




import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


_lowerCamelCase						    =  datasets.logging.get_logger(__name__)


_lowerCamelCase						    =  '''\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1060",
    doi = "10.18653/v1/P16-1060",
    pages = "632--642",
}

'''

_lowerCamelCase						    =  '''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''

_lowerCamelCase						    =  '''
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting \'keep_singletons=False\', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    \'mentions\': mentions
    \'muc\': MUC metric [Vilain et al, 1995]
    \'bcub\': B-cubed [Bagga and Baldwin, 1998]
    \'ceafe\': CEAFe [Luo et al., 2005]
    \'lea\': LEA [Moosavi and Strube, 2016]
    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric(\'coval\')
    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',
    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',
    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',
    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',
    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',
    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Optional[int]=False		,	__lowerCamelCase   :     Tuple=False		,	__lowerCamelCase   :     Any=True		,	__lowerCamelCase   :     Optional[int]=False		,	__lowerCamelCase   :     Any="dummy_doc"     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	{doc: key_lines}
 __SCREAMING_SNAKE_CASE       :    Dict       =	{doc: sys_lines}

 __SCREAMING_SNAKE_CASE       :    Tuple       =	{}

 __SCREAMING_SNAKE_CASE       :    Dict       =	0
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	0
 __SCREAMING_SNAKE_CASE       :    Tuple       =	0
 __SCREAMING_SNAKE_CASE       :    List[str]       =	0
 __SCREAMING_SNAKE_CASE       :    Any       =	0
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	reader.get_doc_mentions(__UpperCamelCase		,	key_doc_lines[doc]		,	__UpperCamelCase     )
 key_singletons_num += singletons_num

 if NP_only or min_span:
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	reader.set_annotated_parse_trees(__UpperCamelCase		,	key_doc_lines[doc]		,	__UpperCamelCase		,	__UpperCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	reader.get_doc_mentions(__UpperCamelCase		,	sys_doc_lines[doc]		,	__UpperCamelCase     )
 sys_singletons_num += singletons_num

 if NP_only or min_span:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	reader.set_annotated_parse_trees(__UpperCamelCase		,	key_doc_lines[doc]		,	__UpperCamelCase		,	__UpperCamelCase     )

 if remove_nested:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	reader.remove_nested_coref_mentions(__UpperCamelCase		,	__UpperCamelCase     )
  key_nested_coref_num += nested_mentions
  key_removed_nested_clusters += removed_clusters

  __SCREAMING_SNAKE_CASE       :    Dict       =	reader.remove_nested_coref_mentions(__UpperCamelCase		,	__UpperCamelCase     )
  sys_nested_coref_num += nested_mentions
  sys_removed_nested_clusters += removed_clusters

 __SCREAMING_SNAKE_CASE       :    List[str]       =	reader.get_mention_assignments(__UpperCamelCase		,	__UpperCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	reader.get_mention_assignments(__UpperCamelCase		,	__UpperCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[str]       =	(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

 if remove_nested:
  logger.info(
      "Number of removed nested coreferring mentions in the key "
      F"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"""     )
  logger.info(
      "Number of resulting singleton clusters in the key "
      F"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"""     )

 if not keep_singletons:
  logger.info(
      F"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
      "files, respectively"     )

 return doc_coref_infos






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	get_coref_infos(__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{}
 __SCREAMING_SNAKE_CASE       :    Dict       =	0
 __SCREAMING_SNAKE_CASE       :    List[str]       =	0

 for name, metric in metrics:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	evaluator.evaluate_documents(__UpperCamelCase		,	__UpperCamelCase		,	beta=1     )
  if name in ["muc", "bcub", "ceafe"]:
   conll += fa
   conll_subparts_num += 1
  output_scores.update({F"""{name}/recall""": recall, F"""{name}/precision""": precision, F"""{name}/f1""": fa}     )

  logger.info(
      name.ljust(10     )		,	F"""Recall: {recall * 100:.2f}"""		,	F""" Precision: {precision * 100:.2f}"""		,	F""" F1: {fa * 100:.2f}"""		,	)

 if conll_subparts_num == 3:
  __SCREAMING_SNAKE_CASE       :    str       =	(conll / 3) * 100
  logger.info(F"""CoNLL score: {conll:.2f}"""     )
  output_scores.update({"conll_score": conll}     )

 return output_scores






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Dict       =	False
 for line in key_lines:
  if not line.startswith("#"     ):
   if len(line.split()     ) > 6:
    __SCREAMING_SNAKE_CASE       :    List[Any]       =	line.split()[5]
    if not parse_col == "-":
     __SCREAMING_SNAKE_CASE       :    Tuple       =	True
     break
    else:
     break
 return has_gold_parse






