

"""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,
)

__UpperCamelCase  :      List[Any]							      =		logging.getLogger(__name__)

__UpperCamelCase  :      Tuple							      =		{'''facebook/bart-base''': BartForConditionalGeneration}
__UpperCamelCase  :      Optional[int]							      =		{'''facebook/bart-base''': BartTokenizer}

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[Any]  				=	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.''' )

	lowerCAmelCase__	:						List[str]  				=	parser.parse_args()

	return args

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_="cpu" ):
	lowerCAmelCase__	:						Optional[int]  				=	model_dict[model_name].from_pretrained(A_ ).to(A_ )
	lowerCAmelCase__	:						Any  				=	tokenizer_dict[model_name].from_pretrained(A_ )

	if model_name in ["facebook/bart-base"]:
		lowerCAmelCase__	:						Dict  				=	0
		lowerCAmelCase__	:						Optional[Any]  				=	None
		lowerCAmelCase__	:						Dict  				=	0

	return huggingface_model, tokenizer

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_						,     A_ ):
	model.eval()

	lowerCAmelCase__	:						int  				=	None
	lowerCAmelCase__	:						Tuple  				=	torch.jit.script(BARTBeamSearchGenerator(A_ ) )

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

		lowerCAmelCase__	:						Any  				=	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_ ) )

		lowerCAmelCase__	:						Tuple  				=	remove_dup_initializers(os.path.abspath(A_ ) )

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

		lowerCAmelCase__	:						Tuple  				=	onnxruntime.InferenceSession(A_ )
		lowerCAmelCase__	:						List[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   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[Any]  				=	parse_args()
	lowerCAmelCase__	:						Union[str, Any]  				=	5
	lowerCAmelCase__	:						Union[str, Any]  				=	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()

	lowerCAmelCase__	:						List[str]  				=	torch.device(args.device )

	lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, 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:
		lowerCAmelCase__	:						Optional[int]  				=	args.max_length

	if args.num_beams:
		lowerCAmelCase__	:						Any  				=	args.num_beams

	if args.output_file_path:
		lowerCAmelCase__	:						str  				=	args.output_file_path
	else:
		lowerCAmelCase__	:						str  				=	'''BART.onnx'''

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


if __name__ == "__main__":
							main()







"""simple docstring"""





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    8_0_0,
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    6_0_0,
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    5_0_0,
    4_0_0,
    3_9_9,
    3_7_7,
    3_5_5,
    3_3_3,
    3_1_1,
    2_8_8,
    2_6_6,
    2_4_4,
    2_2_2,
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    1_5_5,
    1_3_3,
    1_1_1,
    8_8,
    6_6,
    4_4,
    2_2,
    0,
]

__UpperCamelCase  :      Tuple							      =		[
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    2_3_8,
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    1_4_2,
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    7_1,
    4_7,
    2_4,
    0,
]

__UpperCamelCase  :      Optional[int]							      =		[
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    9_4_4,
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    4_4,
    3_3,
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    9_7_8,
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    7_3_3,
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    6_7_7,
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    6_5_6,
    6_5_5,
    6_4_5,
    6_3_4,
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    5_7_0,
    5_6_9,
    5_5_5,
    5_4_1,
    5_2_7,
    5_2_6,
    5_0_5,
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    4_8_3,
    4_6_2,
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    4_3_9,
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    2_2_0,
    2_1_9,
    1_7_6,
    1_3_2,
    8_8,
    4_4,
    0,
]

__UpperCamelCase  :      str							      =		[
    9_9_9,
    9_9_7,
    9_9_5,
    9_9_2,
    9_9_0,
    9_8_8,
    9_8_6,
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    9_5_7,
    9_5_6,
    9_5_4,
    9_5_1,
    9_4_9,
    9_4_6,
    9_4_4,
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    9_2_9,
    9_2_6,
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    9_1_9,
    9_1_6,
    9_1_4,
    9_1_3,
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    9_0_7,
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    8_7_9,
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    7_3_3,
    7_3_0,
    7_2_6,
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    7_1_8,
    7_1_4,
    7_1_0,
    7_0_7,
    7_0_3,
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]

__UpperCamelCase  :      Dict							      =		[
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    3_0_0,
    2_0_0,
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]

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    9_9,
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]

__UpperCamelCase  :      Optional[int]							      =		[
    9_9_9,
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    9_9_2,
    9_8_9,
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    9_8_2,
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    6_6_6,
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    6_4_4,
    6_3_3,
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    5_8_5,
    5_7_1,
    5_5_7,
    5_4_2,
    5_2_8,
    5_1_4,
    5_0_0,
    4_9_9,
    4_8_5,
    4_7_1,
    4_5_7,
    4_4_2,
    4_2_8,
    4_1_4,
    4_0_0,
    3_9_9,
    3_7_9,
    3_5_9,
    3_4_0,
    3_2_0,
    3_0_0,
    2_9_9,
    2_7_9,
    2_5_9,
    2_4_0,
    2_2_0,
    2_0_0,
    1_9_9,
    1_6_6,
    1_3_3,
    1_0_0,
    9_9,
    6_6,
    3_3,
    0,
]




