'''simple docstring'''





import torch

from transformers import AutoModel




class 	__A				(			torch.nn.Module     ):



    def __init__(self   :   Tuple       ,      __a   :   List[Any]="sayef/fsner-bert-base-uncased"			):
         super(__a       ,      self			).__init__()

         UpperCAmelCase_	   =       AutoModel.from_pretrained(__a       ,      return_dict=__a			)
         UpperCAmelCase_	   =       torch.nn.CosineSimilarity(3       ,      1E-08			)
         UpperCAmelCase_	   =       torch.nn.Softmax(dim=1			)



    def  _lowercase						(self   :   int       ,      **__a   :   List[str]			):
         return self.bert(**__a			).last_hidden_state



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Tuple			):
         return token_embeddings.sum(2       ,      keepdim=__a			)



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Optional[int]       ,      __a   :   Tuple       ,      __a   :   int=1			):
         return self.softmax(T * self.cos(__a       ,      __a			)			)






    def  _lowercase						(self   :   int       ,      __a   :   Union[str, Any]       ,      __a   :   Optional[Any]			):
         UpperCAmelCase_	   =       W_supports["sizes"].tolist()
         UpperCAmelCase_	   =       W_supports["start_token_id"].item()
         UpperCAmelCase_	   =       W_supports["end_token_id"].item()

         del W_supports["sizes"]
         del W_supports["start_token_id"]
         del W_supports["end_token_id"]

         UpperCAmelCase_	   =       self.BERT(**__a			)
         UpperCAmelCase_	   =       self.BERT(**__a			)

         UpperCAmelCase_	   =       None
         UpperCAmelCase_	   =       None

         UpperCAmelCase_	   =       W_supports["input_ids"] == start_token_id
         UpperCAmelCase_	   =       W_supports["input_ids"] == end_token_id

         for i, size in enumerate(__a			):
              if i == 0:
                   UpperCAmelCase_	   =       0
              else:
                   UpperCAmelCase_	   =       support_sizes[i - 1]

              UpperCAmelCase_	   =       S[s : s + size][start_token_masks[s : s + size]]
              UpperCAmelCase_	   =       S[s : s + size][end_token_masks[s : s + size]]

              UpperCAmelCase_	   =       torch.matmul(q[i]       ,      s_start.T			).sum(1			).softmax(0			)
              UpperCAmelCase_	   =       torch.matmul(q[i]       ,      s_end.T			).sum(1			).softmax(0			)

              if p_starts is not None:
                   UpperCAmelCase_	   =       torch.vstack((p_starts, p_start)			)
                   UpperCAmelCase_	   =       torch.vstack((p_ends, p_end)			)
              else:
                   UpperCAmelCase_	   =       p_start
                   UpperCAmelCase_	   =       p_end

         return p_starts, p_ends





'''simple docstring'''





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,
)

SCREAMING_SNAKE_CASE_:					Tuple  =logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_:					Any  ={'facebook/bart-base': BartForConditionalGeneration}
SCREAMING_SNAKE_CASE_:					int  ={'facebook/bart-base': BartTokenizer}





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




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

     UpperCAmelCase_	   =       parser.parse_args()

     return args





def      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			int="cpu"      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       model_dict[model_name].from_pretrained(snake_case_      ).to(snake_case_      )
     UpperCAmelCase_	   =       tokenizer_dict[model_name].from_pretrained(snake_case_      )

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

     return huggingface_model, tokenizer





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			int ,   snake_case_	:			Dict      )   ->				Dict:


     '''simple docstring'''




     model.eval()

     UpperCAmelCase_	   =       None
     UpperCAmelCase_	   =       torch.jit.script(BARTBeamSearchGenerator(snake_case_      )      )

     with torch.no_grad():
          UpperCAmelCase_	   =       "My friends are cool but they eat too many carbs."
          UpperCAmelCase_	   =       tokenizer([ARTICLE_TO_SUMMARIZE] ,   max_length=10_24 ,   return_tensors="pt"      ).to(model.device      )

          UpperCAmelCase_	   =       model.generate(
              inputs["input_ids"] ,   attention_mask=inputs["attention_mask"] ,   num_beams=snake_case_ ,   max_length=snake_case_ ,   early_stopping=snake_case_ ,   decoder_start_token_id=model.config.decoder_start_token_id ,   )

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

          logger.info("Model exported to {}".format(snake_case_      )      )

          UpperCAmelCase_	   =       remove_dup_initializers(os.path.abspath(snake_case_      )      )

          logger.info("Deduplicated and optimized model written to {}".format(snake_case_      )      )

          UpperCAmelCase_	   =       onnxruntime.InferenceSession(snake_case_      )
          UpperCAmelCase_	   =       ort_sess.run(
              snake_case_ ,   {
                  "input_ids": inputs["input_ids"].cpu().numpy(),
                  "attention_mask": inputs["attention_mask"].cpu().numpy(),
                  "num_beams": np.array(snake_case_      ),
                  "max_length": np.array(snake_case_      ),
                  "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_    ( )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       parse_args()
     UpperCAmelCase_	   =       5
     UpperCAmelCase_	   =       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()

     UpperCAmelCase_	   =       torch.device(args.device      )

     UpperCAmelCase_   ,			UpperCAmelCase_	   =       load_model_tokenizer(args.model_name_or_path ,   snake_case_      )

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

     model.to(snake_case_      )

     if args.max_length:
          UpperCAmelCase_	   =       args.max_length

     if args.num_beams:
          UpperCAmelCase_	   =       args.num_beams

     if args.output_file_path:
          UpperCAmelCase_	   =       args.output_file_path
     else:
          UpperCAmelCase_	   =       "BART.onnx"

     logger.info("Exporting model to ONNX"      )
     export_and_validate_model(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )


if __name__ == "__main__":
    main()




