



"""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,
)

lowerCamelCase      =		logging.getLogger(__name__)

lowerCamelCase      =		{"""facebook/bart-base""": BartForConditionalGeneration}
lowerCamelCase      =		{"""facebook/bart-base""": BartTokenizer}
def      a__			(   ):
      UpperCAmelCase_    				=							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."   )

      UpperCAmelCase_    				=							parser.parse_args()

      return args
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__="cpu"   ):
      UpperCAmelCase_    				=							model_dict[model_name].from_pretrained(lowerCAmelCase__   ).to(lowerCAmelCase__   )
      UpperCAmelCase_    				=							tokenizer_dict[model_name].from_pretrained(lowerCAmelCase__   )

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

      return huggingface_model, tokenizer
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      model.eval()

      UpperCAmelCase_    				=							None
      UpperCAmelCase_    				=							torch.jit.script(BARTBeamSearchGenerator(lowerCAmelCase__   )   )

      with torch.no_grad():
            UpperCAmelCase_    				=							"My friends are cool but they eat too many carbs."
            UpperCAmelCase_    				=							tokenizer([ARTICLE_TO_SUMMARIZE]			,    max_length=1024			,    return_tensors="pt"   ).to(model.device   )

            UpperCAmelCase_    				=							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=14			,    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__   )   )

            UpperCAmelCase_    				=							remove_dup_initializers(os.path.abspath(lowerCAmelCase__   )   )

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

            UpperCAmelCase_    				=							onnxruntime.InferenceSession(lowerCAmelCase__   )
            UpperCAmelCase_    				=							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      a__			(   ):
      UpperCAmelCase_    				=							parse_args()
      UpperCAmelCase_    				=							5
      UpperCAmelCase_    				=							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()

      UpperCAmelCase_    				=							torch.device(args.device   )

      UpperCAmelCase_				,					UpperCAmelCase_    				=							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:
            UpperCAmelCase_    				=							args.max_length

      if args.num_beams:
            UpperCAmelCase_    				=							args.num_beams

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

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


if __name__ == "__main__":
  main()









"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__   ):
      if isinstance(lowerCAmelCase__			,    (list, tuple)   ) and isinstance(videos[0]			,    (list, tuple)   ) and is_valid_image(videos[0][0]   ):
            return videos

      elif isinstance(lowerCAmelCase__			,    (list, tuple)   ) and is_valid_image(videos[0]   ):
            return [videos]

      elif is_valid_image(lowerCAmelCase__   ):
            return [[videos]]

      raise ValueError(f"""Could not make batched video from {videos}"""   )


class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Union[int, float] = 1 / 255	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       **_UpperCAmelCase    :						Any	,       )				->		None:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							size if size is not None else {"shortest_edge": 224}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             UpperCAmelCase_    				=							crop_size if crop_size is not None else {"height": 224, "width": 224}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       param_name="crop_size"					)

             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size
             UpperCAmelCase_    				=							do_center_crop
             UpperCAmelCase_    				=							crop_size
             UpperCAmelCase_    				=							resample
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             UpperCAmelCase_    				=							image_std if image_std is not None else IMAGENET_STANDARD_STD



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Dict[str, int]	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Tuple	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             if "shortest_edge" in size:
                   UpperCAmelCase_    				=							get_resize_output_image_size(_UpperCAmelCase	,       size["shortest_edge"]	,       default_to_square=_UpperCAmelCase					)
             elif "height" in size and "width" in size:
                   UpperCAmelCase_    				=							(size["height"], size["width"])
             else:
                   raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""					)
             return resize(_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Dict[str, int]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Union[str, Any]	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase					)
             if "height" not in size or "width" not in size:
                   raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""					)
             return center_crop(_UpperCAmelCase	,       size=(size["height"], size["width"])	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						List[Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[int, float]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						str	,       )				->		List[str]:


             '''simple docstring'''




             return rescale(_UpperCAmelCase	,       scale=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Tuple	,       )				->		np.ndarray:


             '''simple docstring'''




             return normalize(_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						ImageInput	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						PILImageResampling = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[ChannelDimension] = ChannelDimension.FIRST	,       )				->		np.ndarray:


             '''simple docstring'''




             if do_resize and size is None or resample is None:
                   raise ValueError("Size and resample must be specified if do_resize is True."					)

             if do_center_crop and crop_size is None:
                   raise ValueError("Crop size must be specified if do_center_crop is True."					)

             if do_rescale and rescale_factor is None:
                   raise ValueError("Rescale factor must be specified if do_rescale is True."					)

             if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError("Image mean and std must be specified if do_normalize is True."					)

             # All transformations expect numpy arrays.
             UpperCAmelCase_    				=							to_numpy_array(_UpperCAmelCase					)

             if do_resize:
                   UpperCAmelCase_    				=							self.resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase					)

             if do_center_crop:
                   UpperCAmelCase_    				=							self.center_crop(_UpperCAmelCase	,       size=_UpperCAmelCase					)

             if do_rescale:
                   UpperCAmelCase_    				=							self.rescale(image=_UpperCAmelCase	,       scale=_UpperCAmelCase					)

             if do_normalize:
                   UpperCAmelCase_    				=							self.normalize(image=_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase					)

             UpperCAmelCase_    				=							to_channel_dimension_format(_UpperCAmelCase	,       _UpperCAmelCase					)
             return image







       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						ImageInput	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						PILImageResampling = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[str, TensorType]] = None	,       _UpperCAmelCase    :						ChannelDimension = ChannelDimension.FIRST	,       **_UpperCAmelCase    :						int	,       )				->		PIL.Image.Image:


             '''simple docstring'''




             UpperCAmelCase_    				=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_    				=							resample if resample is not None else self.resample
             UpperCAmelCase_    				=							do_center_crop if do_center_crop is not None else self.do_center_crop
             UpperCAmelCase_    				=							do_rescale if do_rescale is not None else self.do_rescale
             UpperCAmelCase_    				=							rescale_factor if rescale_factor is not None else self.rescale_factor
             UpperCAmelCase_    				=							do_normalize if do_normalize is not None else self.do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else self.image_mean
             UpperCAmelCase_    				=							image_std if image_std is not None else self.image_std

             UpperCAmelCase_    				=							size if size is not None else self.size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             UpperCAmelCase_    				=							crop_size if crop_size is not None else self.crop_size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       param_name="crop_size"					)

             if not valid_images(_UpperCAmelCase					):
                   raise ValueError(
                       "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                       "torch.Tensor, tf.Tensor or jax.ndarray."					)

             UpperCAmelCase_    				=							make_batched(_UpperCAmelCase					)

             UpperCAmelCase_    				=							[
                 [
                     self._preprocess_image(
                         image=_UpperCAmelCase	,       do_resize=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       do_center_crop=_UpperCAmelCase	,       crop_size=_UpperCAmelCase	,       do_rescale=_UpperCAmelCase	,       rescale_factor=_UpperCAmelCase	,       do_normalize=_UpperCAmelCase	,       image_mean=_UpperCAmelCase	,       image_std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       )
                     for img in video
                 ]
                 for video in videos
             ]

             UpperCAmelCase_    				=							{"pixel_values": videos}
             return BatchFeature(data=_UpperCAmelCase	,       tensor_type=_UpperCAmelCase					)




