


'''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,
)

lowercase__				     =		logging.getLogger(__name__)

lowercase__				     =		{"facebook/bart-base": BartForConditionalGeneration}
lowercase__				     =		{"facebook/bart-base": BartTokenizer}






def __UpperCamelCase   (			)      ->     int:







     '''simple docstring'''






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

     _a						=		parser.parse_args()

     return args






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: str="cpu"				)      ->     Dict:







     '''simple docstring'''






     _a						=		model_dict[model_name].from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
     _a						=		tokenizer_dict[model_name].from_pretrained(__lowerCamelCase				)

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

     return huggingface_model, tokenizer






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: int				)      ->     Dict:







     '''simple docstring'''






     model.eval()

     _a						=		None
     _a						=		torch.jit.script(BARTBeamSearchGenerator(__lowerCamelCase				)				)

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

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

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

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

          _a						=		remove_dup_initializers(os.path.abspath(__lowerCamelCase				)				)

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

          _a						=		onnxruntime.InferenceSession(__lowerCamelCase				)
          _a						=		ort_sess.run(
              __lowerCamelCase      ,  {
                  "input_ids": inputs["input_ids"].cpu().numpy(),
                  "attention_mask": inputs["attention_mask"].cpu().numpy(),
                  "num_beams": np.array(__lowerCamelCase				),
                  "max_length": np.array(__lowerCamelCase				),
                  "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   (			)      ->     Union[str, Any]:







     '''simple docstring'''






     _a						=		parse_args()
     _a						=		5
     _a						=		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						=		torch.device(args.device				)

     _a			,			_a						=		load_model_tokenizer(args.model_name_or_path      ,  __lowerCamelCase				)

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

     model.to(__lowerCamelCase				)

     if args.max_length:
          _a						=		args.max_length

     if args.num_beams:
          _a						=		args.num_beams

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

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


if __name__ == "__main__":
   main()





'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a			,			_a						=		9, 14  # noqa: F841
     _a						=		[
         [0, 1, 4],
         [0, 7, 8],
         [1, 2, 8],
         [7, 8, 7],
         [7, 6, 1],
         [2, 8, 2],
         [8, 6, 6],
         [2, 3, 7],
         [2, 5, 4],
         [6, 5, 2],
         [3, 5, 14],
         [3, 4, 9],
         [5, 4, 10],
         [1, 7, 11],
     ]

     _a						=		defaultdict(__lowerCamelCase				)
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]				)
          adjancency[nodea].append([nodea, cost]				)

     _a						=		mst(__lowerCamelCase				)

     _a						=		[
         [7, 6, 1],
         [2, 8, 2],
         [6, 5, 2],
         [0, 1, 4],
         [2, 5, 4],
         [2, 3, 7],
         [0, 7, 8],
         [3, 4, 9],
     ]

     for answer in expected:
          _a						=		tuple(answer[:2]				)
          _a						=		tuple(edge[::-1]				)
          assert edge in result or reverse in result

