






'''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


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

UpperCamelCase__			=      logging.getLogger(__name__)

UpperCamelCase__			=      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',
)

UpperCamelCase__			=      parser.parse_args()

if args.tokenizer_name:
 UpperCamelCase__			=      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)

UpperCamelCase__			=      args.per_device_eval_batch_size

UpperCamelCase__			=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCamelCase__			=      True

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

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

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

 with builder.create_builder_config() as config:
  UpperCamelCase__			=      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)
  UpperCamelCase__			=      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)
  UpperCamelCase__			=      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    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  np.asarray(inputs["input_ids"]    ,		dtype=np.intaa				)
   lowercase_						:     Dict							     =  np.asarray(inputs["attention_mask"]    ,		dtype=np.intaa				)
   lowercase_						:     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
   lowercase_						:     List[str]							     =  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
   lowercase_						:     int							     =  time.time()
   lowercase_						:     Union[str, Any]							     =  end_time - start_time
   lowercase_						:     List[str]							     =  (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.
UpperCamelCase__			=      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.
 UpperCamelCase__			=      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.

UpperCamelCase__			=      raw_datasets['validation'].column_names

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

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

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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     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.
   lowercase_						:     Union[str, 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.
   lowercase_						:     List[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.
   lowercase_						:     List[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).
      lowercase_						:     Tuple							     =  tokenized_examples.sequence_ids(_UpperCamelCase				)
      lowercase_						:     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.
      lowercase_						:     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.
      lowercase_						:     Optional[int]							     =  [
          (o if sequence_ids[k] == context_index else None)
          for k, o in enumerate(tokenized_examples["offset_mapping"][i]				)
      ]

   return tokenized_examples


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

UpperCamelCase__			=      default_data_collator

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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase="eval"				):


   """simple docstring"""




   lowercase_						:     Dict							     =  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:
      lowercase_						:     Dict							     =  [
          {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
      ]
   else:
      lowercase_						:     str							     =  [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

   lowercase_						:     str							     =  [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
   return EvalPrediction(predictions=_UpperCamelCase    ,		label_ids=_UpperCamelCase				)


UpperCamelCase__			=      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				):


    """simple docstring"""




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

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

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

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

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

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

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

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

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

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

 UpperCamelCase__			=      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 * 1000 / niter))
 logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))





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

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





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





"""simple docstring"""



from collections import defaultdict
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			total  # total no of tasks (N)

										# DP table will have a dimension of (2^M)*N
										# initially all values are set to -1
										A__       :     Optional[int]								=			[
										    [-1 for i in range(total + 1							)] for j in range(2 ** len(snake_case_							)							)
										]

										A__       :     Optional[int]								=			defaultdict(snake_case_							)  # stores the list of persons for each task

										# final_mask is used to check if all persons are included by setting all bits
										# to 1
										A__       :     Optional[int]								=			(1 << len(snake_case_							)) - 1





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if mask == self.final_mask:
																	return 1

										# if not everyone gets the task and no more tasks are available, return 0
										if task_no > self.total_tasks:
																	return 0

										# if case already considered
										if self.dp[mask][task_no] != -1:
																	return self.dp[mask][task_no]

										# Number of ways when we don't this task in the arrangement
										A__       :     Optional[int]								=			self.count_ways_until(snake_case_   ,      task_no + 1							)

										# now assign the tasks one by one to all possible persons and recursively
										# assign for the remaining tasks.
										if task_no in self.task:
																	for p in self.task[task_no]:
																								# if p is already given a task
																								if mask & (1 << p):
																															continue

																								# assign this task to p and change the mask value. And recursively
																								# assign tasks with the new mask value.
																								total_ways_util += self.count_ways_until(mask | (1 << p)   ,      task_no + 1							)

        # save the value.
										A__       :     List[str]								=			total_ways_util

										return self.dp[mask][task_no]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for i in range(len(snake_case_							)							):
																	for j in task_performed[i]:
																								self.task[j].append(snake_case_							)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
										return self.count_ways_until(0   ,      1							)


if __name__ == "__main__":
				_UpperCamelCase								=		5  # total no of tasks (the value of N)

				# the list of tasks that can be done by M persons.
				_UpperCamelCase								=		[[1, 3, 4], [1, 2, 5], [3, 4]]
				print(
				    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
				        task_performed
				    )
				)





