
"""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,
)

__magic_name__       =			logging.getLogger(__name__)

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



def 						_lowerCAmelCase				(				):
		__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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_="cpu"				):
		__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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		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 						_lowerCAmelCase				(				):
		__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()






"""simple docstring"""





from __future__ import annotations

from fractions import Fraction



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = 11
		__SCREAMING_SNAKE_CASE            = int("""1""" + """0""" * digit_len				)
		for num in range(UpperCamelCase_  ,				UpperCamelCase_				):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(UpperCamelCase_  ,				UpperCamelCase_				):
										solutions.append(f"{num}/{den}"				)
						den += 1
				num += 1
				__SCREAMING_SNAKE_CASE            = 10
		return solutions



def 						_lowerCAmelCase				(				UpperCamelCase_ = 2				):
		__SCREAMING_SNAKE_CASE            = 1.0
		for fraction in fraction_list(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = Fraction(UpperCamelCase_				)
				result *= frac.denominator / frac.numerator
		return int(UpperCamelCase_				)


if __name__ == "__main__":
							print(solution())