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,    _KWARGS_DESCRIPTION )
class       _SCREAMING_SNAKE_CASE	(datasets.Metric ):





     def 	__snake_case    (       self :							Tuple				)->List[str]:
      return datasets.MetricInfo(
          description=_DESCRIPTION     ,  citation=_CITATION     ,  inputs_description=_KWARGS_DESCRIPTION     ,  features=datasets.Features(
              {
                  "predictions": datasets.Sequence(datasets.Value("string"				)				),
                  "references": datasets.Sequence(datasets.Value("string"				)				),
              }				)     ,  codebase_urls=["https://github.com/ns-moosavi/coval"]     ,  reference_urls=[
              "https://github.com/ns-moosavi/coval",
              "https://www.aclweb.org/anthology/P16-1060",
              "http://www.conll.cemantix.org/2012/data.html",
          ]     ,  )






     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Optional[int]=True     ,  UpperCamelCase :							Optional[int]=False     ,  UpperCamelCase :							Union[str, Any]=False     ,  UpperCamelCase :							Dict=False				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    int       =	[
          ("""mentions""", evaluator.mentions),
          ("""muc""", evaluator.muc),
          ("""bcub""", evaluator.b_cubed),
          ("""ceafe""", evaluator.ceafe),
          ("""lea""", evaluator.lea),
      ]

      if min_span:
       __SCREAMING_SNAKE_CASE       :    Any       =	util.check_gold_parse_annotation(_lowercase				)
       if not has_gold_parse:
        raise NotImplementedError("References should have gold parse annotation to use 'min_span'."				)
        # util.parse_key_file(key_file)
        # key_file = key_file + ".parsed"

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	evaluate(
          key_lines=_lowercase     ,  sys_lines=_lowercase     ,  metrics=_lowercase     ,  NP_only=_lowercase     ,  remove_nested=_lowercase     ,  keep_singletons=_lowercase     ,  min_span=_lowercase     ,  )

      return score











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


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

_lowerCamelCase						    =  logging.getLogger(__name__)

_lowerCamelCase						    =  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""",
)

_lowerCamelCase						    =  parser.parse_args()

if args.tokenizer_name:
      _lowerCamelCase						    =  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)

_lowerCamelCase						    =  args.per_device_eval_batch_size

_lowerCamelCase						    =  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCamelCase						    =  True

_lowerCamelCase						    =  """temp_engine/bert-fp32.engine"""
if args.fpaa:
      _lowerCamelCase						    =  """temp_engine/bert-fp16.engine"""
if args.inta:
      _lowerCamelCase						    =  """temp_engine/bert-int8.engine"""

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

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

      with builder.create_builder_config() as config:
            _lowerCamelCase						    =  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)
            _lowerCamelCase						    =  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)
            _lowerCamelCase						    =  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						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[Any]     ):



 """simple docstring"""





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

 # Copy inputs
 cuda.memcpy_htod_async(d_inputs[0]		,	input_ids.ravel()		,	__lowerCamelCase     )
 cuda.memcpy_htod_async(d_inputs[1]		,	attention_mask.ravel()		,	__lowerCamelCase     )
 cuda.memcpy_htod_async(d_inputs[2]		,	token_type_ids.ravel()		,	__lowerCamelCase     )
 # start time
 __SCREAMING_SNAKE_CASE       :    str       =	time.time()
 # Run inference
 context.execute_async(
     bindings=[int(__lowerCamelCase     ) for d_inp in d_inputs] + [int(__lowerCamelCase     ), int(__lowerCamelCase     )]		,	stream_handle=stream.handle     )
 # Transfer predictions back from GPU
 cuda.memcpy_dtoh_async(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
 cuda.memcpy_dtoh_async(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
 # Synchronize the stream and take time
 stream.synchronize()
 # end time
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	time.time()
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	end_time - start_time
 __SCREAMING_SNAKE_CASE       :    Tuple       =	(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.
_lowerCamelCase						    =  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.
      _lowerCamelCase						    =  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.

_lowerCamelCase						    =  raw_datasets["""validation"""].column_names

_lowerCamelCase						    =  """question""" if """question""" in column_names else column_names[0]
_lowerCamelCase						    =  """context""" if """context""" in column_names else column_names[1]
_lowerCamelCase						    =  """answers""" if """answers""" in column_names else column_names[2]

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

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





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

 return tokenized_examples


_lowerCamelCase						    =  raw_datasets["""validation"""]
# Validation Feature Creation
_lowerCamelCase						    =  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""",
)

_lowerCamelCase						    =  default_data_collator

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int="eval"     ):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
 return EvalPrediction(predictions=__lowerCamelCase		,	label_ids=__lowerCamelCase     )


_lowerCamelCase						    =  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						(				__lowerCamelCase   :     Optional[int]     ):



       """simple docstring"""





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

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

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

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

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

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

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

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

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

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

      _lowerCamelCase						    =  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)

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






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






