




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 			a   ():
							SCREAMING_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.'''     )

							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()

							return args

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase="cpu"     ):
							SCREAMING_SNAKE_CASE_   	=    model_dict[model_name].from_pretrained(_lowerCAmelCase     ).to(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    tokenizer_dict[model_name].from_pretrained(_lowerCAmelCase     )

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

							return huggingface_model, tokenizer

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							model.eval()

							SCREAMING_SNAKE_CASE_   	=    None
							SCREAMING_SNAKE_CASE_   	=    torch.jit.script(BARTBeamSearchGenerator(_lowerCAmelCase     )     )

							with torch.no_grad():
														SCREAMING_SNAKE_CASE_   	=    '''My friends are cool but they eat too many carbs.'''
														SCREAMING_SNAKE_CASE_   	=    tokenizer([ARTICLE_TO_SUMMARIZE]  ,    max_length=1_0_2_4  ,    return_tensors='''pt'''     ).to(model.device     )

														SCREAMING_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=1_4  ,    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     )     )

														SCREAMING_SNAKE_CASE_   	=    remove_dup_initializers(os.path.abspath(_lowerCAmelCase     )     )

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

														SCREAMING_SNAKE_CASE_   	=    onnxruntime.InferenceSession(_lowerCAmelCase     )
														SCREAMING_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 			a   ():
							SCREAMING_SNAKE_CASE_   	=    parse_args()
							SCREAMING_SNAKE_CASE_   	=    5
							SCREAMING_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()

							SCREAMING_SNAKE_CASE_   	=    torch.device(args.device     )

							SCREAMING_SNAKE_CASE_	,	SCREAMING_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:
														SCREAMING_SNAKE_CASE_   	=    args.max_length

							if args.num_beams:
														SCREAMING_SNAKE_CASE_   	=    args.num_beams

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

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


if __name__ == "__main__":
			main()








from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE 						=1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE 						=3e8  # unit of c : m * s^-1

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):

							if (force, area, distance).count(0     ) != 1:
														raise ValueError('''One and only one argument must be 0'''     )
							if force < 0:
														raise ValueError('''Magnitude of force can not be negative'''     )
							if distance < 0:
														raise ValueError('''Distance can not be negative'''     )
							if area < 0:
														raise ValueError('''Area can not be negative'''     )
							if force == 0:
														SCREAMING_SNAKE_CASE_   	=    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
														    2_4_0 * (distance) ** 4
														)
														return {"force": force}
							elif area == 0:
														SCREAMING_SNAKE_CASE_   	=    (2_4_0 * force * (distance) ** 4) / (
														    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
														)
														return {"area": area}
							elif distance == 0:
														SCREAMING_SNAKE_CASE_   	=    (
														    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
														) ** (1 / 4)
														return {"distance": distance}
							raise ValueError('''One and only one argument must be 0'''     )


# Run doctest
if __name__ == "__main__":
			import doctest

			doctest.testmod()


