


import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


a			:			Dict												=					get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
	from transformers.models.plbart.modeling_plbart import shift_tokens_right

a			:			Optional[Any]												=					50003
a			:			List[Any]												=					50002
@require_sentencepiece
@require_tokenizers
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					PLBartTokenizer
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					False






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								super().setUp()

								# We have a SentencePiece fixture for testing
								__lowercase			       = PLBartTokenizer(snake_case_			,							language_codes='''base'''			,							keep_accents=snake_case_    )
								tokenizer.save_pretrained(self.tmpdirname    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = PLBartTokenizer(snake_case_			,							language_codes='''base'''			,							keep_accents=snake_case_    )

								__lowercase			       = tokenizer.tokenize('''This is a test'''    )
								self.assertListEqual(snake_case_			,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']    )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(snake_case_    )			,							[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,							)

								__lowercase			       = tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
								self.assertListEqual(
								    snake_case_			,							[
								        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''',
								        '''.''',
								    ]			,							)
								__lowercase			       = tokenizer.convert_tokens_to_ids(snake_case_    )
								self.assertListEqual(
								    snake_case_			,							[
								        value + tokenizer.fairseq_offset
								        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
								    ]			,							)

								__lowercase			       = tokenizer.convert_ids_to_tokens(snake_case_    )
								self.assertListEqual(
								    snake_case_			,							[
								        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>''',
								        '''.''',
								    ]			,							)

								__lowercase			       = tokenizer.vocab_size
								__lowercase			       = [tokenizer.convert_ids_to_tokens(snake_case_    ) for x in range(end - 4			,							snake_case_    )]

								self.assertListEqual(snake_case_			,							['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>''']    )

								__lowercase			       = '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
								__lowercase			       = tokenizer(snake_case_    ).input_ids
								self.assertEqual(
								    tokenizer.decode(snake_case_			,							skip_special_tokens=snake_case_			,							clean_up_tokenization_spaces=snake_case_    )			,							snake_case_			,							)






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = PLBartTokenizer(snake_case_			,							language_codes='''multi'''			,							keep_accents=snake_case_    )

								__lowercase			       = tokenizer.tokenize('''This is a test'''    )
								self.assertListEqual(snake_case_			,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']    )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(snake_case_    )			,							[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,							)

								__lowercase			       = tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
								self.assertListEqual(
								    snake_case_			,							[
								        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''',
								        '''.''',
								    ]			,							)
								__lowercase			       = tokenizer.convert_tokens_to_ids(snake_case_    )
								self.assertListEqual(
								    snake_case_			,							[
								        value + tokenizer.fairseq_offset
								        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
								    ]			,							)

								__lowercase			       = tokenizer.convert_ids_to_tokens(snake_case_    )
								self.assertListEqual(
								    snake_case_			,							[
								        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>''',
								        '''.''',
								    ]			,							)
								__lowercase			       = tokenizer.vocab_size
								__lowercase			       = [tokenizer.convert_ids_to_tokens(snake_case_    ) for x in range(end - 7			,							snake_case_    )]

								self.assertListEqual(
								    snake_case_			,							['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']    )
								__lowercase			       = '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
								__lowercase			       = tokenizer(snake_case_    ).input_ids
								self.assertEqual(
								    tokenizer.decode(snake_case_			,							skip_special_tokens=snake_case_			,							clean_up_tokenization_spaces=snake_case_    )			,							snake_case_			,							)




@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"uclanlp/plbart-python-en_XX"
					__UpperCAmelCase							  =					[
					    "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
					    "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
					]
					__UpperCAmelCase							  =					[
					    "Returns the maximum value of a b c.",
					    "Sums the values of a b c.",
					]
					__UpperCAmelCase							  =					[
					    134,
					    5_452,
					    33_460,
					    33_441,
					    33_463,
					    33_465,
					    33_463,
					    33_449,
					    988,
					    20,
					    33_456,
					    19,
					    33_456,
					    771,
					    39,
					    4_258,
					    889,
					    3_318,
					    33_441,
					    33_463,
					    33_465,
					    33_463,
					    33_449,
					    2_471,
					    2,
					    PYTHON_CODE,
					]






					@classmethod
					def A   (					cls    ) ->      int:







								'''simple docstring'''

								__lowercase			       = PLBartTokenizer.from_pretrained(
								    cls.checkpoint_name			,							language_codes='''base'''			,							src_lang='''python'''			,							tgt_lang='''en_XX'''    )
								__lowercase			       = 1
								return cls






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']			,							5_0_0_0_1    )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']			,							5_0_0_0_2    )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']			,							5_0_0_0_3    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens			,							snake_case_    )






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								self.assertIn(snake_case_			,							self.tokenizer.all_special_ids    )
								__lowercase			       = [EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
								__lowercase			       = self.tokenizer.decode(snake_case_			,							skip_special_tokens=snake_case_    )
								__lowercase			       = self.tokenizer.decode(generated_ids[1:]			,							skip_special_tokens=snake_case_    )
								self.assertEqual(snake_case_			,							snake_case_    )
								self.assertNotIn(self.tokenizer.eos_token			,							snake_case_    )






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







								'''simple docstring'''

