
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_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',
)

__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 << 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)
												__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 				a						(							snake_case__:					str						,   snake_case__:					Dict						,   snake_case__:					Tuple						,   snake_case__:					Any						,   snake_case__:					Tuple						,   snake_case__:					Optional[int]						,   snake_case__:					Optional[int]						,   snake_case__:					Optional[Any]					):


				'''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.
__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 				a						(							snake_case__:					Dict					):


				'''simple docstring'''
				# Some of the questions have lots of whitespace on the left, which is not useful and will make the
				# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
				# left whitespace
				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


__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 				a						(							snake_case__:					Tuple						,   snake_case__:					Optional[int]						,   snake_case__:					Optional[int]						,   snake_case__:					Optional[Any]="eval"					):


				'''simple docstring'''
				# Post-processing: we match the start logits and end logits to answers in the original context.
				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_					)


__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 				a						(							snake_case__:					int					):


										'''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_0_0)
												__a  			=					accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

												__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_0_0)

						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 * 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)

__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}")











import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


snake_case_				=			logging.getLogger(__name__)

@dataclass(frozen=_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				str
      A_						:				str
      A_						:				Optional[str]							=    None
      A_						:				Optional[str]							=    None
      A_						:				Optional[str]							=    None






@dataclass(frozen=_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				List[int]
      A_						:				Optional[List[int]]							=    None
      A_						:				Optional[List[int]]							=    None
      A_						:				Optional[Union[int, float]]							=    None
      A_						:				Optional[int]							=    None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset

    class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
          A_						:				List[InputFeatures]


          def __init__(self       :      int				,			a__       :      str				,			a__       :      PreTrainedTokenizer				,			a__       :      str				,			a__       :      Optional[int] = None				,			a__       :      List[Any]=False				,			a__       :      bool = False				,			):





             """simple docstring"""



             __snake_case							       =   hans_processors[task]()

             __snake_case							       =   os.path.join(
                 a__				,			'''cached_{}_{}_{}_{}'''.format(
                     '''dev''' if evaluate else '''train'''				,			tokenizer.__class__.__name__				,			str(a__      )				,			a__				,			)				,			)
             __snake_case							       =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                __snake_case       ,	__snake_case							       =   label_list[2], label_list[1]
             __snake_case							       =   label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             __snake_case							       =   cached_features_file + '''.lock'''
             with FileLock(a__      ):
                if os.path.exists(a__      ) and not overwrite_cache:
                   logger.info(f"""Loading features from cached file {cached_features_file}"""      )
                   __snake_case							       =   torch.load(a__      )
                else:
                   logger.info(f"""Creating features from dataset file at {data_dir}"""      )

                   __snake_case							       =   (
                       processor.get_dev_examples(a__      ) if evaluate else processor.get_train_examples(a__      )
                   )

                   logger.info('''Training examples: %s'''				,			len(a__      )      )
                   __snake_case							       =   hans_convert_examples_to_features(a__				,			a__				,			a__				,			a__      )
                   logger.info('''Saving features into cached file %s'''				,			a__      )
                   torch.save(self.features				,			a__      )


          def __len__(self       :      int      ):





             """simple docstring"""



             return len(self.features      )


          def __getitem__(self       :      Dict				,			a__       :      List[Any]      ):





             """simple docstring"""



             return self.features[i]


          def    a (self       :      List[Any]      ):





             """simple docstring"""



             return self.label_list


if is_tf_available():
    import tensorflow as tf

    class 		SCREAMING_SNAKE_CASE__				:
          A_						:				List[InputFeatures]


          def __init__(self       :      Tuple				,			a__       :      str				,			a__       :      PreTrainedTokenizer				,			a__       :      str				,			a__       :      Optional[int] = 128				,			a__       :      Any=False				,			a__       :      bool = False				,			):





             """simple docstring"""



             __snake_case							       =   hans_processors[task]()
             __snake_case							       =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                __snake_case       ,	__snake_case							       =   label_list[2], label_list[1]
             __snake_case							       =   label_list

