




'''simple docstring'''
import shutil
import tempfile
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE			=						get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE			=						25_00_04
_SCREAMING_SNAKE_CASE			=						25_00_20
@require_sentencepiece
@require_tokenizers
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		MBartaaTokenizer
					_UpperCamelCase							=		MBartaaTokenizerFast
					_UpperCamelCase							=		True
					_UpperCamelCase							=		True



					def     __snake_case						(	self     :						Any)    ->							Optional[Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								A_									= MBartaaTokenizer(_lowercase      ,       src_lang='en_XX'      ,       tgt_lang='ro_RO'      ,       keep_accents=_lowercase)
								tokenizer.save_pretrained(self.tmpdirname)



					def     __snake_case						(	self     :						Any)    ->							Dict:
								A_									= '<s>'
								A_									= 0

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



					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								A_									= list(self.get_tokenizer().get_vocab().keys())

								self.assertEqual(vocab_keys[0]      ,       '<s>')
								self.assertEqual(vocab_keys[1]      ,       '<pad>')
								self.assertEqual(vocab_keys[-1]      ,       '<mask>')
								self.assertEqual(len(_lowercase)      ,       1_054)



					def     __snake_case						(	self     :						int)    ->							str:
								self.assertEqual(self.get_tokenizer().vocab_size      ,       1_054)



					def     __snake_case						(	self     :						Optional[Any])    ->							Tuple:
								A_									= MBartaaTokenizer(_lowercase      ,       src_lang='en_XX'      ,       tgt_lang='ro_RO'      ,       keep_accents=_lowercase)

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

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(_lowercase)      ,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,       )

								A_									= tokenizer.tokenize('I was born in 92000, and this is falsé.')
								self.assertListEqual(
								    _lowercase      ,       [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', '.']      ,       )
								A_									= tokenizer.convert_tokens_to_ids(_lowercase)
								self.assertListEqual(
								    _lowercase      ,       [
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]      ,       )

								A_									= tokenizer.convert_ids_to_tokens(_lowercase)
								self.assertListEqual(
								    _lowercase      ,       [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>', '.']      ,       )



					@slow
					def     __snake_case						(	self     :						Tuple)    ->							Tuple:
								A_									= {'input_ids': [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 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], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 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]], '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, 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, 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=_lowercase      ,       model_name='facebook/mbart-large-50'      ,       revision='d3913889c59cd5c9e456b269c376325eabad57e2'      ,       )



					def     __snake_case						(	self     :						Tuple)    ->							List[Any]:







								if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

								A_									= (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
														A_									= self.rust_tokenizer_class.from_pretrained(_lowercase      ,       **_lowercase)
														A_									= self.tokenizer_class.from_pretrained(_lowercase      ,       **_lowercase)

														A_									= tempfile.mkdtemp()

														A_									= tokenizer_r.save_pretrained(_lowercase)
														A_									= tokenizer_p.save_pretrained(_lowercase)

														# Checks it save with the same files + the tokenizer.json file for the fast one
														self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
														A_									= tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
														self.assertSequenceEqual(_lowercase      ,       _lowercase)

														# Checks everything loads correctly in the same way
														A_									= tokenizer_r.from_pretrained(_lowercase)
														A_									= tokenizer_p.from_pretrained(_lowercase)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(_lowercase      ,       _lowercase))
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(_lowercase)

														# Save tokenizer rust, legacy_format=True
														A_									= tempfile.mkdtemp()

														A_									= tokenizer_r.save_pretrained(_lowercase      ,       legacy_format=_lowercase)
														A_									= tokenizer_p.save_pretrained(_lowercase)

														# Checks it save with the same files
														self.assertSequenceEqual(_lowercase      ,       _lowercase)

														# Checks everything loads correctly in the same way
														A_									= tokenizer_r.from_pretrained(_lowercase)
														A_									= tokenizer_p.from_pretrained(_lowercase)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(_lowercase      ,       _lowercase))

														shutil.rmtree(_lowercase)

