






'''simple docstring'''







from ...processing_utils import ProcessorMixin





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					["image_processor", "feature_extractor"]
			snake_case__	=					"TvltImageProcessor"
			snake_case__	=					"TvltFeatureExtractor"






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      List[Any]:
							super().__init__(image_processor=__SCREAMING_SNAKE_CASE	,    feature_extractor=__SCREAMING_SNAKE_CASE       )

							a_		:	Any									=   image_processor
							a_		:	Optional[int]									=   feature_extractor






			def __call__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=None	,    __SCREAMING_SNAKE_CASE   :      Any=None	,    __SCREAMING_SNAKE_CASE   :      int=None	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=None	,    __SCREAMING_SNAKE_CASE   :      List[str]=False	,    __SCREAMING_SNAKE_CASE   :      List[Any]=False	,    *__SCREAMING_SNAKE_CASE   :      int	,    **__SCREAMING_SNAKE_CASE   :      Tuple	,    )      ->      List[str]:

							if images is None and audio is None:
											raise ValueError('''You need to specify either an `images` or `audio` input to process.'''       )

							a_		:	Optional[Any]									=   None
							if images is not None:
											a_		:	Optional[int]									=   self.image_processor(__SCREAMING_SNAKE_CASE	,    mask_pixel=__SCREAMING_SNAKE_CASE	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
							if images_mixed is not None:
											a_		:	List[str]									=   self.image_processor(__SCREAMING_SNAKE_CASE	,    is_mixed=__SCREAMING_SNAKE_CASE	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
							if audio is not None:
											a_		:	Union[str, Any]									=   self.feature_extractor(
											    __SCREAMING_SNAKE_CASE	,    *__SCREAMING_SNAKE_CASE	,    sampling_rate=__SCREAMING_SNAKE_CASE	,    mask_audio=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )

							a_		:	List[Any]									=   {}
							if audio is not None:
											output_dict.update(__SCREAMING_SNAKE_CASE       )
							if images is not None:
											output_dict.update(__SCREAMING_SNAKE_CASE       )
							if images_mixed_dict is not None:
											output_dict.update(__SCREAMING_SNAKE_CASE       )
							return output_dict







			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Dict:
							a_		:	Tuple									=   self.image_processor.model_input_names
							a_		:	Tuple									=   self.feature_extractor.model_input_names
							return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names       )       )












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

				a_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

				if model_name in ["facebook/bart-base"]:
								a_		:	str									=   0
								a_		:	Any									=   None
								a_		:	int									=   0

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

				a_		:	Union[str, Any]									=   None
				a_		:	str									=   torch.jit.script(BARTBeamSearchGenerator(__A   )   )

				with torch.no_grad():
								a_		:	Any									=   '''My friends are cool but they eat too many carbs.'''
								a_		:	Dict									=   tokenizer([ARTICLE_TO_SUMMARIZE]    ,		max_length=10_24    ,		return_tensors='''pt'''   ).to(model.device   )

								a_		:	Dict									=   model.generate(
								    inputs['''input_ids''']    ,		attention_mask=inputs['''attention_mask''']    ,		num_beams=__A    ,		max_length=__A    ,		early_stopping=__A    ,		decoder_start_token_id=model.config.decoder_start_token_id    ,		)

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

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

								a_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

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

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[Any]									=   ort_sess.run(
								    __A    ,		{
								        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
								        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
								        '''num_beams''': np.array(__A   ),
								        '''max_length''': np.array(__A   ),
								        '''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 		_UpperCAmelCase (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[str]									=   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_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   load_model_tokenizer(args.model_name_or_path    ,		__A   )

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

				model.to(__A   )

				if args.max_length:
								a_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

				if args.output_file_path:
								a_		:	str									=   args.output_file_path
				else:
								a_		:	Union[str, Any]									=   '''BART.onnx'''

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


if __name__ == "__main__":
		main()




