


"""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,
)

__snake_case    					=					logging.getLogger(__name__)

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


def    _lowerCamelCase   (     ):
 lowercase__       :       Optional[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."""  )

 lowercase__       :       Dict				=						parser.parse_args()

 return args


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							Optional[Any]="cpu"  ):
 lowercase__       :       Optional[Any]				=						model_dict[model_name].from_pretrained(lowerCamelCase__  ).to(lowerCamelCase__  )
 lowercase__       :       int				=						tokenizer_dict[model_name].from_pretrained(lowerCamelCase__  )

 if model_name in ["facebook/bart-base"]:
  lowercase__       :       Tuple				=						0
  lowercase__       :       Optional[int]				=						None
  lowercase__       :       Union[str, Any]				=						0

 return huggingface_model, tokenizer


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							int  ):
 model.eval()

 lowercase__       :       List[Any]				=						None
 lowercase__       :       Dict				=						torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase__  )  )

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

  lowercase__       :       Optional[Any]				=						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__  )  )

  lowercase__       :       Union[str, Any]				=						remove_dup_initializers(os.path.abspath(lowerCamelCase__  )  )

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

  lowercase__       :       List[str]				=						onnxruntime.InferenceSession(lowerCamelCase__  )
  lowercase__       :       str				=						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    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						parse_args()
 lowercase__       :       Optional[int]				=						5
 lowercase__       :       Optional[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()

 lowercase__       :       str				=						torch.device(args.device  )

 lowercase__	,       lowercase__       :       int				=						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:
  lowercase__       :       int				=						args.max_length

 if args.num_beams:
  lowercase__       :       Tuple				=						args.num_beams

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

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


if __name__ == "__main__":
   main()



"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						tempfile.mkdtemp()

    lowercase__       :       List[Any]				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       str				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

    lowercase__       :       Optional[Any]				=						{
        """do_resize""": True,
        """size""": 20,
        """do_center_crop""": True,
        """crop_size""": 18,
        """do_normalize""": True,
        """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
        """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    lowercase__       :       Any				=						os.path.join(self.tmpdirname ,     lowerCamelCase__						)
    with open(self.image_processor_file ,     """w""" ,     encoding="""utf-8"""						) as fp:
     json.dump(lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizerFast.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							List[Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    shutil.rmtree(self.tmpdirname						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Tuple				=						[np.random.randint(255 ,     size=(3, 30, 400) ,     dtype=np.uinta						)]
    lowercase__       :       Any				=						[Image.fromarray(np.moveaxis(lowerCamelCase__ ,     0 ,     -1						)						) for x in image_inputs]
    return image_inputs


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()
    lowercase__       :       Union[str, Any]				=						self.get_rust_tokenizer()
    lowercase__       :       Any				=						self.get_image_processor()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_slow.save_pretrained(self.tmpdirname						)
    lowercase__       :       Tuple				=						AlignProcessor.from_pretrained(self.tmpdirname ,     use_fast=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_fast.save_pretrained(self.tmpdirname						)
    lowercase__       :       str				=						AlignProcessor.from_pretrained(self.tmpdirname						)

    self.assertEqual(processor_slow.tokenizer.get_vocab() ,     tokenizer_slow.get_vocab()						)
    self.assertEqual(processor_fast.tokenizer.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertEqual(tokenizer_slow.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertIsInstance(processor_slow.tokenizer ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.tokenizer ,     lowerCamelCase__						)

    self.assertEqual(processor_slow.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertEqual(processor_fast.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertIsInstance(processor_slow.image_processor ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.image_processor ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=self.get_tokenizer() ,     image_processor=self.get_image_processor()						)
    processor.save_pretrained(self.tmpdirname						)

    lowercase__       :       Dict				=						self.get_tokenizer(bos_token="""(BOS)""" ,     eos_token="""(EOS)"""						)
    lowercase__       :       Any				=						self.get_image_processor(do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    lowercase__       :       Union[str, Any]				=						AlignProcessor.from_pretrained(
        self.tmpdirname ,     bos_token="""(BOS)""" ,     eos_token="""(EOS)""" ,     do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    self.assertEqual(processor.tokenizer.get_vocab() ,     tokenizer_add_kwargs.get_vocab()						)
    self.assertIsInstance(processor.tokenizer ,     lowerCamelCase__						)

    self.assertEqual(processor.image_processor.to_json_string() ,     image_processor_add_kwargs.to_json_string()						)
    self.assertIsInstance(processor.image_processor ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[int]				=						self.get_image_processor()
    lowercase__       :       Dict				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Optional[int]				=						self.prepare_image_inputs()

    lowercase__       :       Tuple				=						image_processor(lowerCamelCase__ ,     return_tensors="""np"""						)
    lowercase__       :       str				=						processor(images=lowerCamelCase__ ,     return_tensors="""np"""						)

    for key in input_image_proc.keys():
     self.assertAlmostEqual(input_image_proc[key].sum() ,     input_processor[key].sum() ,     delta=1E-2						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						self.get_image_processor()
    lowercase__       :       Tuple				=						self.get_tokenizer()

    lowercase__       :       List[str]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Any				=						"""lower newer"""

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__						)

    lowercase__       :       Tuple				=						tokenizer(lowerCamelCase__ ,     padding="""max_length""" ,     max_length=64						)

    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key] ,     encoded_processor[key]						)


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[Any]				=						self.get_image_processor()
    lowercase__       :       Any				=						self.get_tokenizer()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						"""lower newer"""
    lowercase__       :       List[str]				=						self.prepare_image_inputs()

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

    self.assertListEqual(list(inputs.keys()						) ,     ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]						)

    # test if it raises when no input is passed
    with pytest.raises(lowerCamelCase__						):
     processor()


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       int				=						self.get_image_processor()
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Union[str, Any]				=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    lowercase__       :       Tuple				=						processor.batch_decode(lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						tokenizer.batch_decode(lowerCamelCase__						)

    self.assertListEqual(lowerCamelCase__ ,     lowerCamelCase__						)







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       List[str]				=						self.get_image_processor()
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()

    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       str				=						"""lower newer"""
    lowercase__       :       List[Any]				=						self.prepare_image_inputs()

    lowercase__       :       int				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

    self.assertListEqual(list(inputs.keys()						) ,     processor.model_input_names						)