





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					:				Any				=			logging.getLogger(__name__)

_A					:				Optional[int]				=			{"""facebook/bart-base""": BartForConditionalGeneration}
_A					:				str				=			{"""facebook/bart-base""": BartTokenizer}


def 							__snake_case							(    )				->						Dict:
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_="cpu"	)				->						List[str]:
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    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 							__snake_case							(    )				->						Optional[Any]:
    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()







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_A					:				Optional[int]				=			object()

# For specifying empty leaf dict `{}`
_A					:				Tuple				=			object()


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			tuple((re.compile(x + '''$'''	) for x in qs)	)
    for i in range(len(lowerCAmelCase_	) - len(lowerCAmelCase_	) + 1	):
        SCREAMING_SNAKE_CASE__											=			[x.match(lowerCAmelCase_	) for x, y in zip(lowerCAmelCase_ ,							ks[i:]	)]
        if matches and all(lowerCAmelCase_	):
            return True
    return False


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    def replace(lowerCAmelCase_ ,							lowerCAmelCase_	):
        for rule, replacement in rules:
            if _match(lowerCAmelCase_ ,							lowerCAmelCase_	):
                return replacement
        return val

    return replace


def 							__snake_case							(    )				->						Optional[int]:
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        (("transformer", "wte", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("attention", "out_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("mlp", "c_fc", "bias"), P('''mp'''	)),
        (("mlp", "c_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]



def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			_get_partition_rules()
    SCREAMING_SNAKE_CASE__											=			_replacement_rules(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			{k: _unmatched for k in flatten_dict(lowerCAmelCase_	)}
    SCREAMING_SNAKE_CASE__											=			{k: replace(lowerCAmelCase_ ,							lowerCAmelCase_	) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(lowerCAmelCase_	)	)
