




'''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,
)

_lowercase  =			logging.getLogger(__name__)

_lowercase  =			{"""facebook/bart-base""": BartForConditionalGeneration}
_lowercase  =			{"""facebook/bart-base""": BartTokenizer}
def 						lowerCamelCase__   (				):
						__snake_case						 =   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.'							)

						__snake_case						 =   parser.parse_args()

						return args
def 						lowerCamelCase__   (				a    ,	a="cpu"							):
						__snake_case						 =   model_dict[model_name].from_pretrained(a							).to(a							)
						__snake_case						 =   tokenizer_dict[model_name].from_pretrained(a							)

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

						return huggingface_model, tokenizer
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a							):
						model.eval()

						__snake_case						 =   None
						__snake_case						 =   torch.jit.script(BARTBeamSearchGenerator(a							)							)

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

												__snake_case						 =   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							)							)

												__snake_case						 =   remove_dup_initializers(os.path.abspath(a							)							)

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

												__snake_case						 =   onnxruntime.InferenceSession(a							)
												__snake_case						 =   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 						lowerCamelCase__   (				):
						__snake_case						 =   parse_args()
						__snake_case						 =   5
						__snake_case						 =   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()

						__snake_case						 =   torch.device(args.device							)

						__snake_case ,       __snake_case						 =   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:
												__snake_case						 =   args.max_length

						if args.num_beams:
												__snake_case						 =   args.num_beams

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

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


if __name__ == "__main__":
	main()







'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFDebertaVaForMaskedLM,
	    TFDebertaVaForQuestionAnswering,
	    TFDebertaVaForSequenceClassification,
	    TFDebertaVaForTokenClassification,
	    TFDebertaVaModel,
	)
class 						a_				:




	def __init__(       self     :  Tuple       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]=1_3       ,		__lowerCAmelCase     :  int=7       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  Optional[Any]=True       ,		__lowerCAmelCase     :  List[Any]=9_9       ,		__lowerCAmelCase     :  List[str]=3_2       ,		__lowerCAmelCase     :  List[Any]=2       ,		__lowerCAmelCase     :  Any=4       ,		__lowerCAmelCase     :  Any=3_7       ,		__lowerCAmelCase     :  Union[str, Any]="gelu"       ,		__lowerCAmelCase     :  Any=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Optional[int]=5_1_2       ,		__lowerCAmelCase     :  Optional[Any]=1_6       ,		__lowerCAmelCase     :  Optional[Any]=2       ,		__lowerCAmelCase     :  Dict=0.02       ,		__lowerCAmelCase     :  Dict=False       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  Tuple="None"       ,		__lowerCAmelCase     :  Optional[int]=3       ,		__lowerCAmelCase     :  Optional[int]=4       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_input_mask
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   relative_attention
							__snake_case						 =   position_biased_input
							__snake_case						 =   pos_att_type
							__snake_case						 =   scope




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

							__snake_case						 =   None
							if self.use_input_mask:
													__snake_case						 =   random_attention_mask([self.batch_size, self.seq_length]      )

							__snake_case						 =   None
							if self.use_token_type_ids:
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.type_vocab_size      )

							__snake_case						 =   None
							__snake_case						 =   None
							__snake_case						 =   None
							if self.use_labels:
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels      )

							__snake_case						 =   DebertaVaConfig(
							    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		num_attention_heads=self.num_attention_heads       ,		intermediate_size=self.intermediate_size       ,		hidden_act=self.hidden_act       ,		hidden_dropout_prob=self.hidden_dropout_prob       ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,		max_position_embeddings=self.max_position_embeddings       ,		type_vocab_size=self.type_vocab_size       ,		relative_attention=self.relative_attention       ,		position_biased_input=self.position_biased_input       ,		initializer_range=self.initializer_range       ,		return_dict=__lowerCAmelCase       ,		)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]      ):
							__snake_case						 =   TFDebertaVaModel(config=__lowerCAmelCase      )
							__snake_case						 =   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

							__snake_case						 =   [input_ids, input_mask]
							__snake_case						 =   model(__lowerCAmelCase      )

							__snake_case						 =   model(__lowerCAmelCase      )

							self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   TFDebertaVaForMaskedLM(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFDebertaVaForSequenceClassification(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)      )




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFDebertaVaForTokenClassification(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)      )




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.start_logits.shape       ,		(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape       ,		(self.batch_size, self.seq_length)      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_tf
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[str]			  =      (
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase_       :     Union[str, Any]			  =      (
	    {
	        '''feature-extraction''': TFDebertaVaModel,
	        '''fill-mask''': TFDebertaVaForMaskedLM,
	        '''question-answering''': TFDebertaVaForQuestionAnswering,
	        '''text-classification''': TFDebertaVaForSequenceClassification,
	        '''token-classification''': TFDebertaVaForTokenClassification,
	        '''zero-shot''': TFDebertaVaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	lowercase_       :     int			  =      False
	lowercase_       :     List[Any]			  =      False




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   TFDebertaVaModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		hidden_size=3_7      )




	def   lowercase__   (       self     :  Dict      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[int]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
							self.assertIsNotNone(__lowerCAmelCase      )





@require_tf
class 						a_				(			unittest.TestCase						):




	@unittest.skip(reason='Model not available yet'      )
	def   lowercase__   (       self     :  List[str]      ):
							pass




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
							__snake_case						 =   tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
							__snake_case						 =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )[0]

							__snake_case						 =   tf.constant(
							    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]      )
							tf.debugging.assert_near(output[:, 1:4, 1:4]       ,		__lowerCAmelCase       ,		atol=1E-4      )