								__lowercase			       = ['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 2_0]
								self.assertIsInstance(src_text[0]			,							snake_case_    )
								__lowercase			       = 1_0
								__lowercase			       = self.tokenizer(snake_case_			,							max_length=snake_case_			,							truncation=snake_case_    ).input_ids[0]
								self.assertEqual(ids[-2]			,							2    )
								self.assertEqual(ids[-1]			,							snake_case_    )
								self.assertEqual(len(snake_case_    )			,							snake_case_    )






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']    )			,							[5_0_0_0_4, 5_0_0_0_1]    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = tempfile.mkdtemp()
								__lowercase			       = self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(snake_case_    )
								__lowercase			       = PLBartTokenizer.from_pretrained(snake_case_    )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,							snake_case_    )






					@require_torch
					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.tokenizer(self.src_text			,							text_target=self.tgt_text			,							padding=snake_case_			,							return_tensors='''pt'''    )
								__lowercase			       = shift_tokens_right(batch['''labels''']			,							self.tokenizer.pad_token_id    )

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								self.assertEqual(batch.input_ids[1][-2:].tolist()			,							[2, PYTHON_CODE]    )
								self.assertEqual(batch.decoder_input_ids[1][0]			,							snake_case_    )
								self.assertEqual(batch.decoder_input_ids[1][-1]			,							2    )
								self.assertEqual(batch.labels[1][-2:].tolist()			,							[2, EN_CODE]    )






					@require_torch
					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.tokenizer(
								    self.src_text			,							text_target=self.tgt_text			,							padding=snake_case_			,							truncation=snake_case_			,							max_length=len(self.expected_src_tokens    )			,							return_tensors='''pt'''			,							)
								__lowercase			       = shift_tokens_right(batch['''labels''']			,							self.tokenizer.pad_token_id    )

								self.assertIsInstance(snake_case_			,							snake_case_    )

								self.assertEqual((2, 2_6)			,							batch.input_ids.shape    )
								self.assertEqual((2, 2_6)			,							batch.attention_mask.shape    )
								__lowercase			       = batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens			,							snake_case_    )
								self.assertEqual(2			,							batch.decoder_input_ids[0, -1]    )  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens			,							[]    )
								self.assertEqual(self.tokenizer.suffix_tokens			,							[self.tokenizer.eos_token_id, PYTHON_CODE]    )






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.tokenizer(self.src_text			,							padding=snake_case_			,							truncation=snake_case_			,							max_length=3			,							return_tensors='''pt'''    )
								__lowercase			       = self.tokenizer(
								    text_target=self.tgt_text			,							padding=snake_case_			,							truncation=snake_case_			,							max_length=1_0			,							return_tensors='''pt'''    )
								__lowercase			       = targets['''input_ids''']
								__lowercase			       = shift_tokens_right(snake_case_			,							self.tokenizer.pad_token_id    )

								self.assertEqual(batch.input_ids.shape[1]			,							3    )
								self.assertEqual(batch.decoder_input_ids.shape[1]			,							1_0    )






					@require_torch
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.tokenizer._build_translation_inputs(
								    '''A test'''			,							return_tensors='''pt'''			,							src_lang='''en_XX'''			,							tgt_lang='''java'''    )

								self.assertEqual(
								    nested_simplify(snake_case_    )			,							{
								        # A, test, EOS, en_XX
								        '''input_ids''': [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
								        '''attention_mask''': [[1, 1, 1, 1]],
								        # java
								        '''forced_bos_token_id''': 5_0_0_0_1,
								    }			,							)






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,
)

a			:			Union[str, Any]												=					logging.getLogger(__name__)

a			:			Any												=					{'''facebook/bart-base''': BartForConditionalGeneration}
a			:			int												=					{'''facebook/bart-base''': BartTokenizer}


def 		lowercase_	(  ):


			'''simple docstring'''







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

			__lowercase			       = parser.parse_args()

			return args


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase="cpu"	):


			'''simple docstring'''







			__lowercase			       = model_dict[model_name].from_pretrained(_UpperCamelCase	).to(_UpperCamelCase	)
			__lowercase			       = tokenizer_dict[model_name].from_pretrained(_UpperCamelCase	)

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

			return huggingface_model, tokenizer


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			model.eval()

			__lowercase			       = None
			__lowercase			       = torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase	)	)

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

						__lowercase			       = model.generate(
						    inputs['''input_ids''']							,      attention_mask=inputs['''attention_mask''']							,      num_beams=_UpperCamelCase							,      max_length=_UpperCamelCase							,      early_stopping=_UpperCamelCase							,      decoder_start_token_id=model.config.decoder_start_token_id							,      )

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

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

						__lowercase			       = remove_dup_initializers(os.path.abspath(_UpperCamelCase	)	)

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

						__lowercase			       = onnxruntime.InferenceSession(_UpperCamelCase	)
						__lowercase			       = ort_sess.run(
						    _UpperCamelCase							,      {
						        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
						        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
						        '''num_beams''': np.array(_UpperCamelCase	),
						        '''max_length''': np.array(_UpperCamelCase	),
						        '''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'''







			__lowercase			       = parse_args()
			__lowercase			       = 5
			__lowercase			       = 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()

			__lowercase			       = torch.device(args.device	)

			__lowercase       ,			__lowercase			       = load_model_tokenizer(args.model_name_or_path							,      _UpperCamelCase	)

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

			model.to(_UpperCamelCase	)

			if args.max_length:
						__lowercase			       = args.max_length

			if args.num_beams:
						__lowercase			       = args.num_beams

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

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


if __name__ == "__main__":
	main()


