


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,
)

snake_case				:    Any       				=     logging.getLogger(__name__)

snake_case				:    Optional[int]       				=     {'''facebook/bart-base''': BartForConditionalGeneration}
snake_case				:    List[str]       				=     {'''facebook/bart-base''': BartTokenizer}
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:List[Any] 							=       argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''						)
      parser.add_argument(
          '''--validation_file''' ,  type=UpperCAmelCase_ ,  default=UpperCAmelCase_ ,  help='''A csv or a json file containing the validation data.'''						)
      parser.add_argument(
          '''--max_length''' ,  type=UpperCAmelCase_ ,  default=5 ,  help='''The maximum total input sequence length after tokenization.''' ,  )
      parser.add_argument(
          '''--num_beams''' ,  type=UpperCAmelCase_ ,  default=UpperCAmelCase_ ,  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=UpperCAmelCase_ ,  help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,  required=UpperCAmelCase_ ,  )
      parser.add_argument(
          '''--config_name''' ,  type=UpperCAmelCase_ ,  default=UpperCAmelCase_ ,  help='''Pretrained config name or path if not the same as model_name''' ,  )
      parser.add_argument(
          '''--device''' ,  type=UpperCAmelCase_ ,  default='''cpu''' ,  help='''Device where the model will be run''' ,  )
      parser.add_argument('''--output_file_path''' ,  type=UpperCAmelCase_ ,  default=UpperCAmelCase_ ,  help='''Where to store the final ONNX file.'''						)

      a						:Dict 							=       parser.parse_args()

      return args
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[Any]="cpu"						):




      """simple docstring"""





      a						:Optional[Any] 							=       model_dict[model_name].from_pretrained(UpperCAmelCase_						).to(UpperCAmelCase_						)
      a						:Tuple 							=       tokenizer_dict[model_name].from_pretrained(UpperCAmelCase_						)

      if model_name in ["facebook/bart-base"]:
            a						:str 							=       0
            a						:Optional[Any] 							=       None
            a						:Dict 							=       0

      return huggingface_model, tokenizer
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		Tuple ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      model.eval()

      a						:Tuple 							=       None
      a						:Dict 							=       torch.jit.script(BARTBeamSearchGenerator(UpperCAmelCase_						)						)

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

            a						:Tuple 							=       model.generate(
                inputs['''input_ids'''] ,  attention_mask=inputs['''attention_mask'''] ,  num_beams=UpperCAmelCase_ ,  max_length=UpperCAmelCase_ ,  early_stopping=UpperCAmelCase_ ,  decoder_start_token_id=model.config.decoder_start_token_id ,  )

            torch.onnx.export(
                UpperCAmelCase_ ,  (
                    inputs['''input_ids'''],
                    inputs['''attention_mask'''],
                    num_beams,
                    max_length,
                    model.config.decoder_start_token_id,
                ) ,  UpperCAmelCase_ ,  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=UpperCAmelCase_ ,  )

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

            a						:Dict 							=       remove_dup_initializers(os.path.abspath(UpperCAmelCase_						)						)

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

            a						:List[str] 							=       onnxruntime.InferenceSession(UpperCAmelCase_						)
            a						:List[str] 							=       ort_sess.run(
                UpperCAmelCase_ ,  {
                    '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                    '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                    '''num_beams''': np.array(UpperCAmelCase_						),
                    '''max_length''': np.array(UpperCAmelCase_						),
                    '''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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:List[Any] 							=       parse_args()
      a						:str 							=       5
      a						:Optional[Any] 							=       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()

      a						:Any 							=       torch.device(args.device						)

      a							,				a						:str 							=       load_model_tokenizer(args.model_name_or_path ,  UpperCAmelCase_						)

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

      model.to(UpperCAmelCase_						)

      if args.max_length:
            a						:List[str] 							=       args.max_length

      if args.num_beams:
            a						:Any 							=       args.num_beams

      if args.output_file_path:
            a						:Any 							=       args.output_file_path
      else:
            a						:Dict 							=       '''BART.onnx'''

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


if __name__ == "__main__":
     main()









def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      while b:
            a							,				a						:Optional[Any] 							=       b, a % b
      return a
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_ ,  a % b						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 ,  5						)}'''						)
      print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 ,  3						)}'''						)
      print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 ,  3						)}'''						)
      print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 ,  6						)}'''						)
      print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 ,  3						)}'''						)

      print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,  5						)}'''						)
      print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,  6						)}'''						)
      print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,  3						)}'''						)


if __name__ == "__main__":
     main()





