


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,
)

UpperCAmelCase      =   logging.getLogger(__name__)

UpperCAmelCase      =   {"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase      =   {"facebook/bart-base": BartTokenizer}






def 	__lowerCAmelCase   ()->  Union[str, Any]:




      """simple docstring"""

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

      snake_case_	=				parser.parse_args()

      return args






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="cpu" )->  List[Any]:




      """simple docstring"""

      snake_case_	=				model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE ).to(SCREAMING_SNAKE_CASE )
      snake_case_	=				tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE )

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

      return huggingface_model, tokenizer






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      model.eval()

      snake_case_	=				None
      snake_case_	=				torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE ) )

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

            snake_case_	=				model.generate(
                inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , num_beams=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , early_stopping=SCREAMING_SNAKE_CASE , decoder_start_token_id=model.config.decoder_start_token_id , )

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

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

            snake_case_	=				remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE ) )

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

            snake_case_	=				onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE )
            snake_case_	=				ort_sess.run(
                SCREAMING_SNAKE_CASE , {
                    '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                    '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                    '''num_beams''': np.array(SCREAMING_SNAKE_CASE ),
                    '''max_length''': np.array(SCREAMING_SNAKE_CASE ),
                    '''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   ()->  Dict:




      """simple docstring"""

      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 , SCREAMING_SNAKE_CASE )

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

      model.to(SCREAMING_SNAKE_CASE )

      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(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
   main()



import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''a''', '''b''', '''c''']

       # Defaults to last layer if both are None
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[2] )

       # Out indices set to match out features
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(['''a''', '''c''']		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features set to match out indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[0, 2]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features selected from negative indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[-3, -1]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[-3, -1] )



 def 						UpperCamelCase__					(     self ):
       # Stage names must be set
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				_UpperCAmelCase )

       # Out features must be a list
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(('''a''', '''b''')		,				(0, 1)		,				['''a''', '''b'''] )

       # Out features must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				['''a'''] )

       # Out indices must be a list or tuple
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				0		,				['''a''', '''b'''] )

       # Out indices must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				(0, 1)		,				['''a'''] )

       # Out features and out indices must be the same length
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0,)		,				['''a''', '''b''', '''c'''] )

       # Out features should match out indices
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 2)		,				['''a''', '''b''', '''c'''] )

       # Out features and out indices should be in order
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''b''', '''a''']		,				(0, 1)		,				['''a''', '''b'''] )

       # Check passes with valid inputs
       verify_out_features_out_indices(['''a''', '''b''', '''d''']		,				(0, 1, -1)		,				['''a''', '''b''', '''c''', '''d'''] )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				BackboneMixin()

       snake_case_	=				['''a''', '''b''', '''c''']
       snake_case_	=				['''a''', '''c''']
       snake_case_	=				[0, 2]

       # Check that the output features and indices are set correctly
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[0, 2] )

       # Check out features and indices are updated correctly
       snake_case_	=				['''a''', '''b''']
       self.assertEqual(backbone.out_features		,				['''a''', '''b'''] )
       self.assertEqual(backbone.out_indices		,				[0, 1] )

       snake_case_	=				[-3, -1]
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[-3, -1] )