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     :  Optional[int]					     =       logging.getLogger(__name__)

A     :  Any					     =       {"facebook/bart-base": BartForConditionalGeneration}
A     :  Tuple					     =       {"facebook/bart-base": BartTokenizer}


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  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."				)

      SCREAMING_SNAKE_CASE_          =  parser.parse_args()

      return args


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase="cpu"				):
      SCREAMING_SNAKE_CASE_          =  model_dict[model_name].from_pretrained(__UpperCamelCase				).to(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  tokenizer_dict[model_name].from_pretrained(__UpperCamelCase				)

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

      return huggingface_model, tokenizer


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      model.eval()

      SCREAMING_SNAKE_CASE_          =  None
      SCREAMING_SNAKE_CASE_          =  torch.jit.script(BARTBeamSearchGenerator(__UpperCamelCase				)				)

      with torch.no_grad():
            SCREAMING_SNAKE_CASE_          =  "My friends are cool but they eat too many carbs."
            SCREAMING_SNAKE_CASE_          =  tokenizer([ARTICLE_TO_SUMMARIZE]     ,      max_length=1_0_2_4     ,      return_tensors="pt"				).to(model.device				)

            SCREAMING_SNAKE_CASE_          =  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=1_4     ,      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				)				)

            SCREAMING_SNAKE_CASE_          =  remove_dup_initializers(os.path.abspath(__UpperCamelCase				)				)

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

            SCREAMING_SNAKE_CASE_          =  onnxruntime.InferenceSession(__UpperCamelCase				)
            SCREAMING_SNAKE_CASE_          =  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      a__ (		):
      SCREAMING_SNAKE_CASE_          =  parse_args()
      SCREAMING_SNAKE_CASE_          =  5
      SCREAMING_SNAKE_CASE_          =  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()

      SCREAMING_SNAKE_CASE_          =  torch.device(args.device				)

      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  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:
            SCREAMING_SNAKE_CASE_          =  args.max_length

      if args.num_beams:
            SCREAMING_SNAKE_CASE_          =  args.num_beams

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

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


if __name__ == "__main__":
      main()



import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


A     :  Any					     =       logging.getLogger(__name__)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Optional[Any]=-1   )  ->	Optional[Any]:
        # in NER datasets, the last column is usually reserved for NER label
        SCREAMING_SNAKE_CASE_          =  label_idx







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []
        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              SCREAMING_SNAKE_CASE_          =  []
              SCREAMING_SNAKE_CASE_          =  []
              for line in f:
                    if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                          if words:
                                examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                                guid_index += 1
                                SCREAMING_SNAKE_CASE_          =  []
                                SCREAMING_SNAKE_CASE_          =  []
                    else:
                          SCREAMING_SNAKE_CASE_          =  line.split(" "   )
                          words.append(splits[0]   )
                          if len(__magic_name__   ) > 1:
                                labels.append(splits[self.label_idx].replace("\n"  ,     ""   )   )
                          else:
                                # Examples could have no label for mode = "test"
                                labels.append("O"   )
              if words:
                    examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
        return examples







  def    __A     (							self :       Tuple  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for line in test_input_reader:
              if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                    writer.write(__magic_name__   )
                    if not preds_list[example_id]:
                          example_id += 1
              elif preds_list[example_id]:
                    SCREAMING_SNAKE_CASE_          =  line.split()[0] + " " + preds_list[example_id].pop(0   ) + "\n"
                    writer.write(__magic_name__   )
              else:
                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."  ,     line.split()[0]   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Optional[int]   )  ->	str:
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2   )







  def    __A     (							self :       Any  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return [
                  "O",
                  "B-ADVP",
                  "B-INTJ",
                  "B-LST",
                  "B-PRT",
                  "B-NP",
                  "B-SBAR",
                  "B-VP",
                  "B-ADJP",
                  "B-CONJP",
                  "B-PP",
                  "I-ADVP",
                  "I-INTJ",
                  "I-LST",
                  "I-PRT",
                  "I-NP",
                  "I-SBAR",
                  "I-VP",
                  "I-ADJP",
                  "I-CONJP",
                  "I-PP",
              ]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []

        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              for sentence in parse_incr(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  []
                    SCREAMING_SNAKE_CASE_          =  []
                    for token in sentence:
                          words.append(token["form"]   )
                          labels.append(token["upos"]   )
                    assert len(__magic_name__   ) == len(__magic_name__   )
                    if words:
                          examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                          guid_index += 1
        return examples







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for sentence in parse_incr(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  preds_list[example_id]
              SCREAMING_SNAKE_CASE_          =  ""
              for token in sentence:
                    out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0   )}) '''
              out += "\n"
              writer.write(__magic_name__   )
              example_id += 1







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    return f.read().splitlines()
        else:
              return [
                  "ADJ",
                  "ADP",
                  "ADV",
                  "AUX",
                  "CCONJ",
                  "DET",
                  "INTJ",
                  "NOUN",
                  "NUM",
                  "PART",
                  "PRON",
                  "PROPN",
                  "PUNCT",
                  "SCONJ",
                  "SYM",
                  "VERB",
                  "X",
              ]


