




'''simple docstring'''





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
		import torch

if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


_SCREAMING_SNAKE_CASE							:       List[Any]						=			logging.get_logger(__name__)




@dataclass
class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Optional[Any]							=   [
			    "no_inference",
			    "no_cuda",
			    "no_tpu",
			    "no_speed",
			    "no_memory",
			    "no_env_print",
			    "no_multi_process",
			]




			def __init__( self    ,							**a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										for deprecated_arg in self.deprecated_args:
																	if deprecated_arg in kwargs:
																								snake_case_      =				deprecated_arg[3:]
																								setattr(self    ,							a__    ,							not kwargs.pop(a__							)							)
																								logger.warning(
																								    F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
																								    F' {positive_arg}={kwargs[positive_arg]}'							)

										snake_case_      =				kwargs.pop("torchscript"    ,							self.torchscript							)
										snake_case_      =				kwargs.pop("torch_xla_tpu_print_metrics"    ,							self.torch_xla_tpu_print_metrics							)
										snake_case_      =				kwargs.pop("fp16_opt_level"    ,							self.fpaa_opt_level							)
										super().__init__(**a__							)

			lowerCAmelCase_					:				bool							=   field(default=lowercase_						,						metadata={"help": "Trace the models using torchscript"}							)
			lowerCAmelCase_					:				bool							=   field(default=lowercase_						,						metadata={"help": "Print Xla/PyTorch tpu metrics"}							)
			lowerCAmelCase_					:				str							=   field(
			    default="O1"						,						metadata={
			        "help": (
			            "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
			            "See details at https://nvidia.github.io/apex/amp.html"
			        )
			    }						,						)




			@cached_property
			def        lowerCAmelCase__    ( self							)    ->    Tuple["torch.device", int]:

										'''simple docstring'''




										requires_backends(self    ,							["torch"]							)
										logger.info("PyTorch: setting up devices"							)
										if not self.cuda:
																	snake_case_      =				torch.device("cpu"							)
																	snake_case_      =				0
										elif is_torch_tpu_available():
																	snake_case_      =				xm.xla_device()
																	snake_case_      =				0
										else:
																	snake_case_      =				torch.device("cuda" if torch.cuda.is_available() else "cpu"							)
																	snake_case_      =				torch.cuda.device_count()
										return device, n_gpu




			@property
			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										return is_torch_tpu_available() and self.tpu




			@property
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										requires_backends(self    ,							["torch"]							)
										# TODO(PVP): currently only single GPU is supported
										return torch.cuda.current_device()




			@property
			def        lowerCAmelCase__    ( self							)    ->    "torch.device":

										'''simple docstring'''




										requires_backends(self    ,							["torch"]							)
										return self._setup_devices[0]




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										requires_backends(self    ,							["torch"]							)
										return self._setup_devices[1]




			@property
			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										return self.n_gpu > 0










'''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()




