



import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

lowercase_:  Dict     						=					logging.getLogger(__name__)



@dataclass
class 						lowercase__      :

     """simple docstring"""






     __UpperCamelCase    :	Optional[int]		=       field(
         default=1_2_8						,      metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }						,      )
     __UpperCamelCase    :	bool		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}						)
     __UpperCamelCase    :	bool		=       field(
         default=_lowerCamelCase						,      metadata={
             'help': (
                 'Whether to pad all samples to `max_seq_length`. '
                 'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
             )
         }						,      )
     __UpperCamelCase    :	Optional[int]		=       field(
         default=_lowerCamelCase						,      metadata={
             'help': (
                 'For debugging purposes or quicker training, truncate the number of training examples to this '
                 'value if set.'
             )
         }						,      )
     __UpperCamelCase    :	Optional[int]		=       field(
         default=_lowerCamelCase						,      metadata={
             'help': (
                 'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                 'value if set.'
             )
         }						,      )
     __UpperCamelCase    :	Optional[int]		=       field(
         default=_lowerCamelCase						,      metadata={
             'help': (
                 'For debugging purposes or quicker training, truncate the number of prediction examples to this '
                 'value if set.'
             )
         }						,      )



@dataclass
class 						lowercase__      :

     """simple docstring"""






     __UpperCamelCase    :	str		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
     __UpperCamelCase    :	str		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'}						)
     __UpperCamelCase    :	Optional[str]		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Train language if it is different from the evaluation language.'}						)
     __UpperCamelCase    :	Optional[str]		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
     __UpperCamelCase    :	Optional[str]		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
     __UpperCamelCase    :	Optional[str]		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,      )
     __UpperCamelCase    :	Optional[bool]		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}						,      )
     __UpperCamelCase    :	bool		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}						,      )
     __UpperCamelCase    :	str		=       field(
         default='main'						,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}						,      )
     __UpperCamelCase    :	bool		=       field(
         default=_lowerCamelCase						,      metadata={
             'help': (
                 'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                 'with private models).'
             )
         }						,      )
     __UpperCamelCase    :	bool		=       field(
         default=_lowerCamelCase						,      metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}						,      )

def 	_lowercase	(     ):




 """simple docstring"""




 snake_case__				:							Any      							= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
 snake_case__							,		snake_case__							,		snake_case__				:							Optional[Any]      							= parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry("""run_xnli"""						,   lowerCAmelCase__)

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""						,   datefmt="""%m/%d/%Y %H:%M:%S"""						,   handlers=[logging.StreamHandler(sys.stdout)]						,   )

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 snake_case__				:							List[str]      							= training_args.get_process_log_level()
 logger.setLevel(lowerCAmelCase__)
 datasets.utils.logging.set_verbosity(lowerCAmelCase__)
 transformers.utils.logging.set_verbosity(lowerCAmelCase__)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
     + F'distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}')
 logger.info(F'Training/evaluation parameters {training_args}')

 # Detecting last checkpoint.
 snake_case__				:							List[Any]      							= None
 if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
  snake_case__				:							int      							= get_last_checkpoint(training_args.output_dir)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
   raise ValueError(
       F'Output directory ({training_args.output_dir}) already exists and is not empty. '
       """Use --overwrite_output_dir to overcome.""")
  elif last_checkpoint is not None:
   logger.info(
       F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
       """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""")

    # Set seed before initializing model.
 set_seed(training_args.seed)

 # In distributed training, the load_dataset function guarantees that only one local process can concurrently
 # download the dataset.
 # Downloading and loading xnli dataset from the hub.
 if training_args.do_train:
  if model_args.train_language is None:
   snake_case__				:							List[str]      							= load_dataset(
       """xnli"""						,   model_args.language						,   split="""train"""						,   cache_dir=model_args.cache_dir						,   use_auth_token=True if model_args.use_auth_token else None						,   )
  else:
   snake_case__				:							Optional[int]      							= load_dataset(
       """xnli"""						,   model_args.train_language						,   split="""train"""						,   cache_dir=model_args.cache_dir						,   use_auth_token=True if model_args.use_auth_token else None						,   )
  snake_case__				:							str      							= train_dataset.features["""label"""].names

 if training_args.do_eval:
  snake_case__				:							int      							= load_dataset(
      """xnli"""						,   model_args.language						,   split="""validation"""						,   cache_dir=model_args.cache_dir						,   use_auth_token=True if model_args.use_auth_token else None						,   )
  snake_case__				:							Tuple      							= eval_dataset.features["""label"""].names

 if training_args.do_predict:
  snake_case__				:							Union[str, Any]      							= load_dataset(
      """xnli"""						,   model_args.language						,   split="""test"""						,   cache_dir=model_args.cache_dir						,   use_auth_token=True if model_args.use_auth_token else None						,   )
  snake_case__				:							str      							= predict_dataset.features["""label"""].names

