"""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,
)

__lowerCamelCase											=  logging.getLogger(__name__)

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






def   UpperCAmelCase					(	):






       """simple docstring"""





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

       A__	     =							parser.parse_args()

       return args






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__="cpu"				):






       """simple docstring"""





       A__	     =							model_dict[model_name].from_pretrained(UpperCamelCase__				).to(UpperCamelCase__				)
       A__	     =							tokenizer_dict[model_name].from_pretrained(UpperCamelCase__				)

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

       return huggingface_model, tokenizer






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






       """simple docstring"""





       model.eval()

       A__	     =							None
       A__	     =							torch.jit.script(BARTBeamSearchGenerator(UpperCamelCase__				)				)

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

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

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

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

              A__	     =							remove_dup_initializers(os.path.abspath(UpperCamelCase__				)				)

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

              A__	     =							onnxruntime.InferenceSession(UpperCamelCase__				)
              A__	     =							ort_sess.run(
                  UpperCamelCase__       ,    {
                      'input_ids': inputs['input_ids'].cpu().numpy(),
                      'attention_mask': inputs['attention_mask'].cpu().numpy(),
                      'num_beams': np.array(UpperCamelCase__				),
                      'max_length': np.array(UpperCamelCase__				),
                      '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					(	):






       """simple docstring"""





       A__	     =							parse_args()
       A__	     =							5
       A__	     =							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__	     =							torch.device(args.device				)

       A__     ,  A__	     =							load_model_tokenizer(args.model_name_or_path       ,    UpperCamelCase__				)

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

       model.to(UpperCamelCase__				)

       if args.max_length:
              A__	     =							args.max_length

       if args.num_beams:
              A__	     =							args.num_beams

       if args.output_file_path:
              A__	     =							args.output_file_path
       else:
              A__	     =							'BART.onnx'

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


if __name__ == "__main__":
    main()






"""simple docstring"""




import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				str:
													A__	     =							get_activation('swish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							get_activation('silu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('mish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.Mish     )

													self.assertEqual(act(torch.tensor(-2_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('gelu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.GELU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





