






"""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		:  str						=		trt.Logger(trt.Logger.WARNING)
_UpperCamelCase		:  Union[str, Any]						=		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_UpperCamelCase		:  int						=		logging.getLogger(__name__)

_UpperCamelCase		:  Union[str, Any]						=		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_8_4,
    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_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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		:  Optional[int]						=		parser.parse_args()

if args.tokenizer_name:
    _UpperCamelCase		:  str						=		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		:  Dict						=		args.per_device_eval_batch_size

_UpperCamelCase		:  Dict						=		(args.eval_batch_size, args.max_seq_length)

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

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

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

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

    with builder.create_builder_config() as config:
        _UpperCamelCase		:  Tuple						=		1 << 5_0
        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		:  Union[str, 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)
        _UpperCamelCase		:  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Optional[Any]		,					__snake_case			:  Dict		,					__snake_case			:  Any		,					__snake_case			:  List[str]		,					__snake_case			:  List[str]		,					__snake_case			:  int		,					__snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       np.asarray(inputs['input_ids']		,					dtype=np.intaa    )
  lowercase							    =       np.asarray(inputs['attention_mask']		,					dtype=np.intaa    )
  lowercase							    =       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
  lowercase							    =       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
  lowercase							    =       time.time()
  lowercase							    =       end_time - start_time
  lowercase							    =       (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		:  Optional[Any]						=		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		:  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.

_UpperCamelCase		:  Optional[int]						=		raw_datasets['validation'].column_names

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

# Padding side determines if we do (question|context) or (context|question).
_UpperCamelCase		:  Union[str, 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}.'''
    )

_UpperCamelCase		:  Tuple						=		min(args.max_seq_length, tokenizer.model_max_length)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       [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							    =       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.
  lowercase							    =       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							    =       []

  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							    =       tokenized_examples.sequence_ids(__snake_case    )
    lowercase							    =       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							    =       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							    =       [
        (o if sequence_ids[k] == context_index else None)
        for k, o in enumerate(tokenized_examples['offset_mapping'][i]    )
    ]

  return tokenized_examples


_UpperCamelCase		:  List[str]						=		raw_datasets['validation']
# Validation Feature Creation
_UpperCamelCase		:  List[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',
)

_UpperCamelCase		:  List[Any]						=		default_data_collator

_UpperCamelCase		:  List[str]						=		eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_UpperCamelCase		:  Dict						=		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  Optional[Any]		,					__snake_case			:  List[str]		,					__snake_case			:  List[str]="eval"    ):





  '''simple docstring'''





  lowercase							    =       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:
    lowercase							    =       [
        {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
    ]
  else:
    lowercase							    =       [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

  lowercase							    =       [{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=__snake_case		,					label_ids=__snake_case    )


_UpperCamelCase		:  Optional[int]						=		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 (    __snake_case			:  List[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.
    _UpperCamelCase		:  int						=		[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

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

    # Create a stream in which to copy inputs/outputs and run inference.
    _UpperCamelCase		:  List[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}''')

    _UpperCamelCase		:  Tuple						=		0.0
    _UpperCamelCase		:  Union[str, Any]						=		0
    _UpperCamelCase		:  Dict						=		timeit.default_timer()

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

        _UpperCamelCase,		_UpperCamelCase		:  Any						=		outputs
        _UpperCamelCase		:  Dict						=		torch.tensor(start_logits)
        _UpperCamelCase		:  Optional[Any]						=		torch.tensor(end_logits)

        # necessary to pad predictions and labels for being gathered
        _UpperCamelCase		:  Optional[int]						=		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
        _UpperCamelCase		:  int						=		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

        _UpperCamelCase		:  int						=		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
        _UpperCamelCase		:  Optional[Any]						=		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

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

    _UpperCamelCase		:  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 * 1_0_0_0 / niter))
    logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_0_0_0))



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

_UpperCamelCase		:  Dict						=		post_processing_function(eval_examples, eval_dataset, all_preds)
_UpperCamelCase		:  Any						=		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



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










"""simple docstring"""



import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase		:  Tuple						=		logging.getLogger()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Path		,					__snake_case			:  list    ):





  '''simple docstring'''





  lowercase							    =       '\n'.join(__snake_case    )
  Path(__snake_case    ).open('w'    ).writelines(__snake_case    )


_UpperCamelCase		:  Union[str, Any]						=		'patrickvonplaten/t5-tiny-random'
_UpperCamelCase		:  Union[str, Any]						=		'sshleifer/bart-tiny-random'
_UpperCamelCase		:  Tuple						=		'sshleifer/tiny-mbart'

_UpperCamelCase		:  Union[str, Any]						=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class        a     (   a_	):







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()
      lowercase							    =       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
      _dump_articles(_lowerCamelCase							,		_lowerCamelCase       )

      lowercase							    =       str(Path(self.get_auto_remove_tmp_dir()       ) / 'scores.json'       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        run_generate()
        assert Path(_lowerCamelCase       ).exists()
        # os.remove(Path(output_file_name))







    def 			UpperCamelCase_      (			self       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([BART_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([T5_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()

      lowercase							    =       {
          'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
          'de': [
              'Maschinelles Lernen ist großartig, oder?',
              'Ich esse gerne Bananen',
              'Morgen ist wieder ein toller Tag!',
          ],
      }

      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       )
      lowercase							    =       str(tmp_dir / 'scores.json'       )
      lowercase							    =       str(tmp_dir / 'val.target'       )
      _dump_articles(_lowerCamelCase							,		text['en']       )
      _dump_articles(_lowerCamelCase							,		text['de']       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {str(_lowerCamelCase       )}\n            {str(_lowerCamelCase       )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
      testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']       )

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        with CaptureStdout() as cs:
          run_search()
        lowercase							    =       [' num_beams | length_penalty', model, 'Best score args']
        lowercase							    =       ['Info']
        if "translation" in task:
          expected_strings.append('bleu'       )
        else:
          expected_strings.extend(_lowerCamelCase       )
        for w in expected_strings:
          assert w in cs.out
        for w in un_expected_strings:
          assert w not in cs.out
        assert Path(_lowerCamelCase       ).exists()
        os.remove(Path(_lowerCamelCase       )       )


