



'''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,
)

__lowerCAmelCase		     =  logging.getLogger(__name__)

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



def 	__lowerCamelCase	( )     -> Union[str, Any]:
		_a	: List[str]								=				argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'    )
		parser.add_argument(
		    '--validation_file'		,     type=lowerCAmelCase_		,     default=lowerCAmelCase_		,     help='A csv or a json file containing the validation data.'    )
		parser.add_argument(
		    '--max_length'		,     type=lowerCAmelCase_		,     default=5		,     help='The maximum total input sequence length after tokenization.'		,     )
		parser.add_argument(
		    '--num_beams'		,     type=lowerCAmelCase_		,     default=lowerCAmelCase_		,     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=lowerCAmelCase_		,     help='Path to pretrained model or model identifier from huggingface.co/models.'		,     required=lowerCAmelCase_		,     )
		parser.add_argument(
		    '--config_name'		,     type=lowerCAmelCase_		,     default=lowerCAmelCase_		,     help='Pretrained config name or path if not the same as model_name'		,     )
		parser.add_argument(
		    '--device'		,     type=lowerCAmelCase_		,     default='cpu'		,     help='Device where the model will be run'		,     )
		parser.add_argument('--output_file_path'		,     type=lowerCAmelCase_		,     default=lowerCAmelCase_		,     help='Where to store the final ONNX file.'    )

		_a	: Dict								=				parser.parse_args()

		return args



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_="cpu"    )     -> Any:
		_a	: Tuple								=				model_dict[model_name].from_pretrained(lowerCAmelCase_    ).to(lowerCAmelCase_    )
		_a	: Optional[int]								=				tokenizer_dict[model_name].from_pretrained(lowerCAmelCase_    )

		if model_name in ["facebook/bart-base"]:
				_a	: Union[str, Any]								=				0
				_a	: Union[str, Any]								=				None
				_a	: Tuple								=				0

		return huggingface_model, tokenizer



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Union[str, Any]:
		model.eval()

		_a	: Optional[int]								=				None
		_a	: List[str]								=				torch.jit.script(BARTBeamSearchGenerator(lowerCAmelCase_    )    )

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

				_a	: Optional[int]								=				model.generate(
				    inputs['input_ids']		,     attention_mask=inputs['attention_mask']		,     num_beams=lowerCAmelCase_		,     max_length=lowerCAmelCase_		,     early_stopping=lowerCAmelCase_		,     decoder_start_token_id=model.config.decoder_start_token_id		,     )

				torch.onnx.export(
				    lowerCAmelCase_		,     (
				        inputs['input_ids'],
				        inputs['attention_mask'],
				        num_beams,
				        max_length,
				        model.config.decoder_start_token_id,
				    )		,     lowerCAmelCase_		,     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=lowerCAmelCase_		,     )

				logger.info('Model exported to {}'.format(lowerCAmelCase_    )    )

				_a	: Optional[Any]								=				remove_dup_initializers(os.path.abspath(lowerCAmelCase_    )    )

				logger.info('Deduplicated and optimized model written to {}'.format(lowerCAmelCase_    )    )

				_a	: List[str]								=				onnxruntime.InferenceSession(lowerCAmelCase_    )
				_a	: Tuple								=				ort_sess.run(
				    lowerCAmelCase_		,     {
				        'input_ids': inputs['input_ids'].cpu().numpy(),
				        'attention_mask': inputs['attention_mask'].cpu().numpy(),
				        'num_beams': np.array(lowerCAmelCase_    ),
				        'max_length': np.array(lowerCAmelCase_    ),
				        '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	( )     -> Optional[int]:
		_a	: Optional[int]								=				parse_args()
		_a	: Dict								=				5
		_a	: Dict								=				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()

		_a	: str								=				torch.device(args.device    )

		_a			,   _a	: List[Any]								=				load_model_tokenizer(args.model_name_or_path		,     lowerCAmelCase_    )

		if model.config.decoder_start_token_id is None:
				raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'    )

		model.to(lowerCAmelCase_    )

		if args.max_length:
				_a	: List[str]								=				args.max_length

		if args.num_beams:
				_a	: Optional[Any]								=				args.num_beams

		if args.output_file_path:
				_a	: int								=				args.output_file_path
		else:
				_a	: List[str]								=				'BART.onnx'

		logger.info('Exporting model to ONNX'    )
		export_and_validate_model(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )


if __name__ == "__main__":
							main()









'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCAmelCase		     =  TypeVar('''T''')

class       __magic_name__		(       Generic[T]  ):







							def __init__(				self   :			int      ,_UpperCAmelCase   :			bool = True						):
									_a	: dict[T, list[T]]								=				{}  # dictionary of lists
									_a	: Tuple								=				directed







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			T      ,_UpperCAmelCase   :			T						):

									if not self.directed:  # For undirected graphs
											# if both source vertex and destination vertex are both present in the
											# adjacency list, add destination vertex to source vertex list of adjacent
											# vertices and add source vertex to destination vertex list of adjacent
											# vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination vertex
											# to source vertex list of adjacent vertices, then create a new vertex with
											# destination vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: Optional[Any]								=				[source_vertex]
											# if only destination vertex is present in adjacency list, add source vertex
											# to destination vertex list of adjacent vertices, then create a new vertex
											# with source vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif destination_vertex in self.adj_list:
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
													_a	: Union[str, Any]								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and assign a list
											# containing the destination vertex as it's first adjacent vertex also
											# create a new vertex with destination vertex as key and assign a list
											# containing the source vertex as it's first adjacent vertex.
											else:
													_a	: Union[str, Any]								=				[destination_vertex]
													_a	: Optional[int]								=				[source_vertex]
									else:  # For directed graphs
											# if both source vertex and destination vertex are present in adjacency
											# list, add destination vertex to source vertex list of adjacent vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination
											# vertex to source vertex list of adjacent vertices and create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: int								=				[]
											# if only destination vertex is present in adjacency list, create a new
											# vertex with source vertex as key and assign a list containing destination
											# vertex as first adjacent vertex
											elif destination_vertex in self.adj_list:
													_a	: Tuple								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and a list containing
											# destination vertex as it's first adjacent vertex. Then create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											else:
													_a	: Tuple								=				[destination_vertex]
													_a	: str								=				[]

									return self







							def __repr__(				self   :			int						):
									return pformat(self.adj_list						)




