




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,
)

UpperCamelCase__    			=			logging.getLogger(__name__)

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







def 	_a      (						):
					__lowerCAmelCase				   =       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."     )

					__lowerCAmelCase				   =       parser.parse_args()

					return args







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]="cpu"     ):
					__lowerCAmelCase				   =       model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_     )

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

					return huggingface_model, tokenizer







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					model.eval()

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

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

										__lowerCAmelCase				   =       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_     )     )

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

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

										__lowerCAmelCase				   =       onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       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 	_a      (						):
					__lowerCAmelCase				   =       parse_args()
					__lowerCAmelCase				   =       5
					__lowerCAmelCase				   =       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()

					__lowerCAmelCase				   =       torch.device(args.device     )

					__lowerCAmelCase   ,							__lowerCAmelCase				   =       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:
										__lowerCAmelCase				   =       args.max_length

					if args.num_beams:
										__lowerCAmelCase				   =       args.num_beams

					if args.output_file_path:
										__lowerCAmelCase				   =       args.output_file_path
					else:
										__lowerCAmelCase				   =       "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()








class 			_lowercase :  # Public class to implement a graph


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  row
             lowerCamelCase__		:				Optional[Any]    =  col
             lowerCamelCase__		:				Union[str, Any]    =  graph





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowerCamelCase__		:				Tuple    =  [-1, 0, 1, -1, 1, -1, 0, 1]
             lowerCamelCase__		:				List[Any]    =  True  # Make those cells visited
             for k in range(8       ):
                   if self.is_safe(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       ):
                         self.diffs(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):  # And finally, count all islands.


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  [[False for j in range(self.COL       )] for i in range(self.ROW       )]
             lowerCamelCase__		:				Optional[Any]    =  0
             for i in range(self.ROW       ):
                   for j in range(self.COL       ):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                               self.diffs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                               count += 1
             return count


