


"""simple docstring"""
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							:      str						=      logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      {'facebook/bart-base': BartForConditionalGeneration}
__SCREAMING_SNAKE_CASE							:      int						=      {'facebook/bart-base': BartTokenizer}







def   _a			( )      ->			Optional[int]:
							snake_case_          =						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."""			)

							snake_case_          =						parser.parse_args()

							return args







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE="cpu"			)      ->			Union[str, Any]:
							snake_case_          =						model_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						tokenizer_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE			)

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

							return huggingface_model, tokenizer







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							model.eval()

							snake_case_          =						None
							snake_case_          =						torch.jit.script(BARTBeamSearchGenerator(_SCREAMING_SNAKE_CASE			)			)

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

														snake_case_          =						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			)			)

														snake_case_          =						remove_dup_initializers(os.path.abspath(_SCREAMING_SNAKE_CASE			)			)

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

														snake_case_          =						onnxruntime.InferenceSession(_SCREAMING_SNAKE_CASE			)
														snake_case_          =						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			( )      ->			Optional[int]:
							snake_case_          =						parse_args()
							snake_case_          =						5
							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()

							snake_case_          =						torch.device(args.device			)

							snake_case_    ,  snake_case_          =						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:
														snake_case_          =						args.max_length

							if args.num_beams:
														snake_case_          =						args.num_beams

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







"""simple docstring"""
from collections import defaultdict
from math import gcd







def   _a			( _SCREAMING_SNAKE_CASE = 1_500_000			)      ->			int:
							snake_case_          =						defaultdict(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						2
							while 2 * euclid_m * (euclid_m + 1) <= limit:
														for euclid_n in range((euclid_m % 2) + 1			,	_SCREAMING_SNAKE_CASE			,	2			):
																					if gcd(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) > 1:
																												continue
																					snake_case_          =						2 * euclid_m * (euclid_m + euclid_n)
																					for perimeter in range(_SCREAMING_SNAKE_CASE			,	limit + 1			,	_SCREAMING_SNAKE_CASE			):
																												frequencies[perimeter] += 1
														euclid_m += 1
							return sum(1 for frequency in frequencies.values() if frequency == 1			)


if __name__ == "__main__":
				print(f"""{solution() = }""")



