




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
_SCREAMING_SNAKE_CASE							:       Tuple						=			[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]


def 					UpperCamelCase_(     snake_case							:	Optional[int]					):

							'''simple docstring'''






							for file in FILES_TO_FIND:
														if not (transformers_path / file).exists():
																					return False
							return True


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[str]						=			argparse.ArgumentParser()
		parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
		_SCREAMING_SNAKE_CASE							:       List[str]						=			parser.parse_args()
		if args.check_lib:
				_SCREAMING_SNAKE_CASE							:       Any						=			importlib.import_module("transformers")
				_SCREAMING_SNAKE_CASE							:       List[str]						=			Path(transformers_module.__file__).parent
		else:
				_SCREAMING_SNAKE_CASE							:       Optional[int]						=			Path.cwd() / '''build/lib/transformers'''
		if not test_custom_files_are_present(transformers_path):
				raise ValueError("The built release does not contain the custom files. Fix this before going further!")










'''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							:       Dict						=			logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE							:       List[str]						=			{"facebook/bart-base": BartForConditionalGeneration}
_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			{"facebook/bart-base": BartTokenizer}


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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

							snake_case_      =				parser.parse_args()

							return args


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]							,    snake_case							:	List[str]="cpu"					):

							'''simple docstring'''






							snake_case_      =				model_dict[model_name].from_pretrained(snake_case					).to(snake_case					)
							snake_case_      =				tokenizer_dict[model_name].from_pretrained(snake_case					)

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

							return huggingface_model, tokenizer


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	Tuple							,    snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							model.eval()

							snake_case_      =				None
							snake_case_      =				torch.jit.script(BARTBeamSearchGenerator(snake_case					)					)

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

														snake_case_      =				model.generate(
														    inputs["input_ids"]							,    attention_mask=inputs["attention_mask"]							,    num_beams=snake_case							,    max_length=snake_case							,    early_stopping=snake_case							,    decoder_start_token_id=model.config.decoder_start_token_id							,    )

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

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

														snake_case_      =				remove_dup_initializers(os.path.abspath(snake_case					)					)

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

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






							snake_case_      =				parse_args()
							snake_case_      =				5
							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()

							snake_case_      =				torch.device(args.device					)

							snake_case_ ,				snake_case_      =				load_model_tokenizer(args.model_name_or_path							,    snake_case					)

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

							model.to(snake_case					)

							if args.max_length:
														snake_case_      =				args.max_length

							if args.num_beams:
														snake_case_      =				args.num_beams

							if args.output_file_path:
														snake_case_      =				args.output_file_path
							else:
														snake_case_      =				"BART.onnx"

							logger.info("Exporting model to ONNX"					)
							export_and_validate_model(snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case					)


if __name__ == "__main__":
		main()




