






'''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,
)

UpperCamelCase__ =			logging.getLogger(__name__)

UpperCamelCase__ =			{'facebook/bart-base': BartForConditionalGeneration}
UpperCamelCase__ =			{'facebook/bart-base': BartTokenizer}




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




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

   lowercase_	:    int           =     parser.parse_args()

   return args




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase="cpu"							):


   """simple docstring"""




   lowercase_	:    Dict           =     model_dict[model_name].from_pretrained(_UpperCamelCase							).to(_UpperCamelCase							)
   lowercase_	:    Tuple           =     tokenizer_dict[model_name].from_pretrained(_UpperCamelCase							)

   if model_name in ["facebook/bart-base"]:
      lowercase_	:    int           =     0
      lowercase_	:    List[str]           =     None
      lowercase_	:    Tuple           =     0

   return huggingface_model, tokenizer




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   model.eval()

   lowercase_	:    int           =     None
   lowercase_	:    str           =     torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase							)							)

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

      lowercase_	:    Optional[Any]           =     model.generate(
          inputs["input_ids"]						,		attention_mask=inputs["attention_mask"]						,		num_beams=_UpperCamelCase						,		max_length=_UpperCamelCase						,		early_stopping=_UpperCamelCase						,		decoder_start_token_id=model.config.decoder_start_token_id						,		)

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

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

      lowercase_	:    Tuple           =     remove_dup_initializers(os.path.abspath(_UpperCamelCase							)							)

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

      lowercase_	:    str           =     onnxruntime.InferenceSession(_UpperCamelCase							)
      lowercase_	:    Tuple           =     ort_sess.run(
          _UpperCamelCase						,		{
              "input_ids": inputs["input_ids"].cpu().numpy(),
              "attention_mask": inputs["attention_mask"].cpu().numpy(),
              "num_beams": np.array(_UpperCamelCase							),
              "max_length": np.array(_UpperCamelCase							),
              "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					(  ):


   """simple docstring"""




   lowercase_	:    List[str]           =     parse_args()
   lowercase_	:    Dict           =     5
   lowercase_	:    Optional[Any]           =     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()

   lowercase_	:    int           =     torch.device(args.device							)

   lowercase_    ,		lowercase_	:    int           =     load_model_tokenizer(args.model_name_or_path						,		_UpperCamelCase							)

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

   model.to(_UpperCamelCase							)

   if args.max_length:
      lowercase_	:    Dict           =     args.max_length

   if args.num_beams:
      lowercase_	:    Optional[int]           =     args.num_beams

   if args.output_file_path:
      lowercase_	:    List[Any]           =     args.output_file_path
   else:
      lowercase_	:    int           =     "BART.onnx"

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


if __name__ == "__main__":
 main()












'''simple docstring'''
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    'pipelines_utils',
    '0.22.0',
    'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
    standard_warn=False,
    stacklevel=3,
)




