

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase_             =     logging.get_logger(__name__)
def   a   (    A__			:     Optional[int]=None   ,     A__			:     Any=None  )					->   Any:





      """simple docstring"""



      return field(default_factory=lambda: default   ,     metadata=A__  )







@dataclass
class 				__lowerCAmelCase  :
  _a		    =						list_field(
      default=[]    ,    metadata={
          """help""": (
              """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
              """ of all available models"""
          )
      }    ,    )

  _a		    =						list_field(
      default=[8]    ,    metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}							)

  _a		    =						list_field(
      default=[8, 32, 128, 512]    ,    metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}    ,    )

  _a		    =						field(
      default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}    ,    )
  _a		    =						field(
      default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}    ,    )
  _a		    =						field(
      default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}							)
  _a		    =						field(default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Use FP16 to accelerate inference."""}							)
  _a		    =						field(default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Benchmark training of model"""}							)
  _a		    =						field(default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Verbose memory tracing"""}							)
  _a		    =						field(
      default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}    ,    )
  _a		    =						field(
      default=SCREAMING_SNAKE_CASE    ,    metadata={
          """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
      }    ,    )
  _a		    =						field(default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Trace memory line by line"""}							)
  _a		    =						field(default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Save result to a CSV file"""}							)
  _a		    =						field(default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Save all print statements in a log file"""}							)
  _a		    =						field(default=SCREAMING_SNAKE_CASE    ,    metadata={"""help""": """Whether to print environment information"""}							)
  _a		    =						field(
      default=SCREAMING_SNAKE_CASE    ,    metadata={
          """help""": (
              """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
              """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
              """ for debugging / testing and on TPU."""
          )
      }    ,    )
  _a		    =						field(
      default=f"inference_time_{round(time()							)}.csv"    ,    metadata={"""help""": """CSV filename used if saving time results to csv."""}    ,    )
  _a		    =						field(
      default=f"inference_memory_{round(time()							)}.csv"    ,    metadata={"""help""": """CSV filename used if saving memory results to csv."""}    ,    )
  _a		    =						field(
      default=f"train_time_{round(time()							)}.csv"    ,    metadata={"""help""": """CSV filename used if saving time results to csv for training."""}    ,    )
  _a		    =						field(
      default=f"train_memory_{round(time()							)}.csv"    ,    metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}    ,    )
  _a		    =						field(
      default=f"env_info_{round(time()							)}.csv"    ,    metadata={"""help""": """CSV filename used if saving environment information."""}    ,    )
  _a		    =						field(
      default=f"log_{round(time()							)}.csv"    ,    metadata={"""help""": """Log filename used if print statements are saved in log."""}    ,    )
  _a		    =						field(default=3    ,    metadata={"""help""": """Times an experiment will be run."""}							)
  _a		    =						field(
      default=SCREAMING_SNAKE_CASE    ,    metadata={
          """help""": (
              """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
              """ model weights."""
          )
      }    ,    )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        warnings.warn(
            F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
            ' are deprecated in general and it is advised to use external Benchmarking libraries '
            ' to benchmark Transformer models.'							,     lowerCAmelCase							,     )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        return json.dumps(dataclasses.asdict(self      )							,     indent=2      )




  @property
  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        if len(self.models      ) <= 0:
              raise ValueError(
                  'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
                  ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'      )
        return self.models




  @property
  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        if not self.multi_process:
              return False
        elif self.is_tpu:
              logger.info('Multiprocessing is currently not possible on TPU.'      )
              return False
        else:
              return True





import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    level=os.environ.get('LOGLEVEL', 'INFO').upper(),
    stream=sys.stdout,
)

lowercase_             =     logging.getLogger(__name__)

lowercase_             =     {'facebook/bart-base': BartForConditionalGeneration}
lowercase_             =     {'facebook/bart-base': BartTokenizer}
def   a   (    )					->   Optional[Any]:





      """simple docstring"""



