


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			(      ):
						lowerCAmelCase_	:     int          = 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.")

						lowerCAmelCase_	:     Tuple          = parser.parse_args()

						return args

def   UpperCamelCase			(      snake_case__						,  snake_case__="cpu"):
						lowerCAmelCase_	:     List[Any]          = model_dict[model_name].from_pretrained(__lowerCAmelCase).to(__lowerCAmelCase)
						lowerCAmelCase_	:     Any          = tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase)

						if model_name in ["facebook/bart-base"]:
												lowerCAmelCase_	:     str          = 0
												lowerCAmelCase_	:     str          = None
												lowerCAmelCase_	:     Optional[int]          = 0

						return huggingface_model, tokenizer

def   UpperCamelCase			(      snake_case__						,  snake_case__						,  snake_case__						,  snake_case__						,  snake_case__):
						model.eval()

						lowerCAmelCase_	:     List[str]          = None
						lowerCAmelCase_	:     List[Any]          = torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase))

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

												lowerCAmelCase_	:     Dict          = 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))

												lowerCAmelCase_	:     Any          = remove_dup_initializers(os.path.abspath(__lowerCAmelCase))

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

												lowerCAmelCase_	:     int          = onnxruntime.InferenceSession(__lowerCAmelCase)
												lowerCAmelCase_	:     List[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   UpperCamelCase			(      ):
						lowerCAmelCase_	:     Tuple          = parse_args()
						lowerCAmelCase_	:     Dict          = 5
						lowerCAmelCase_	:     Union[str, Any]          = 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()

						lowerCAmelCase_	:     List[str]          = torch.device(args.device)

						lowerCAmelCase_    ,					lowerCAmelCase_	:     str          = 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:
												lowerCAmelCase_	:     int          = args.max_length

						if args.num_beams:
												lowerCAmelCase_	:     Union[str, Any]          = args.num_beams

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

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


if __name__ == "__main__":
							main()




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



