





"""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__				:str				   =       logging.getLogger(__name__)

lowercase__				:Any				   =       {'facebook/bart-base': BartForConditionalGeneration}
lowercase__				:Optional[Any]				   =       {'facebook/bart-base': BartTokenizer}


def       lowerCamelCase_       (   )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						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.'''  )

  __UpperCAmelCase    :						Optional[Any]       =						parser.parse_args()

  return args


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_="cpu"  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						model_dict[model_name].from_pretrained(UpperCAmelCase_  ).to(UpperCAmelCase_  )
  __UpperCAmelCase    :						Any       =						tokenizer_dict[model_name].from_pretrained(UpperCAmelCase_  )

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

  return huggingface_model, tokenizer


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  model.eval()

  __UpperCAmelCase    :						Union[str, Any]       =						None
  __UpperCAmelCase    :						int       =						torch.jit.script(BARTBeamSearchGenerator(UpperCAmelCase_  )  )

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

    __UpperCAmelCase    :						List[Any]       =						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=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=UpperCAmelCase_  ,			)

    logger.info('''Model exported to {}'''.format(UpperCAmelCase_  )  )

    __UpperCAmelCase    :						Dict       =						remove_dup_initializers(os.path.abspath(UpperCAmelCase_  )  )

    logger.info('''Deduplicated and optimized model written to {}'''.format(UpperCAmelCase_  )  )

    __UpperCAmelCase    :						Optional[Any]       =						onnxruntime.InferenceSession(UpperCAmelCase_  )
    __UpperCAmelCase    :						Any       =						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       lowerCamelCase_       (   )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						parse_args()
  __UpperCAmelCase    :						Optional[int]       =						5
  __UpperCAmelCase    :						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    :						Tuple       =						torch.device(args.device  )

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

  if args.num_beams:
    __UpperCAmelCase    :						Optional[int]       =						args.num_beams

  if args.output_file_path:
    __UpperCAmelCase    :						int       =						args.output_file_path
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						'''BART.onnx'''

  logger.info('''Exporting model to ONNX'''  )
  export_and_validate_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )


if __name__ == "__main__":
  main()






"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__				:int				   =       TypeVar('T')




class 							snake_case							(			Generic[T] ):

      '''simple docstring'''



      def __init__(							self       :      Optional[Any]						,				__lowercase       :      list[T]						,				__lowercase       :      Callable[[T, T], T] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any | T       =						None

        __UpperCAmelCase    :						int       =						len(__lowercase )
        __UpperCAmelCase    :						list[T]       =						[any_type for _ in range(self.N )] + arr
        __UpperCAmelCase    :						List[Any]       =						fnc
        self.build()

      def       A_					(							self       :      str ):


        '''simple docstring'''




        for p in range(self.N - 1						,				0						,				-1 ):
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      int						,				__lowercase       :      T ):


        '''simple docstring'''




        p += self.N
        __UpperCAmelCase    :						Tuple       =						v
        while p > 1:
          __UpperCAmelCase    :						Union[str, Any]       =						p // 2
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )







      def       A_					(							self       :      str						,				__lowercase       :      int						,				__lowercase       :      int ):  # noqa: E741


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						l + self.N, r + self.N

        __UpperCAmelCase    :						T | None       =						None
        while l <= r:
          if l % 2 == 1:
            __UpperCAmelCase    :						Any       =						self.st[l] if res is None else self.fn(__lowercase						,				self.st[l] )
          if r % 2 == 0:
            __UpperCAmelCase    :						Any       =						self.st[r] if res is None else self.fn(__lowercase						,				self.st[r] )
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						(l + 1) // 2, (r - 1) // 2
        return res


if __name__ == "__main__":
  from functools import reduce

  lowercase__				:Any				   =       [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

  lowercase__				:List[Any]				   =       {
      0: 7,
      1: 2,
      2: 6,
      3: -1_4,
      4: 5,
      5: 4,
      6: 7,
      7: -1_0,
      8: 9,
      9: 1_0,
      1_0: 1_2,
      1_1: 1,
  }

  lowercase__				:List[str]				   =       SegmentTree(test_array, min)
  lowercase__				:List[str]				   =       SegmentTree(test_array, max)
  lowercase__				:Tuple				   =       SegmentTree(test_array, lambda a, b: a + b)


  def       lowerCamelCase_       (   )						->None:





    """simple docstring"""


    for i in range(len(UpperCAmelCase_  )  ):
      for j in range(UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ):
        __UpperCAmelCase    :						Optional[Any]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Optional[int]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Union[str, Any]       =						reduce(lambda UpperCAmelCase_  ,			UpperCAmelCase_  : a + b  ,			test_array[i : j + 1]  )
        assert min_range == min_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert max_range == max_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert sum_range == sum_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )

  test_all_segments()

  for index, value in test_updates.items():
    lowercase__				:Optional[Any]				   =       value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()