
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__     :		Optional[int]        = logging.getLogger(__name__)

lowerCamelCase__     :		int        = {"""facebook/bart-base""": BartForConditionalGeneration}
lowerCamelCase__     :		List[Any]        = {"""facebook/bart-base""": BartTokenizer}

def 		UpperCamelCase						(   )		->						Dict:


  '''simple docstring'''






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

  lowercase__	: str     		=   parser.parse_args()

  return args

def 		UpperCamelCase						(   lowercase_			,			lowercase_="cpu" )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   model_dict[model_name].from_pretrained(lowercase_ ).to(lowercase_ )
  lowercase__	: Tuple     		=   tokenizer_dict[model_name].from_pretrained(lowercase_ )

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

  return huggingface_model, tokenizer

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  model.eval()

  lowercase__	: int     		=   None
  lowercase__	: Any     		=   torch.jit.script(BARTBeamSearchGenerator(lowercase_ ) )

  with torch.no_grad():
    lowercase__	: Union[str, Any]     		=   """My friends are cool but they eat too many carbs."""
    lowercase__	: Dict     		=   tokenizer([ARTICLE_TO_SUMMARIZE]			,			max_length=10_24			,			return_tensors="""pt""" ).to(model.device )

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

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

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

    lowercase__	: str     		=   remove_dup_initializers(os.path.abspath(lowercase_ ) )

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

    lowercase__	: Optional[Any]     		=   onnxruntime.InferenceSession(lowercase_ )
    lowercase__	: Optional[int]     		=   ort_sess.run(
        lowercase_			,			{
            """input_ids""": inputs["""input_ids"""].cpu().numpy(),
            """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
            """num_beams""": np.array(lowercase_ ),
            """max_length""": np.array(lowercase_ ),
            """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						(   )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: Dict     		=   parse_args()
  lowercase__	: List[Any]     		=   5
  lowercase__	: 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__	: Optional[Any]     		=   torch.device(args.device )

  lowercase__		,     lowercase__	: str     		=   load_model_tokenizer(args.model_name_or_path			,			lowercase_ )

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

  model.to(lowercase_ )

  if args.max_length:
    lowercase__	: List[Any]     		=   args.max_length

  if args.num_beams:
    lowercase__	: List[Any]     		=   args.num_beams

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

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


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


lowerCamelCase__     :		Tuple        = {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		Optional[int]        = [
         """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MgpstrModel""",
         """MgpstrPreTrainedModel""",
         """MgpstrForSceneTextRecognition""",
     ]

if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
     from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mgp_str import (
              MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
              MgpstrForSceneTextRecognition,
              MgpstrModel,
              MgpstrPreTrainedModel,
          )
else:
     import sys

     lowerCamelCase__     :		List[Any]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