 # Labels
 snake_case__				:							Optional[Any]      							= len(lowerCAmelCase__)

 # Load pretrained model and tokenizer
 # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 snake_case__				:							List[Any]      							= AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path						,   num_labels=lowerCAmelCase__						,   idalabel={str(lowerCAmelCase__): label for i, label in enumerate(lowerCAmelCase__)}						,   labelaid={label: i for i, label in enumerate(lowerCAmelCase__)}						,   finetuning_task="""xnli"""						,   cache_dir=model_args.cache_dir						,   revision=model_args.model_revision						,   use_auth_token=True if model_args.use_auth_token else None						,   )
 snake_case__				:							List[Any]      							= AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,   do_lower_case=model_args.do_lower_case						,   cache_dir=model_args.cache_dir						,   use_fast=model_args.use_fast_tokenizer						,   revision=model_args.model_revision						,   use_auth_token=True if model_args.use_auth_token else None						,   )
 snake_case__				:							str      							= AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path						,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path)						,   config=lowerCAmelCase__						,   cache_dir=model_args.cache_dir						,   revision=model_args.model_revision						,   use_auth_token=True if model_args.use_auth_token else None						,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,   )

 # Preprocessing the datasets
 # Padding strategy
 if data_args.pad_to_max_length:
  snake_case__				:							str      							= """max_length"""
 else:
  # We will pad later, dynamically at batch creation, to the max sequence length in each batch
  snake_case__				:							List[Any]      							= False

 def preprocess_function(UpperCAmelCase_):
  # Tokenize the texts
  return tokenizer(
      examples["""premise"""]						,   examples["""hypothesis"""]						,   padding=lowerCAmelCase__						,   max_length=data_args.max_seq_length						,   truncation=lowerCAmelCase__						,   )

 if training_args.do_train:
  if data_args.max_train_samples is not None:
   snake_case__				:							int      							= min(len(lowerCAmelCase__)						,   data_args.max_train_samples)
   snake_case__				:							Any      							= train_dataset.select(range(lowerCAmelCase__))
  with training_args.main_process_first(desc="""train dataset map pre-processing"""):
   snake_case__				:							List[Any]      							= train_dataset.map(
       lowerCAmelCase__						,   batched=lowerCAmelCase__						,   load_from_cache_file=not data_args.overwrite_cache						,   desc="""Running tokenizer on train dataset"""						,   )
  # Log a few random samples from the training set:
  for index in random.sample(range(len(lowerCAmelCase__))						,   3):
   logger.info(F'Sample {index} of the training set: {train_dataset[index]}.')

 if training_args.do_eval:
  if data_args.max_eval_samples is not None:
   snake_case__				:							Optional[int]      							= min(len(lowerCAmelCase__)						,   data_args.max_eval_samples)
   snake_case__				:							Union[str, Any]      							= eval_dataset.select(range(lowerCAmelCase__))
  with training_args.main_process_first(desc="""validation dataset map pre-processing"""):
   snake_case__				:							str      							= eval_dataset.map(
       lowerCAmelCase__						,   batched=lowerCAmelCase__						,   load_from_cache_file=not data_args.overwrite_cache						,   desc="""Running tokenizer on validation dataset"""						,   )

 if training_args.do_predict:
  if data_args.max_predict_samples is not None:
   snake_case__				:							Dict      							= min(len(lowerCAmelCase__)						,   data_args.max_predict_samples)
   snake_case__				:							Optional[Any]      							= predict_dataset.select(range(lowerCAmelCase__))
  with training_args.main_process_first(desc="""prediction dataset map pre-processing"""):
   snake_case__				:							Any      							= predict_dataset.map(
       lowerCAmelCase__						,   batched=lowerCAmelCase__						,   load_from_cache_file=not data_args.overwrite_cache						,   desc="""Running tokenizer on prediction dataset"""						,   )

