




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   :						str				=			logging.getLogger(__name__)

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







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	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.'''				)

					__magic_name__							:	Optional[Any]    							=							parser.parse_args()

					return args







def 		lowercase__   (		__A:	Optional[Any]							,__A:	Tuple="cpu"				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							model_dict[model_name].from_pretrained(__A				).to(__A				)
					__magic_name__							:	int    							=							tokenizer_dict[model_name].from_pretrained(__A				)

					if model_name in ["facebook/bart-base"]:
										__magic_name__							:	Dict    							=							0
										__magic_name__							:	List[Any]    							=							None
										__magic_name__							:	Union[str, Any]    							=							0

					return huggingface_model, tokenizer







def 		lowercase__   (		__A:	Any							,__A:	Tuple							,__A:	Tuple							,__A:	Union[str, Any]							,__A:	List[Any]				):





					'''simple docstring'''






					model.eval()

					__magic_name__							:	Dict    							=							None
					__magic_name__							:	Dict    							=							torch.jit.script(BARTBeamSearchGenerator(__A				)				)

					with torch.no_grad():
										__magic_name__							:	Dict    							=							'''My friends are cool but they eat too many carbs.'''
										__magic_name__							:	List[Any]    							=							tokenizer([ARTICLE_TO_SUMMARIZE]							,max_length=1_0_2_4							,return_tensors='''pt'''				).to(model.device				)

										__magic_name__							:	Optional[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=1_4							,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				)				)

										__magic_name__							:	List[str]    							=							remove_dup_initializers(os.path.abspath(__A				)				)

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

										__magic_name__							:	Optional[Any]    							=							onnxruntime.InferenceSession(__A				)
										__magic_name__							:	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 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	int    							=							parse_args()
					__magic_name__							:	List[Any]    							=							5
					__magic_name__							:	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()

					__magic_name__							:	int    							=							torch.device(args.device				)

					__magic_name__							,    __magic_name__							:	Dict    							=							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:
										__magic_name__							:	Dict    							=							args.max_length

					if args.num_beams:
										__magic_name__							:	int    							=							args.num_beams

					if args.output_file_path:
										__magic_name__							:	Any    							=							args.output_file_path
					else:
										__magic_name__							:	Tuple    							=							'''BART.onnx'''

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


if __name__ == "__main__":
	main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



