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_    :		int							=	logging.getLogger(__name__)

lowerCamelCase_    :		Dict							=	{'facebook/bart-base': BartForConditionalGeneration}
lowerCamelCase_    :		Optional[int]							=	{'facebook/bart-base': BartTokenizer}
def 							lowerCAmelCase(     ):
					__a		 =       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		 =       parser.parse_args()

					return args
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase="cpu"						):
					__a		 =       model_dict[model_name].from_pretrained(lowerCamelCase_						).to(lowerCamelCase_						)
					__a		 =       tokenizer_dict[model_name].from_pretrained(lowerCamelCase_						)

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

					return huggingface_model, tokenizer
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					model.eval()

					__a		 =       None
					__a		 =       torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_						)						)

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

										__a		 =       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		 =       remove_dup_initializers(os.path.abspath(lowerCamelCase_						)						)

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

										__a		 =       onnxruntime.InferenceSession(lowerCamelCase_						)
										__a		 =       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(     ):
					__a		 =       parse_args()
					__a		 =       5
					__a		 =       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		 =       torch.device(args.device						)

					__a		 =       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		 =       args.max_length

					if args.num_beams:
										__a		 =       args.num_beams

					if args.output_file_path:
										__a		 =       args.output_file_path
					else:
										__a		 =       '''BART.onnx'''

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


if __name__ == "__main__":
	main()





import warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Any:
								warnings.warn(
								    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
								    'instead.'				,						UpperCAmelCase				,						)
								super().__init__(args=UpperCAmelCase				,						**UpperCAmelCase   )




