






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      :       List[Any]				  =   logging.getLogger(__name__)

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



def 			_SCREAMING_SNAKE_CASE						( )						->      Optional[Any]:





    """simple docstring"""

    __A         =	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."""    )

    __A         =	parser.parse_args()

    return args



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]			,				__lowercase   :			str="cpu"    )						->      Optional[int]:





    """simple docstring"""

    __A         =	model_dict[model_name].from_pretrained(__lowercase    ).to(__lowercase    )
    __A         =	tokenizer_dict[model_name].from_pretrained(__lowercase    )

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

    return huggingface_model, tokenizer



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Dict			,				__lowercase   :			Tuple			,				__lowercase   :			Union[str, Any]			,				__lowercase   :			str    )						->      int:





    """simple docstring"""

    model.eval()

    __A         =	None
    __A         =	torch.jit.script(BARTBeamSearchGenerator(__lowercase    )    )

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

        __A         =	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=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=__lowercase			,				)

        logger.info("""Model exported to {}""".format(__lowercase    )    )

        __A         =	remove_dup_initializers(os.path.abspath(__lowercase    )    )

        logger.info("""Deduplicated and optimized model written to {}""".format(__lowercase    )    )

        __A         =	onnxruntime.InferenceSession(__lowercase    )
        __A         =	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 			_SCREAMING_SNAKE_CASE						( )						->      List[str]:





    """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			,				__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:
        __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(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )


if __name__ == "__main__":
  main()












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

    if sum(__lowercase    ) > max_sum or (remaining_nums_sum + sum(__lowercase    )) < max_sum:
        return
    if sum(__lowercase    ) == max_sum:
        result.append(__lowercase    )
        return
    for index in range(__lowercase			,				len(__lowercase    )    ):
        create_state_space_tree(
            __lowercase			,				__lowercase			,				index + 1			,				[*path, nums[index]]			,				__lowercase			,				remaining_nums_sum - nums[index]			,				)
__a      :       str				  =   [3, 34, 4, 12, 5, 2]
__a      :       Optional[Any]				  =   9
__a      :       List[str]				  =   generate_sum_of_subsets_soln(nums, max_sum)






print(*result)




