





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 :Dict     =    logging.getLogger(__name__)

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







def    A				(							):
					_snake_case       :					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."				)

					_snake_case       :					List[Any]				       =   parser.parse_args()

					return args







def    A				(							UpperCAmelCase  ,  UpperCAmelCase="cpu"				):
					_snake_case       :					Optional[int]				       =   model_dict[model_name].from_pretrained(UpperCAmelCase				).to(UpperCAmelCase				)
					_snake_case       :					Optional[int]				       =   tokenizer_dict[model_name].from_pretrained(UpperCAmelCase				)

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

					return huggingface_model, tokenizer







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					model.eval()

					_snake_case       :					Dict				       =   None
					_snake_case       :					int				       =   torch.jit.script(BARTBeamSearchGenerator(UpperCAmelCase				)				)

					with torch.no_grad():
										_snake_case       :					str				       =   "My friends are cool but they eat too many carbs."
										_snake_case       :					Optional[Any]				       =   tokenizer([ARTICLE_TO_SUMMARIZE]  ,  max_length=1_024  ,  return_tensors="pt"				).to(model.device				)

										_snake_case       :					int				       =   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				)				)

										_snake_case       :					Dict				       =   remove_dup_initializers(os.path.abspath(UpperCAmelCase				)				)

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

										_snake_case       :					str				       =   onnxruntime.InferenceSession(UpperCAmelCase				)
										_snake_case       :					str				       =   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    A				(							):
					_snake_case       :					Union[str, Any]				       =   parse_args()
					_snake_case       :					str				       =   5
					_snake_case       :					Optional[int]				       =   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()

					_snake_case       :					Optional[Any]				       =   torch.device(args.device				)

					_snake_case       , _snake_case       :					Any				       =   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:
										_snake_case       :					Optional[int]				       =   args.max_length

					if args.num_beams:
										_snake_case       :					str				       =   args.num_beams

					if args.output_file_path:
										_snake_case       :					Dict				       =   args.output_file_path
					else:
										_snake_case       :					str				       =   "BART.onnx"

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


if __name__ == "__main__":
							main()






import numpy as np

__lowerCAmelCase :Dict     =    [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class        _a:







		def __init__(    self				)      ->      None:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   np.array(__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      np.ndarray:


							'''simple docstring'''

							_snake_case       , _snake_case       :					List[Any]				       =   np.where(letter == self.SQUARE				)
							_snake_case       :					Any				       =   np.concatenate([indexa + 1, indexa + 1]				)
							return indexes







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   self.SQUARE[indexa - 1, indexa - 1]
							return letter







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   message.lower()
							_snake_case       :					List[Any]				       =   message.replace(" " ,						""				)
							_snake_case       :					Any				       =   message.replace("j" ,						"i"				)

							_snake_case       :					Tuple				       =   np.empty((2, len(__snake_case				))				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   self.letter_to_numbers(message[letter_index]				)

												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					List[Any]				       =   numbers[1]

							_snake_case       :					Any				       =   first_step.reshape(2 * len(__snake_case				)				)
							_snake_case       :					Optional[Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Optional[int]				       =   int(second_step[numbers_index * 2]				)
												_snake_case       :					List[str]				       =   int(second_step[(numbers_index * 2) + 1]				)
												_snake_case       :					Any				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					Any				       =   encoded_message + letter

							return encoded_message







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   message.lower()
							message.replace(" " ,						""				)
							_snake_case       :					Tuple				       =   np.empty(2 * len(__snake_case				)				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Union[str, Any]				       =   self.letter_to_numbers(message[letter_index]				)
												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					Any				       =   numbers[1]

							_snake_case       :					Optional[Any]				       =   first_step.reshape((2, len(__snake_case				))				)
							_snake_case       :					Union[str, Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   int(second_step[0, numbers_index]				)
												_snake_case       :					Optional[Any]				       =   int(second_step[1, numbers_index]				)
												_snake_case       :					Union[str, Any]				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					str				       =   decoded_message + letter

							return decoded_message