



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,
)

lowercase_								=     logging.getLogger(__name__)

lowercase_								=     {"""facebook/bart-base""": BartForConditionalGeneration}
lowercase_								=     {"""facebook/bart-base""": BartTokenizer}



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

   lowercase__					  =				parser.parse_args()

   return args



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_="cpu"  ):
   lowercase__					  =				model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )
   lowercase__					  =				tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_  )

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

   return huggingface_model, tokenizer



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
   model.eval()

   lowercase__					  =				None
   lowercase__					  =				torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE_  )  )

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

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

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

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

      lowercase__					  =				remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE_  )  )

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

      lowercase__					  =				onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE_  )
      lowercase__					  =				ort_sess.run(
          SCREAMING_SNAKE_CASE_						,  {
              "input_ids": inputs["input_ids"].cpu().numpy(),
              "attention_mask": inputs["attention_mask"].cpu().numpy(),
              "num_beams": np.array(SCREAMING_SNAKE_CASE_  ),
              "max_length": np.array(SCREAMING_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 __lowerCAmelCase (     ):
   lowercase__					  =				parse_args()
   lowercase__					  =				5
   lowercase__					  =				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__					  =				torch.device(args.device  )

   lowercase__  ,       lowercase__					  =				load_model_tokenizer(args.model_name_or_path						,  SCREAMING_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(SCREAMING_SNAKE_CASE_  )

   if args.max_length:
      lowercase__					  =				args.max_length

   if args.num_beams:
      lowercase__					  =				args.num_beams

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

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


if __name__ == "__main__":
       main()









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XmodForCausalLM""",
							    """XmodForMaskedLM""",
							    """XmodForMultipleChoice""",
							    """XmodForQuestionAnswering""",
							    """XmodForSequenceClassification""",
							    """XmodForTokenClassification""",
							    """XmodModel""",
							    """XmodPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xmod import (
														    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XmodForCausalLM,
														    XmodForMaskedLM,
														    XmodForMultipleChoice,
														    XmodForQuestionAnswering,
														    XmodForSequenceClassification,
														    XmodForTokenClassification,
														    XmodModel,
														    XmodPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




