.get_binding_dtype(SCREAMING_SNAKE_CASE    ).itemsize

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

  # Allocate output buffer
  _a					:			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					:			Dict							=    cuda.mem_alloc(h_outputa.nbytes)
  _a					:			str							=    cuda.mem_alloc(h_outputa.nbytes)

  # Create a stream in which to copy inputs/outputs and run inference.
  _a					:			Optional[int]							=    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					:			Union[str, Any]							=    0.0
  _a					:			List[str]							=    0
  _a					:			List[str]							=    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					:			str							=    outputs
    _a					:			int							=    torch.tensor(start_logits)
    _a					:			Tuple							=    torch.tensor(end_logits)

    # necessary to pad predictions and labels for being gathered
    _a					:			Any							=    accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
    _a					:			int							=    accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

    _a					:			Optional[Any]							=    (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
    _a					:			Tuple							=    logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

  _a					:			Union[str, 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					:			int							=    post_processing_function(eval_examples, eval_dataset, all_preds)
_a					:			List[str]							=    metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



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





'''simple docstring'''




from __future__ import annotations


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int | float | str							, SCREAMING_SNAKE_CASE   : int | float | str    ):
  if nth_term == "":
    return [""]
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						[]
  for temp in range(int(SCREAMING_SNAKE_CASE    )    ):
    series.append(f'''1 / {pow(temp + 1							, int(SCREAMING_SNAKE_CASE    )    )}''' if series else '1'    )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  _a					:			List[Any]							=    int(input('Enter the last number (nth term) of the P-Series'))
  _a					:			Tuple							=    int(input('Enter the power for  P-Series'))
  print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
  print(p_series(nth_term, power))


