


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			: List[str]				   =					logging.getLogger(__name__)

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



def 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				Optional[Any] 						=						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.'					)

    _lowercase			:				Optional[Any] 						=						parser.parse_args()

    return args



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_="cpu"					)       ->		str:
    _lowercase			:				List[Any] 						=						model_dict[model_name].from_pretrained(lowerCamelCase_					).to(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						tokenizer_dict[model_name].from_pretrained(lowerCamelCase_					)

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

    return huggingface_model, tokenizer



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    model.eval()

    _lowercase			:				str 						=						None
    _lowercase			:				List[str] 						=						torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_					)					)

    with torch.no_grad():
        _lowercase			:				str 						=						'My friends are cool but they eat too many carbs.'
        _lowercase			:				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=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_					)					)

        _lowercase			:				Any 						=						remove_dup_initializers(os.path.abspath(lowerCamelCase_					)					)

        logger.info('Deduplicated and optimized model written to {}'.format(lowerCamelCase_					)					)

        _lowercase			:				int 						=						onnxruntime.InferenceSession(lowerCamelCase_					)
        _lowercase			:				Any 						=						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 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				int 						=						parse_args()
    _lowercase			:				Optional[Any] 						=						5
    _lowercase			:				str 						=						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[int] 						=						torch.device(args.device					)

    _lowercase						,							_lowercase			:				List[str] 						=						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:
        _lowercase			:				int 						=						args.max_length

    if args.num_beams:
        _lowercase			:				Union[str, Any] 						=						args.num_beams

    if args.output_file_path:
        _lowercase			:				List[Any] 						=						args.output_file_path
    else:
        _lowercase			:				List[Any] 						=						'BART.onnx'

    logger.info('Exporting model to ONNX'					)
    export_and_validate_model(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)


if __name__ == "__main__":
 main()










'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase      					=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase      					=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase      					=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase      					=re.compile(R"(_{2,})")

UpperCamelCase      					=R"^\w+(\.\w+)*$"

UpperCamelCase      					=R"<>:/\|?*"



def 				snake_case       (  a_				:			Union[str, Any]						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				_uppercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      UpperCamelCase_					: Any            =				_lowercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      return name.lower()



def 				snake_case       (  a_				:			str						)							->					Optional[int]:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				_single_underscore_re.split(a_						)
      UpperCamelCase_					: Any            =				[_multiple_underscores_re.split(a_						) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_						) if n != """"""						)



def 				snake_case       (  a_				:			str						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      return camelcase_to_snakecase(a_						)



def 				snake_case       (  a_				:			int     , a_				:			List[Any]						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      if not re.match(_split_re     , a_						):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."						)
      return f"{filename_prefix_for_name(a_						)}-{split}"



def 				snake_case       (  a_				:			str     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			Optional[int]=None						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				filename_prefix_for_split(a_     , a_						)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_					: List[str]            =				os.path.join(a_     , a_						)
      return f"{filepath}*"



def 				snake_case       (  a_				:			List[Any]     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			int=None     , a_				:			Dict=None						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				filename_prefix_for_split(a_     , a_						)
      UpperCamelCase_					: Union[str, Any]            =				os.path.join(a_     , a_						)

      if shard_lengths:
            UpperCamelCase_					: List[Any]            =				len(a_						)
            UpperCamelCase_					: Optional[int]            =				[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_						)]
            if filetype_suffix:
                  UpperCamelCase_					: Tuple            =				[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_					: List[Any]            =				prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