             __snake_case							       =   processor.get_dev_examples(a__      ) if evaluate else processor.get_train_examples(a__      )
             __snake_case							       =   hans_convert_examples_to_features(a__				,			a__				,			a__				,			a__      )

             def gen():
                for ex_index, ex in tqdm.tqdm(enumerate(self.features      )				,			desc='''convert examples to features'''      ):
                   if ex_index % 1_0000 == 0:
                      logger.info('''Writing example %d of %d''' % (ex_index, len(a__      ))      )

                   yield (
                       {
                           "example_id": 0,
                           "input_ids": ex.input_ids,
                           "attention_mask": ex.attention_mask,
                           "token_type_ids": ex.token_type_ids,
                       },
                       ex.label,
                   )

             __snake_case							       =   tf.data.Dataset.from_generator(
                 a__				,			(
                     {
                         '''example_id''': tf.intaa,
                         '''input_ids''': tf.intaa,
                         '''attention_mask''': tf.intaa,
                         '''token_type_ids''': tf.intaa,
                     },
                     tf.intaa,
                 )				,			(
                     {
                         '''example_id''': tf.TensorShape([]      ),
                         '''input_ids''': tf.TensorShape([None, None]      ),
                         '''attention_mask''': tf.TensorShape([None, None]      ),
                         '''token_type_ids''': tf.TensorShape([None, None]      ),
                     },
                     tf.TensorShape([]      ),
                 )				,			)


          def    a (self       :      Union[str, Any]      ):





             """simple docstring"""



             return self.dataset


          def __len__(self       :      Dict      ):





             """simple docstring"""



             return len(self.features      )


          def __getitem__(self       :      Any				,			a__       :      Dict      ):





             """simple docstring"""



             return self.features[i]


          def    a (self       :      str      ):





             """simple docstring"""



             return self.label_list

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def    a (self       :      Dict				,			a__       :      Dict      ):





         """simple docstring"""



         return self._create_examples(self._read_tsv(os.path.join(a__				,			'''heuristics_train_set.txt'''      )      )				,			'''train'''      )


      def    a (self       :      Optional[int]				,			a__       :      Tuple      ):





         """simple docstring"""



         return self._create_examples(self._read_tsv(os.path.join(a__				,			'''heuristics_evaluation_set.txt'''      )      )				,			'''dev'''      )


      def    a (self       :      int      ):





         """simple docstring"""



         return ["contradiction", "entailment", "neutral"]


      def    a (self       :      Any				,			a__       :      Optional[int]				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   []
         for i, line in enumerate(a__      ):
            if i == 0:
               continue
            __snake_case							       =   '''%s-%s''' % (set_type, line[0])
            __snake_case							       =   line[5]
            __snake_case							       =   line[6]
            __snake_case							       =   line[7][2:] if line[7].startswith('''ex'''      ) else line[7]
            __snake_case							       =   line[0]
            examples.append(InputExample(guid=a__				,			text_a=a__				,			text_b=a__				,			label=a__				,			pairID=a__      )      )
         return examples






def lowerCamelCase__    (							snake_case_	:  List[InputExample]       ,      snake_case_	:  List[str]       ,      snake_case_	:  int       ,      snake_case_	:  PreTrainedTokenizer       ,      )      ->				List[str]:
   __snake_case							       =   {label: i for i, label in enumerate(snake_case_       )}

   __snake_case							       =   []
   for ex_index, example in tqdm.tqdm(enumerate(snake_case_       )       ,      desc='''convert examples to features'''       ):
      if ex_index % 1_0000 == 0:
         logger.info('''Writing example %d''' % (ex_index)       )

      __snake_case							       =   tokenizer(
          example.text_a       ,      example.text_b       ,      add_special_tokens=snake_case_       ,      max_length=snake_case_       ,      padding='''max_length'''       ,      truncation=snake_case_       ,      return_overflowing_tokens=snake_case_       ,      )

      __snake_case							       =   label_map[example.label] if example.label in label_map else 0

      __snake_case							       =   int(example.pairID       )

      features.append(InputFeatures(**snake_case_       ,      label=snake_case_       ,      pairID=snake_case_       )       )

   for i, example in enumerate(examples[:5]       ):
      logger.info('''*** Example ***'''       )
      logger.info(f"""guid: {example}"""       )
      logger.info(f"""features: {features[i]}"""       )

   return features


snake_case_				=			{
    'hans': 3,
}







snake_case_				=			{
    'hans': HansProcessor,
}

