




"""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,
)

SCREAMING_SNAKE_CASE_	     =       logging.getLogger(__name__)

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





def      __snake_case (				):


    """simple docstring"""







    UpperCamelCase              =	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.'''    )

    UpperCamelCase              =	parser.parse_args()

    return args





def      __snake_case (				_lowercase			,_lowercase="cpu"    ):


    """simple docstring"""







    UpperCamelCase              =	model_dict[model_name].from_pretrained(_lowercase    ).to(_lowercase    )
    UpperCamelCase              =	tokenizer_dict[model_name].from_pretrained(_lowercase    )

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

    return huggingface_model, tokenizer





def      __snake_case (				_lowercase			,_lowercase			,_lowercase			,_lowercase			,_lowercase    ):


    """simple docstring"""







    model.eval()

    UpperCamelCase              =	None
    UpperCamelCase              =	torch.jit.script(BARTBeamSearchGenerator(_lowercase    )    )

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

        UpperCamelCase              =	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    )    )

        UpperCamelCase              =	remove_dup_initializers(os.path.abspath(_lowercase    )    )

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

        UpperCamelCase              =	onnxruntime.InferenceSession(_lowercase    )
        UpperCamelCase              =	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      __snake_case (				):


    """simple docstring"""







    UpperCamelCase              =	parse_args()
    UpperCamelCase              =	5
    UpperCamelCase              =	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()

    UpperCamelCase              =	torch.device(args.device    )

    UpperCamelCase       ,	UpperCamelCase              =	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:
        UpperCamelCase              =	args.max_length

    if args.num_beams:
        UpperCamelCase              =	args.num_beams

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

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


if __name__ == "__main__":
  main()


'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int = 1000   )      -> int:
							SCREAMING_SNAKE_CASE_				=				2**power
							SCREAMING_SNAKE_CASE_				=				str(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				list(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				0

							for i in list_num:
														sum_of_num += int(lowerCAmelCase__   )

							return sum_of_num


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				Tuple     	=			int(input("Enter the power of 2: ").strip())
							print("2 ^ ", power, " = ", 2**power)
							SCREAMING_SNAKE_CASE							:				str     	=			solution(power)
							print("Sum of the digits is: ", result)





