
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,
)

__snake_case				      =    logging.getLogger(__name__)

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






def 				_lowercase				(	)						->						Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					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.'						)

					SCREAMING_SNAKE_CASE__						=					parser.parse_args()

					return args






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_="cpu"						)						->						str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					model_dict[model_name].from_pretrained(_UpperCamelCase						).to(_UpperCamelCase						)
					SCREAMING_SNAKE_CASE__						=					tokenizer_dict[model_name].from_pretrained(_UpperCamelCase						)

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

					return huggingface_model, tokenizer






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						Dict:
					'''simple docstring'''


					model.eval()

					SCREAMING_SNAKE_CASE__						=					None
					SCREAMING_SNAKE_CASE__						=					torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase						)						)

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

										SCREAMING_SNAKE_CASE__						=					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						)						)

										SCREAMING_SNAKE_CASE__						=					remove_dup_initializers(os.path.abspath(_UpperCamelCase						)						)

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

										SCREAMING_SNAKE_CASE__						=					onnxruntime.InferenceSession(_UpperCamelCase						)
										SCREAMING_SNAKE_CASE__						=					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 				_lowercase				(	)						->						Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					parse_args()
					SCREAMING_SNAKE_CASE__						=					5
					SCREAMING_SNAKE_CASE__						=					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()

					SCREAMING_SNAKE_CASE__						=					torch.device(args.device						)

					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__						=					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:
										SCREAMING_SNAKE_CASE__						=					args.max_length

					if args.num_beams:
										SCREAMING_SNAKE_CASE__						=					args.num_beams

					if args.output_file_path:
										SCREAMING_SNAKE_CASE__						=					args.output_file_path
					else:
										SCREAMING_SNAKE_CASE__						=					'BART.onnx'

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


if __name__ == "__main__":
				main()









from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							split=snake_case_			,							features=snake_case_			,							cache_dir=snake_case_			,							keep_in_memory=snake_case_			,							streaming=snake_case_			,							num_proc=snake_case_			,							**snake_case_			,							)
								__lowercase			       = path_or_paths if isinstance(snake_case_			,							snake_case_    ) else {self.split: path_or_paths}
								__lowercase			       = Text(
								    cache_dir=snake_case_			,							data_files=snake_case_			,							features=snake_case_			,							**snake_case_			,							)






					def A   (					self    ) ->      int:







								'''simple docstring'''

								if self.streaming:
											__lowercase			       = self.builder.as_streaming_dataset(split=self.split    )
								# Build regular (map-style) dataset
								else:
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None

											self.builder.download_and_prepare(
											    download_config=snake_case_			,							download_mode=snake_case_			,							verification_mode=snake_case_			,							base_path=snake_case_			,							num_proc=self.num_proc			,							)
											__lowercase			       = self.builder.as_dataset(
											    split=self.split			,							verification_mode=snake_case_			,							in_memory=self.keep_in_memory    )
								return dataset


