




"""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_      			=       trt.Logger(trt.Logger.WARNING)
A_      			=       absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A_      			=       logging.getLogger(__name__)

A_      			=       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_      			=       parser.parse_args()

if args.tokenizer_name:
     A_      			=       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_      			=       args.per_device_eval_batch_size

A_      			=       (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A_      			=       True

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

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

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

     with builder.create_builder_config() as config:
          A_      			=       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_      			=       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_      			=       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__							:      Any	,     snake_case__							:      Tuple	,     snake_case__							:      Any	,     snake_case__							:      Union[str, Any]	,     snake_case__							:      Tuple	,     snake_case__							:      int	,     snake_case__							:      Optional[int]	,     snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	Optional[int]			=						np.asarray(inputs["""input_ids"""]	,     dtype=np.intaa					)
  _snake_case							:	Any			=						np.asarray(inputs["""attention_mask"""]	,     dtype=np.intaa					)
  _snake_case							:	str			=						np.asarray(inputs["""token_type_ids"""]	,     dtype=np.intaa					)

  # Copy inputs
  cuda.memcpy_htod_async(d_inputs[0]	,     input_ids.ravel()	,     snake_case__					)
  cuda.memcpy_htod_async(d_inputs[1]	,     attention_mask.ravel()	,     snake_case__					)
  cuda.memcpy_htod_async(d_inputs[2]	,     token_type_ids.ravel()	,     snake_case__					)
  # start time
  _snake_case							:	Optional[int]			=						time.time()
  # Run inference
  context.execute_async(
      bindings=[int(snake_case__					) for d_inp in d_inputs] + [int(snake_case__					), int(snake_case__					)]	,     stream_handle=stream.handle					)
  # Transfer predictions back from GPU
  cuda.memcpy_dtoh_async(snake_case__	,     snake_case__	,     snake_case__					)
  cuda.memcpy_dtoh_async(snake_case__	,     snake_case__	,     snake_case__					)
  # Synchronize the stream and take time
  stream.synchronize()
  # end time
  _snake_case							:	int			=						time.time()
  _snake_case							:	str			=						end_time - start_time
  _snake_case							:	int			=						(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_      			=       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_      			=       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_      			=       raw_datasets['''validation'''].column_names

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

# Padding side determines if we do (question|context) or (context|question).
A_      			=       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_      			=       min(args.max_seq_length, tokenizer.model_max_length)


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]					):
  """simple docstring"""

  _snake_case							:	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.
  _snake_case							:	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=snake_case__	,     stride=args.doc_stride	,     return_overflowing_tokens=snake_case__	,     return_offsets_mapping=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.
  _snake_case							:	Union[str, 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.
  _snake_case							:	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).
    _snake_case							:	List[Any]			=						tokenized_examples.sequence_ids(snake_case__					)
    _snake_case							:	Optional[int]			=						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.
    _snake_case							:	Optional[Any]			=						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.
    _snake_case							:	List[Any]			=						[
        (o if sequence_ids[k] == context_index else None)
        for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]					)
    ]

  return tokenized_examples


A_      			=       raw_datasets['''validation''']
# Validation Feature Creation
A_      			=       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_      			=       default_data_collator

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


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      List[Any]	,     snake_case__							:      str	,     snake_case__							:      str="eval"					):
  """simple docstring"""

  _snake_case							:	int			=						postprocess_qa_predictions(
      examples=snake_case__	,     features=snake_case__	,     predictions=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=snake_case__	,     )
  # Format the result to the format the metric expects.
  if args.version_2_with_negative:
    _snake_case							:	Optional[Any]			=						[
        {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
    ]
  else:
    _snake_case							:	int			=						[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

  _snake_case							:	Tuple			=						[{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=snake_case__	,     label_ids=snake_case__					)


A_      			=       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__							:      Optional[Any]					):
       """simple docstring"""

       return trt.volume(engine.get_binding_shape(snake_case__					)					) * engine.get_binding_dtype(snake_case__					).itemsize

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

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

     # Create a stream in which to copy inputs/outputs and run inference.
     A_      			=       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_      			=       0.0
     A_      			=       0
     A_      			=       timeit.default_timer()

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

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

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

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

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

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






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








"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

A_      			=       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A_      			=       [ord(letter) for letter in string.ascii_lowercase]
A_      			=       {ord(char) for char in VALID_CHARS}

A_      			=       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      tuple[int, ...]					):
  """simple docstring"""

  _snake_case							:	str			=						""
  _snake_case							:	int
  _snake_case							:	int
  _snake_case							:	int

  for keychar, cipherchar in zip(cycle(snake_case__					)	,     snake_case__					):
    _snake_case							:	List[str]			=						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(snake_case__					)

  return decoded


def       UpperCAmelCase__ (snake_case__							:      list[int]					):
  """simple docstring"""

  _snake_case							:	list[str]			=						[]
  for key in product(snake_case__	,     repeat=3					):
    _snake_case							:	List[Any]			=						try_key(snake_case__	,     snake_case__					)
    if encoded is not None:
      possibles.append(snake_case__					)
  return possibles


def       UpperCAmelCase__ (snake_case__							:      list[str]	,     snake_case__							:      str					):
  """simple docstring"""

  return [possible for possible in possibles if common_word in possible.lower()]


def       UpperCAmelCase__ (snake_case__							:      str = "p059_cipher.txt"					):
  """simple docstring"""

  _snake_case							:	list[int]
  _snake_case							:	list[str]
  _snake_case							:	str
  _snake_case							:	str
  _snake_case							:	str			=						Path(snake_case__					).parent.joinpath(snake_case__					).read_text(encoding="""utf-8"""					)

  _snake_case							:	List[Any]			=						[int(snake_case__					) for number in data.strip().split(""","""					)]

  _snake_case							:	Optional[Any]			=						filter_valid_chars(snake_case__					)
  for common_word in COMMON_WORDS:
    _snake_case							:	Union[str, Any]			=						filter_common_word(snake_case__	,     snake_case__					)
    if len(snake_case__					) == 1:
      break

  _snake_case							:	Optional[int]			=						possibles[0]
  return sum(ord(snake_case__					) for char in decoded_text					)


if __name__ == "__main__":
     print(F'''{solution() = }''')


