




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,
)

lowercase__				:     Any 		=						logging.getLogger(__name__)

lowercase__				:     Optional[int] 		=						{'''facebook/bart-base''': BartForConditionalGeneration}
lowercase__				:     List[str] 		=						{'''facebook/bart-base''': BartTokenizer}


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	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."							)

	snake_case_		    =     parser.parse_args()

	return args


def 						lowerCamelCase__    (		_A     ,	_A="cpu"							):







	'''simple docstring'''





	snake_case_		    =     model_dict[model_name].from_pretrained(UpperCAmelCase_							).to(UpperCAmelCase_							)
	snake_case_		    =     tokenizer_dict[model_name].from_pretrained(UpperCAmelCase_							)

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

	return huggingface_model, tokenizer


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





	model.eval()

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

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

		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_							)							)

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

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

		snake_case_		    =     onnxruntime.InferenceSession(UpperCAmelCase_							)
		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__    (		):







	'''simple docstring'''





	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_		    =     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_		    =     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(UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_							)


if __name__ == "__main__":
		main()




import math







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase=0    ):  # a graph with Node 0,1,...,N-1
       a						:Optional[int] 							=       n
       a						:Union[str, Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # adjacency matrix for weight
       a						:List[Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # dp[i][j] stores minimum distance from i to j




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Tuple 							=       w




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for k in range(0					,  self.n    ):
             for i in range(0					,  self.n    ):
                   for j in range(0					,  self.n    ):
                         a						:Union[str, Any] 							=       min(self.dp[i][j]					,  self.dp[i][k] + self.dp[k][j]    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return self.dp[u][v]


if __name__ == "__main__":
     snake_case				:    str       				=     Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 10)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 10)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)