      _lowercase			 =argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'  )
      parser.add_argument(
          '--validation_file'   ,     type=A__   ,     default=A__   ,     help='A csv or a json file containing the validation data.'  )
      parser.add_argument(
          '--max_length'   ,     type=A__   ,     default=5   ,     help='The maximum total input sequence length after tokenization.'   ,     )
      parser.add_argument(
          '--num_beams'   ,     type=A__   ,     default=A__   ,     help=(
              'Number of beams to use for evaluation. This argument will be '
              'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
          )   ,     )
      parser.add_argument(
          '--model_name_or_path'   ,     type=A__   ,     help='Path to pretrained model or model identifier from huggingface.co/models.'   ,     required=A__   ,     )
      parser.add_argument(
          '--config_name'   ,     type=A__   ,     default=A__   ,     help='Pretrained config name or path if not the same as model_name'   ,     )
      parser.add_argument(
          '--device'   ,     type=A__   ,     default='cpu'   ,     help='Device where the model will be run'   ,     )
      parser.add_argument('--output_file_path'   ,     type=A__   ,     default=A__   ,     help='Where to store the final ONNX file.'  )

      _lowercase			 =parser.parse_args()

      return args
def   a   (    A__			:     int   ,     A__			:     Optional[int]="cpu"  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =model_dict[model_name].from_pretrained(A__  ).to(A__  )
      _lowercase			 =tokenizer_dict[model_name].from_pretrained(A__  )

      if model_name in ["facebook/bart-base"]:
            _lowercase			 =0
            _lowercase			 =None
            _lowercase			 =0

      return huggingface_model, tokenizer
def   a   (    A__			:     List[str]   ,     A__			:     Optional[Any]   ,     A__			:     List[Any]   ,     A__			:     Dict   ,     A__			:     Tuple  )					->   List[str]:





      """simple docstring"""



      model.eval()

      _lowercase			 =None
      _lowercase			 =torch.jit.script(BARTBeamSearchGenerator(A__  )  )

      with torch.no_grad():
            _lowercase			 ='My friends are cool but they eat too many carbs.'
            _lowercase			 =tokenizer([ARTICLE_TO_SUMMARIZE]   ,     max_length=1024   ,     return_tensors='pt'  ).to(model.device  )

            _lowercase			 =model.generate(
                inputs['input_ids']   ,     attention_mask=inputs['attention_mask']   ,     num_beams=A__   ,     max_length=A__   ,     early_stopping=A__   ,     decoder_start_token_id=model.config.decoder_start_token_id   ,     )

            torch.onnx.export(
                A__   ,     (
                    inputs['input_ids'],
                    inputs['attention_mask'],
                    num_beams,
                    max_length,
                    model.config.decoder_start_token_id,
                )   ,     A__   ,     opset_version=14   ,     input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']   ,     output_names=['output_ids']   ,     dynamic_axes={
                    'input_ids': {0: 'batch', 1: 'seq'},
                    'output_ids': {0: 'batch', 1: 'seq_out'},
                }   ,     example_outputs=A__   ,     )

            logger.info('Model exported to {}'.format(A__  )  )

            _lowercase			 =remove_dup_initializers(os.path.abspath(A__  )  )

            logger.info('Deduplicated and optimized model written to {}'.format(A__  )  )

            _lowercase			 =onnxruntime.InferenceSession(A__  )
            _lowercase			 =ort_sess.run(
                A__   ,     {
                    'input_ids': inputs['input_ids'].cpu().numpy(),
                    'attention_mask': inputs['attention_mask'].cpu().numpy(),
                    'num_beams': np.array(A__  ),
                    'max_length': np.array(A__  ),
                    'decoder_start_token_id': np.array(model.config.decoder_start_token_id  ),
                }   ,     )

            np.testing.assert_allclose(summary_ids.cpu().numpy()   ,     ort_out[0]   ,     rtol=1e-3   ,     atol=1e-3  )

            logger.info('Model outputs from torch and ONNX Runtime are similar.'  )
            logger.info('Success.'  )
def   a   (    )					->   int:





      """simple docstring"""



      _lowercase			 =parse_args()
      _lowercase			 =5
      _lowercase			 =4

      # 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   ,     )

      logger.setLevel(logging.INFO  )
      transformers.utils.logging.set_verbosity_error()

      _lowercase			 =torch.device(args.device  )

      _lowercase , _lowercase			 =load_model_tokenizer(args.model_name_or_path   ,     A__  )

      if model.config.decoder_start_token_id is None:
            raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'  )

      model.to(A__  )

      if args.max_length:
            _lowercase			 =args.max_length

      if args.num_beams:
            _lowercase			 =args.num_beams

      if args.output_file_path:
            _lowercase			 =args.output_file_path
      else:
            _lowercase			 ='BART.onnx'

      logger.info('Exporting model to ONNX'  )
      export_and_validate_model(A__   ,     A__   ,     A__   ,     A__   ,     A__  )


if __name__ == "__main__":
  main()


