'''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,
)

SCREAMING_SNAKE_CASE_	   =			logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_	   =			{'facebook/bart-base': BartForConditionalGeneration}
SCREAMING_SNAKE_CASE_	   =			{'facebook/bart-base': BartTokenizer}






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

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

  return args






def  UpperCamelCase__					(					_lowercase    :				Optional[Any]   ,    _lowercase    :				List[str]="cpu"     )							->	int:
  __UpperCAmelCase:					Any		= model_dict[model_name].from_pretrained(_lowercase     ).to(_lowercase     )
  __UpperCAmelCase:					List[str]		= tokenizer_dict[model_name].from_pretrained(_lowercase     )

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

  return huggingface_model, tokenizer






def  UpperCamelCase__					(					_lowercase    :				List[Any]   ,    _lowercase    :				Optional[Any]   ,    _lowercase    :				List[str]   ,    _lowercase    :				Any   ,    _lowercase    :				List[str]     )							->	List[str]:
  model.eval()

  __UpperCAmelCase:					List[Any]		= None
  __UpperCAmelCase:					Tuple		= torch.jit.script(BARTBeamSearchGenerator(_lowercase     )     )

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

    __UpperCAmelCase:					str		= model.generate(
        inputs["""input_ids"""]   ,    attention_mask=inputs["""attention_mask"""]   ,    num_beams=_lowercase   ,    max_length=_lowercase   ,    early_stopping=_lowercase   ,    decoder_start_token_id=model.config.decoder_start_token_id   ,    )

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

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

    __UpperCAmelCase:					Any		= remove_dup_initializers(os.path.abspath(_lowercase     )     )

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

    __UpperCAmelCase:					Union[str, Any]		= onnxruntime.InferenceSession(_lowercase     )
    __UpperCAmelCase:					str		= ort_sess.run(
        _lowercase   ,    {
            """input_ids""": inputs["""input_ids"""].cpu().numpy(),
            """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
            """num_beams""": np.array(_lowercase     ),
            """max_length""": np.array(_lowercase     ),
            """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__					(					)							->	Optional[int]:
  __UpperCAmelCase:					Tuple		= parse_args()
  __UpperCAmelCase:					int		= 5
  __UpperCAmelCase:					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()

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

  __UpperCAmelCase, __UpperCAmelCase:					Tuple		= load_model_tokenizer(args.model_name_or_path   ,    _lowercase     )

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

  model.to(_lowercase     )

  if args.max_length:
    __UpperCAmelCase:					Optional[Any]		= args.max_length

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

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

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


if __name__ == "__main__":
    main()
'''simple docstring'''






import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class    a (       unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					JukeboxTokenizer
  __lowerCAmelCase				      =					{
      """artist""": """Zac Brown Band""",
      """genres""": """Country""",
      """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
  }







  @require_torch
  def    lowercase_  (							self					):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:					Dict		= JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""					)
    __UpperCAmelCase:					List[str]		= tokenizer(**self.metas					)["""input_ids"""]
    # fmt: off
    __UpperCAmelCase:					int		= [
        torch.tensor([[
            0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
            76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
            44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
            47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
            76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
            30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
            27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
            45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
            41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
            76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
            76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
            64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
            30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
            27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
            34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
            27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
            41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
            44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
            76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
            32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
            40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
            45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
            31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
            45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
            34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
            31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
            40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
            38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
            76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
            41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
            27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
            46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
            41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
            46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
            41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
            40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
            27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
            76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
            41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
            76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
            27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
            34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
            44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
            40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
            46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
            38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
            40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
            27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
            76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
            76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
            76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
            76, 76]]					),
        torch.tensor([[0, 0, 0, 1069, 11]]					),
        torch.tensor([[0, 0, 0, 1069, 11]]					),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,    EXPECTED_OUTPUT[0]					)					)
    self.assertTrue(torch.allclose(tokens[1]   ,    EXPECTED_OUTPUT[1]					)					)
    self.assertTrue(torch.allclose(tokens[2]   ,    EXPECTED_OUTPUT[2]					)					)







  @require_torch
  def    lowercase_  (							self					):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:					Any		= JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""					)
    __UpperCAmelCase:					Tuple		= tokenizer(**self.metas					)["""input_ids"""]
    # fmt: off
    __UpperCAmelCase:					int		= [
        torch.tensor([[
            0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
            31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
            31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
            40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
            79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
            77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
            27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
            37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
            32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
            77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
            77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
            46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
            77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
            77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
            77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
            77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
            64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
            40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
            40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
            38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
            31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
            41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
            46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
            41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
            31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
            31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
            44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
            31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
            38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
            40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
            27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
            31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
            34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
            31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
            31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
            45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
            31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
            15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
            11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
            45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
            41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
            44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
            46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
            27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
            35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
            77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
            31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
            41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
            77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
            40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
            77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
            27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
            77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
            77, 77, 77, 77, 77, 77]]					),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]					),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]					),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,    EXPECTED_OUTPUT[0]					)					)
    self.assertTrue(torch.allclose(tokens[1]   ,    EXPECTED_OUTPUT[1]					)					)
    self.assertTrue(torch.allclose(tokens[2]   ,    EXPECTED_OUTPUT[2]					)					)