'''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,
)

__a									=   logging.getLogger(__name__)

__a									=   {'facebook/bart-base': BartForConditionalGeneration}
__a									=   {'facebook/bart-base': BartTokenizer}






def   __UpperCAmelCase     (    ):
   _UpperCAmelCase				:      int				=  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				:      Union[str, Any]				=  parser.parse_args()

   return args






def   __UpperCAmelCase     (    a_:			Any,   a_:			Optional[int]="cpu"				):
   _UpperCAmelCase				:      Tuple				=  model_dict[model_name].from_pretrained(_snake_case				).to(_snake_case				)
   _UpperCAmelCase				:      int				=  tokenizer_dict[model_name].from_pretrained(_snake_case				)

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

   return huggingface_model, tokenizer






def   __UpperCAmelCase     (    a_:			List[Any],   a_:			Optional[Any],   a_:			List[str],   a_:			List[str],   a_:			Dict				):
   model.eval()

   _UpperCAmelCase				:      Union[str, Any]				=  None
   _UpperCAmelCase				:      List[str]				=  torch.jit.script(BARTBeamSearchGenerator(_snake_case				)				)

   with torch.no_grad():
      _UpperCAmelCase				:      str				=  "My friends are cool but they eat too many carbs."
      _UpperCAmelCase				:      int				=  tokenizer([ARTICLE_TO_SUMMARIZE],   max_length=1_024,   return_tensors="pt"				).to(model.device				)

      _UpperCAmelCase				:      str				=  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				:      str				=  remove_dup_initializers(os.path.abspath(_snake_case				)				)

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

      _UpperCAmelCase				:      Optional[int]				=  onnxruntime.InferenceSession(_snake_case				)
      _UpperCAmelCase				:      List[Any]				=  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   __UpperCAmelCase     (    ):
   _UpperCAmelCase				:      Optional[int]				=  parse_args()
   _UpperCAmelCase				:      int				=  5
   _UpperCAmelCase				:      Optional[int]				=  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				:      Any				=  torch.device(args.device				)

   _UpperCAmelCase				,			_UpperCAmelCase				:      Tuple				=  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				:      Union[str, Any]				=  args.max_length

   if args.num_beams:
      _UpperCAmelCase				:      List[str]				=  args.num_beams

   if args.output_file_path:
      _UpperCAmelCase				:      str				=  args.output_file_path
   else:
      _UpperCAmelCase				:      Optional[int]				=  "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()







import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:str="sayef/fsner-bert-base-uncased"):



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

          return self.softmax(T * self.cos(snake_case,      snake_case))





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

          _lowercase              =W_supports['sizes'].tolist()
          _lowercase              =W_supports['start_token_id'].item()
          _lowercase              =W_supports['end_token_id'].item()

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

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

          _lowercase              =W_supports['input_ids'] == start_token_id
          _lowercase              =W_supports['input_ids'] == end_token_id

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

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

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

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

          return p_starts, p_ends


