

'''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,
)

_snake_case      =    logging.getLogger(__name__)

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







def    _A						(      )     ->      str:
   _lowercase     : str     	=  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."	)

   _lowercase     : int     	=  parser.parse_args()

   return args







def    _A						(      snake_case						,				snake_case="cpu"	)     ->      Optional[Any]:
   _lowercase     : Optional[int]     	=  model_dict[model_name].from_pretrained(lowerCamelCase__	).to(lowerCamelCase__	)
   _lowercase     : Dict     	=  tokenizer_dict[model_name].from_pretrained(lowerCamelCase__	)

   if model_name in ["facebook/bart-base"]:
      _lowercase     : Union[str, Any]     	=  0
      _lowercase     : Any     	=  None
      _lowercase     : Dict     	=  0

   return huggingface_model, tokenizer







def    _A						(      snake_case						,				snake_case						,				snake_case						,				snake_case						,				snake_case	)     ->      Any:
   model.eval()

   _lowercase     : Tuple     	=  None
   _lowercase     : Tuple     	=  torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase__	)	)

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

      _lowercase     : Tuple     	=  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__	)	)

      _lowercase     : Dict     	=  remove_dup_initializers(os.path.abspath(lowerCamelCase__	)	)

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

      _lowercase     : Union[str, Any]     	=  onnxruntime.InferenceSession(lowerCamelCase__	)
      _lowercase     : Optional[Any]     	=  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    _A						(      )     ->      List[Any]:
   _lowercase     : Union[str, Any]     	=  parse_args()
   _lowercase     : Tuple     	=  5
   _lowercase     : Dict     	=  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     : Optional[int]     	=  torch.device(args.device	)

   _lowercase	, _lowercase     : Any     	=  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:
      _lowercase     : Optional[int]     	=  args.max_length

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

   if args.output_file_path:
      _lowercase     : str     	=  args.output_file_path
   else:
      _lowercase     : List[Any]     	=  "BART.onnx"

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


if __name__ == "__main__":
    main()










import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

