



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,
)

__snake_case    :		int  =logging.getLogger(__name__)

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






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					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.''')

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

				return args






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		str="cpu"):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					model_dict[model_name].from_pretrained(lowerCamelCase_).to(lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					tokenizer_dict[model_name].from_pretrained(lowerCamelCase_)

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

				return huggingface_model, tokenizer






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		Dict	,lowerCamelCase_		:		int	,lowerCamelCase_		:		Any):





				'''simple docstring'''

				model.eval()

				lowerCAmelCase__					:    Tuple												=					None
				lowerCAmelCase__					:    Optional[int]												=					torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_))

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

								lowerCAmelCase__					:    Tuple												=					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_))

								lowerCAmelCase__					:    int												=					remove_dup_initializers(os.path.abspath(lowerCamelCase_))

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

								lowerCAmelCase__					:    List[str]												=					onnxruntime.InferenceSession(lowerCamelCase_)
								lowerCAmelCase__					:    Dict												=					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__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					parse_args()
				lowerCAmelCase__					:    Union[str, Any]												=					5
				lowerCAmelCase__					:    int												=					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__					:    Any												=					torch.device(args.device)

				lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					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:
								lowerCAmelCase__					:    int												=					args.max_length

				if args.num_beams:
								lowerCAmelCase__					:    Optional[Any]												=					args.num_beams

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

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


if __name__ == "__main__":
							main()






import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case    :		int  =get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =XLMProphetNetTokenizer
		snake_case_     =False
		snake_case_     =True



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase__					:    int												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)
						tokenizer.save_pretrained(self.tmpdirname			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''[PAD]'''
						lowerCAmelCase__					:    Tuple												=					0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase			)		,__lowerCamelCase			)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase			)		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					list(self.get_tokenizer().get_vocab().keys()			)

						self.assertEqual(vocab_keys[0]		,'''[PAD]'''			)
						self.assertEqual(vocab_keys[1]		,'''[CLS]'''			)
						self.assertEqual(vocab_keys[-1]		,'''j'''			)
						self.assertEqual(len(__lowerCamelCase			)		,10_12			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size		,10_12			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					tokenizer.tokenize('''This is a test'''			)
						self.assertListEqual(__lowerCamelCase		,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)		,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,)

						lowerCAmelCase__					:    Dict												=					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        SPIECE_UNDERLINE + '''I''',
						        SPIECE_UNDERLINE + '''was''',
						        SPIECE_UNDERLINE + '''b''',
						        '''or''',
						        '''n''',
						        SPIECE_UNDERLINE + '''in''',
						        SPIECE_UNDERLINE + '''''',
						        '''9''',
						        '''2''',
						        '''0''',
						        '''0''',
						        '''0''',
						        ''',''',
						        SPIECE_UNDERLINE + '''and''',
						        SPIECE_UNDERLINE + '''this''',
						        SPIECE_UNDERLINE + '''is''',
						        SPIECE_UNDERLINE + '''f''',
						        '''al''',
						        '''s''',
						        '''é''',
						        '''.''',
						    ]		,)
						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]		,)

						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_ids_to_tokens(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        SPIECE_UNDERLINE + '''I''',
						        SPIECE_UNDERLINE + '''was''',
						        SPIECE_UNDERLINE + '''b''',
						        '''or''',
						        '''n''',
						        SPIECE_UNDERLINE + '''in''',
						        SPIECE_UNDERLINE + '''''',
						        '''[UNK]''',
						        '''2''',
						        '''0''',
						        '''0''',
						        '''0''',
						        ''',''',
						        SPIECE_UNDERLINE + '''and''',
						        SPIECE_UNDERLINE + '''this''',
						        SPIECE_UNDERLINE + '''is''',
						        SPIECE_UNDERLINE + '''f''',
						        '''al''',
						        '''s''',
						        '''[UNK]''',
						        '''.''',
						    ]		,)



		@cached_property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					'''Hello World!'''
						lowerCAmelCase__					:    str												=					[3_53_89, 66_72, 49, 2]
						self.assertListEqual(__lowerCamelCase		,self.big_tokenizer.encode(__lowerCamelCase			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					{'''input_ids''': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__lowerCamelCase		,model_name='''microsoft/xprophetnet-large-wiki100-cased'''		,revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''		,)

