




'''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()










'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




@slow
@require_torch_gpu
class 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
										snake_case_      =				torch.manual_seed(0							)
										snake_case_      =				pipe(
										    image=a__    ,							generator=a__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type="numpy"    ,							).images

										snake_case_      =				image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