    # Get the metric function
 snake_case__				:							Dict      							= evaluate.load("""xnli""")

 # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
 # predictions and label_ids field) and has to return a dictionary string to float.
 def compute_metrics(UpperCAmelCase_):
  snake_case__				:							Tuple      							= p.predictions[0] if isinstance(p.predictions						,   lowerCAmelCase__) else p.predictions
  snake_case__				:							Tuple      							= np.argmax(lowerCAmelCase__						,   axis=1)
  return metric.compute(predictions=lowerCAmelCase__						,   references=p.label_ids)

 # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
 if data_args.pad_to_max_length:
  snake_case__				:							Any      							= default_data_collator
 elif training_args.fpaa:
  snake_case__				:							Tuple      							= DataCollatorWithPadding(lowerCAmelCase__						,   pad_to_multiple_of=8)
 else:
  snake_case__				:							List[Any]      							= None

 # Initialize our Trainer
 snake_case__				:							Optional[Any]      							= Trainer(
     model=lowerCAmelCase__						,   args=lowerCAmelCase__						,   train_dataset=train_dataset if training_args.do_train else None						,   eval_dataset=eval_dataset if training_args.do_eval else None						,   compute_metrics=lowerCAmelCase__						,   tokenizer=lowerCAmelCase__						,   data_collator=lowerCAmelCase__						,   )

 # Training
 if training_args.do_train:
  snake_case__				:							int      							= None
  if training_args.resume_from_checkpoint is not None:
   snake_case__				:							Any      							= training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   snake_case__				:							Optional[int]      							= last_checkpoint
  snake_case__				:							Union[str, Any]      							= trainer.train(resume_from_checkpoint=lowerCAmelCase__)
  snake_case__				:							List[Any]      							= train_result.metrics
  snake_case__				:							Union[str, Any]      							= (
      data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase__)
  )
  snake_case__				:							Optional[Any]      							= min(lowerCAmelCase__						,   len(lowerCAmelCase__))

  trainer.save_model()  # Saves the tokenizer too for easy upload

  trainer.log_metrics("""train"""						,   lowerCAmelCase__)
  trainer.save_metrics("""train"""						,   lowerCAmelCase__)
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  logger.info("""*** Evaluate ***""")
  snake_case__				:							Any      							= trainer.evaluate(eval_dataset=lowerCAmelCase__)

  snake_case__				:							Union[str, Any]      							= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase__)
  snake_case__				:							Optional[int]      							= min(lowerCAmelCase__						,   len(lowerCAmelCase__))

  trainer.log_metrics("""eval"""						,   lowerCAmelCase__)
  trainer.save_metrics("""eval"""						,   lowerCAmelCase__)

 # Prediction
 if training_args.do_predict:
  logger.info("""*** Predict ***""")
  snake_case__							,		snake_case__							,		snake_case__				:							str      							= trainer.predict(lowerCAmelCase__						,   metric_key_prefix="""predict""")

  snake_case__				:							Any      							= (
      data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCAmelCase__)
  )
  snake_case__				:							str      							= min(lowerCAmelCase__						,   len(lowerCAmelCase__))

  trainer.log_metrics("""predict"""						,   lowerCAmelCase__)
  trainer.save_metrics("""predict"""						,   lowerCAmelCase__)

  snake_case__				:							Optional[Any]      							= np.argmax(lowerCAmelCase__						,   axis=1)
  snake_case__				:							Union[str, Any]      							= os.path.join(training_args.output_dir						,   """predictions.txt""")
  if trainer.is_world_process_zero():
   with open(lowerCAmelCase__						,   """w""") as writer:
    writer.write("""index\tprediction\n""")
    for index, item in enumerate(lowerCAmelCase__):
     snake_case__				:							List[Any]      							= label_list[item]
     writer.write(F'{index}\t{item}\n')


if __name__ == "__main__":
 main()









import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =sess.run(
				    None,
				    {
				        sess.get_inputs()[0].name: input_ids,
				        sess.get_inputs()[1].name: attention_mask,
				        sess.get_inputs()[2].name: token_type_ids,
				    },
				    run_options=run_opt,
				)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))