



'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			RobertaTokenizer
 __lowerCamelCase       :			List[Any]		=			RobertaTokenizerFast
 __lowerCamelCase       :			Union[str, Any]		=			True
 __lowerCamelCase       :			Dict		=			{'cls_token': '<s>'}



 def    a__    (self			)       ->	int:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	Dict:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return self.tokenizer_class.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  **A			)       ->	str:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    '''lower newer'''
      _a           =    '''lower newer'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.tokenizer_class(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''lower newer'''
      _a           =    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      _a           =    tokenizer.tokenize(A			)  # , add_prefix_space=True)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]
      _a           =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      self.assertListEqual(tokenizer.encode('''Hello world!'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 2]			)
      self.assertListEqual(
          tokenizer.encode('''Hello world! cécé herlolip 418'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]					,  )



 @slow
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.tokenizer_class.from_pretrained('''roberta-base'''			)

      _a           =    tokenizer.encode('''sequence builders'''					,  add_special_tokens=A			)
      _a           =    tokenizer.encode('''multi-sequence build'''					,  add_special_tokens=A			)

      _a           =    tokenizer.encode(
          '''sequence builders'''					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.encode(
          '''sequence builders'''					,  '''multi-sequence build'''					,  add_special_tokens=A					,  add_prefix_space=A			)

      _a           =    tokenizer.build_inputs_with_special_tokens(A			)
      _a           =    tokenizer.build_inputs_with_special_tokens(A					,  A			)

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      _a           =    '''Encode this sequence.'''
      _a           =    tokenizer.byte_encoder[''' '''.encode('''utf-8'''			)[0]]

      # Testing encoder arguments
      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertNotEqual(A					,  A			)

      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertEqual(A					,  A			)

      tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}			)
      _a           =    tokenizer.encode(A					,  add_special_tokens=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
      self.assertNotEqual(A					,  A			)

      # Testing spaces after special tokens
      _a           =    '''<mask>'''
      tokenizer.add_special_tokens(
          {'''mask_token''': AddedToken(A					,  lstrip=A					,  rstrip=A			)}			)  # mask token has a left space
      _a           =    tokenizer.convert_tokens_to_ids(A			)

      _a           =    '''Encode <mask> sequence'''
      _a           =    '''Encode <mask>sequence'''

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertEqual(A					,  A			)

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertNotEqual(A					,  A			)



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      pass



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    self.rust_tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    self.tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    '''A, <mask> AllenNLP sentence.'''
                _a           =    tokenizer_r.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)
                _a           =    tokenizer_p.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r['''token_type_ids''']			)					,  sum(tokens_p['''token_type_ids''']			)			)

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)					,  sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)					,  )

                _a           =    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
                _a           =    tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p['''input_ids''']					,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)
                self.assertSequenceEqual(tokens_r['''input_ids''']					,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)

                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      for trim_offsets, add_prefix_space in itertools.product([True, False]					,  repeat=2			):
           _a           =    self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)

           _a           =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
           _a           =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

           self.assertEqual(pre_tokenizer_state['''add_prefix_space''']					,  A			)

           self.assertEqual(post_processor_state['''add_prefix_space''']					,  A			)
           self.assertEqual(post_processor_state['''trim_offsets''']					,  A			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                _a           =    f'''{text_of_1_token} {text_of_1_token}'''

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    f''' {text}'''

                # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                # )
                # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                # self.assertEqual(
                #     encoding.offset_mapping[1],
                #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                # )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (1, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			) + 1, 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )





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,
)

__magic_name__  							=	logging.getLogger(__name__)

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





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					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."				)

    snake_case__    =					parser.parse_args()

    return args





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase="cpu"				):
    snake_case__    =					model_dict[model_name].from_pretrained(__lowerCAmelCase				).to(__lowerCAmelCase				)
    snake_case__    =					tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase				)

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

    return huggingface_model, tokenizer





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    model.eval()

    snake_case__    =					None
    snake_case__    =					torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase				)				)

    with torch.no_grad():
        snake_case__    =					"My friends are cool but they eat too many carbs."
        snake_case__    =					tokenizer([ARTICLE_TO_SUMMARIZE]     ,		max_length=1_024     ,		return_tensors="pt"				).to(model.device				)

        snake_case__    =					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				)				)

        snake_case__    =					remove_dup_initializers(os.path.abspath(__lowerCAmelCase				)				)

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

        snake_case__    =					onnxruntime.InferenceSession(__lowerCAmelCase				)
        snake_case__    =					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 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					parse_args()
    snake_case__    =					5
    snake_case__    =					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()

    snake_case__    =					torch.device(args.device				)

    snake_case__    ,       snake_case__    =					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:
        snake_case__    =					args.max_length

    if args.num_beams:
        snake_case__    =					args.num_beams

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

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


if __name__ == "__main__":
     main()



