





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,
)

a_			=      logging.getLogger(__name__)

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







def 		__lowercase					(       )       ->List[str]:



    '''simple docstring'''







    __A   :						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.'''						)

    __A   :						List[str]									=					parser.parse_args()

    return args







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  List[Any]="cpu"						)       ->Any:



    '''simple docstring'''







    __A   :						str									=					model_dict[model_name].from_pretrained(lowercase_						).to(lowercase_						)
    __A   :						Any									=					tokenizer_dict[model_name].from_pretrained(lowercase_						)

    if model_name in ["facebook/bart-base"]:
        __A   :						int									=					0
        __A   :						Tuple									=					None
        __A   :						List[str]									=					0

    return huggingface_model, tokenizer







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  List[str]    ,snake_case_  :  Optional[Any]    ,snake_case_  :  Any    ,snake_case_  :  Any						)       ->List[str]:



    '''simple docstring'''







    model.eval()

    __A   :						List[str]									=					None
    __A   :						Optional[int]									=					torch.jit.script(BARTBeamSearchGenerator(lowercase_						)						)

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

        __A   :						List[Any]									=					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=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=lowercase_    ,)

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

        __A   :						Tuple									=					remove_dup_initializers(os.path.abspath(lowercase_						)						)

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

        __A   :						Dict									=					onnxruntime.InferenceSession(lowercase_						)
        __A   :						Tuple									=					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 		__lowercase					(       )       ->int:



    '''simple docstring'''







    __A   :						Any									=					parse_args()
    __A   :						Optional[Any]									=					5
    __A   :						Tuple									=					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()

    __A   :						List[Any]									=					torch.device(args.device						)

    __A			,	__A   :						Optional[int]									=					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:
        __A   :						Optional[int]									=					args.max_length

    if args.num_beams:
        __A   :						int									=					args.num_beams

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

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


if __name__ == "__main__":
       main()











"""simple docstring"""



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					XLMTokenizer
 _lowerCamelCase				=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __A   :						Tuple									=					[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         '''<unk>''',
     ]
     __A   :						Dict									=					dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase  )  )  )  )
     __A   :						Union[str, Any]									=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']  )
     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''merges_file''']  )
     with open(self.vocab_file ,						'''w'''  ) as fp:
         fp.write(json.dumps(__lowerCamelCase  )  )
     with open(self.merges_file ,						'''w'''  ) as fp:
         fp.write('''\n'''.join(__lowerCamelCase  )  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					'''lower newer'''
     __A   :						int									=					'''lower newer'''
     return input_text, output_text

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					XLMTokenizer(self.vocab_file ,						self.merges_file  )

     __A   :						Optional[Any]									=					'''lower'''
     __A   :						Any									=					['''low''', '''er</w>''']
     __A   :						Tuple									=					tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokens + ['''<unk>''']
     __A   :						List[str]									=					[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase  ) ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''  )

     __A   :						Union[str, Any]									=					tokenizer.encode('''sequence builders''' ,						add_special_tokens=__lowerCamelCase  )
     __A   :						int									=					tokenizer.encode('''multi-sequence build''' ,						add_special_tokens=__lowerCamelCase  )

     __A   :						int									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase  )
     __A   :						List[Any]									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,						__lowerCamelCase  )

     assert encoded_sentence == [0] + text + [1]
     assert encoded_pair == [0] + text + [1] + text_a + [1]




