

"""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,
)

lowerCAmelCase_	:    List[str]            =      logging.getLogger(__name__)

lowerCAmelCase_	:    int            =      {'''facebook/bart-base''': BartForConditionalGeneration}
lowerCAmelCase_	:    Dict            =      {'''facebook/bart-base''': BartTokenizer}






def       _lowerCAmelCase       (      ):







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

       UpperCAmelCase	    =			parser.parse_args()

       return args






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase="cpu"		):







       '''simple docstring'''
       UpperCAmelCase	    =			model_dict[model_name].from_pretrained(_A		).to(_A		)
       UpperCAmelCase	    =			tokenizer_dict[model_name].from_pretrained(_A		)

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

       return huggingface_model, tokenizer






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       model.eval()

       UpperCAmelCase	    =			None
       UpperCAmelCase	    =			torch.jit.script(BARTBeamSearchGenerator(_A		)		)

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

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

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

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

              UpperCAmelCase	    =			remove_dup_initializers(os.path.abspath(_A		)		)

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

              UpperCAmelCase	    =			onnxruntime.InferenceSession(_A		)
              UpperCAmelCase	    =			ort_sess.run(
                  _A	,      {
                      """input_ids""": inputs["""input_ids"""].cpu().numpy(),
                      """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
                      """num_beams""": np.array(_A		),
                      """max_length""": np.array(_A		),
                      """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       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			parse_args()
       UpperCAmelCase	    =			5
       UpperCAmelCase	    =			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	    =			torch.device(args.device		)

       UpperCAmelCase    ,       UpperCAmelCase	    =			load_model_tokenizer(args.model_name_or_path	,      _A		)

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

       model.to(_A		)

       if args.max_length:
              UpperCAmelCase	    =			args.max_length

       if args.num_beams:
              UpperCAmelCase	    =			args.num_beams

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

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


if __name__ == "__main__":
 main()








"""simple docstring"""







def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			len(lowerCAmelCase		)

       for i in range(n - 1		):
              for j in range(i + 1	,      lowerCAmelCase		):
                     if arr[i] > arr[j]:
                            num_inversions += 1

       return num_inversions






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       if len(lowerCAmelCase		) <= 1:
              return arr, 0
       UpperCAmelCase	    =			len(lowerCAmelCase		) // 2
       UpperCAmelCase	    =			arr[0:mid]
       UpperCAmelCase	    =			arr[mid:]

       UpperCAmelCase    ,       UpperCAmelCase	    =			count_inversions_recursive(lowerCAmelCase		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			count_inversions_recursive(lowerCAmelCase		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			_count_cross_inversions(lowerCAmelCase	,      lowerCAmelCase		)

       UpperCAmelCase	    =			inversion_p + inversions_q + cross_inversions
       return c, num_inversions






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]
       UpperCAmelCase	    =			UpperCAmelCase	    =			UpperCAmelCase	    =			0
       while i < len(lowerCAmelCase		) and j < len(lowerCAmelCase		):
              if p[i] > q[j]:
                     # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
                     # These are all inversions. The claim emerges from the
                     # property that P is sorted.
                     num_inversion += len(lowerCAmelCase		) - i
                     r.append(q[j]		)
                     j += 1
              else:
                     r.append(p[i]		)
                     i += 1

       if i < len(lowerCAmelCase		):
              r.extend(p[i:]		)
       else:
              r.extend(q[j:]		)

       return r, num_inversion






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			[10, 2, 1, 5, 5, 2, 11]

       # this arr has 8 inversions:
       # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

       UpperCAmelCase	    =			count_inversions_bf(lowerCAmelCase		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			count_inversions_recursive(lowerCAmelCase		)

       assert num_inversions_bf == num_inversions_recursive == 8

       print("""number of inversions = """	,      lowerCAmelCase		)

       # testing an array with zero inversion (a sorted arr_1)

       arr_a.sort()
       UpperCAmelCase	    =			count_inversions_bf(lowerCAmelCase		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			count_inversions_recursive(lowerCAmelCase		)

       assert num_inversions_bf == num_inversions_recursive == 0
       print("""number of inversions = """	,      lowerCAmelCase		)

       # an empty list should also have zero inversions
       UpperCAmelCase	    =			[]
       UpperCAmelCase	    =			count_inversions_bf(lowerCAmelCase		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			count_inversions_recursive(lowerCAmelCase		)

       assert num_inversions_bf == num_inversions_recursive == 0
       print("""number of inversions = """	,      lowerCAmelCase		)


if __name__ == "__main__":
 main()