														# Save tokenizer rust, legacy_format=False
														A_									= tempfile.mkdtemp()

														A_									= tokenizer_r.save_pretrained(_lowercase      ,       legacy_format=_lowercase)
														A_									= tokenizer_p.save_pretrained(_lowercase)

														# Checks it saved the tokenizer.json file
														self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))

														# Checks everything loads correctly in the same way
														A_									= tokenizer_r.from_pretrained(_lowercase)
														A_									= tokenizer_p.from_pretrained(_lowercase)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(_lowercase      ,       _lowercase))

														shutil.rmtree(_lowercase)





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



					'''simple docstring'''





					_UpperCamelCase							=		"""facebook/mbart-large-50-one-to-many-mmt"""
					_UpperCamelCase							=		[
					    """ UN Chief Says There Is No Military Solution in Syria""",
					    """ Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
					]
					_UpperCamelCase							=		[
					    """Şeful ONU declară că nu există o soluţie militară în Siria""",
					    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
					    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
					    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
					]
					_UpperCamelCase							=		[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]



					@classmethod
					def     __snake_case						(	cls     :						str)    ->							Any:
								A_									= MBartaaTokenizer.from_pretrained(
								    cls.checkpoint_name      ,       src_lang='en_XX'      ,       tgt_lang='ro_RO')
								A_									= 1
								return cls



					def     __snake_case						(	self     :						str)    ->							str:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']      ,       250_001)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']      ,       250_004)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']      ,       250_020)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']      ,       250_038)



					def     __snake_case						(	self     :						List[str])    ->							int:
								A_									= self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
								self.assertListEqual(self.expected_src_tokens      ,       _lowercase)



					def     __snake_case						(	self     :						Optional[int])    ->							int:
								self.assertIn(_lowercase      ,       self.tokenizer.all_special_ids)
								A_									= [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
								A_									= self.tokenizer.decode(_lowercase      ,       skip_special_tokens=_lowercase)
								A_									= self.tokenizer.decode(generated_ids[1:]      ,       skip_special_tokens=_lowercase)
								self.assertEqual(_lowercase      ,       _lowercase)
								self.assertNotIn(self.tokenizer.eos_token      ,       _lowercase)



					def     __snake_case						(	self     :						int)    ->							List[str]:
								A_									= ['this is gunna be a long sentence ' * 20]
								assert isinstance(src_text[0]      ,       _lowercase)
								A_									= 10
								A_									= self.tokenizer(_lowercase      ,       max_length=_lowercase      ,       truncation=_lowercase).input_ids[0]
								self.assertEqual(ids[0]      ,       _lowercase)
								self.assertEqual(ids[-1]      ,       2)
								self.assertEqual(len(_lowercase)      ,       _lowercase)



					def     __snake_case						(	self     :						int)    ->							Tuple:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])      ,       [250_053, 250_001])



					def     __snake_case						(	self     :						List[Any])    ->							List[Any]:
								A_									= tempfile.mkdtemp()
								A_									= self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(_lowercase)
								A_									= MBartaaTokenizer.from_pretrained(_lowercase)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,       _lowercase)



					@require_torch
					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.tokenizer(self.src_text      ,       text_target=self.tgt_text      ,       padding=_lowercase      ,       return_tensors='pt')
								A_									= shift_tokens_right(batch['labels']      ,       self.tokenizer.pad_token_id)

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == RO_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



					@require_torch
					def     __snake_case						(	self     :						Optional[int])    ->							Optional[Any]:
								A_									= self.tokenizer(
								    self.src_text      ,       text_target=self.tgt_text      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=len(self.expected_src_tokens)      ,       return_tensors='pt'      ,       )
								A_									= shift_tokens_right(batch['labels']      ,       self.tokenizer.pad_token_id)

								self.assertIsInstance(_lowercase      ,       _lowercase)

								self.assertEqual((2, 14)      ,       batch.input_ids.shape)
								self.assertEqual((2, 14)      ,       batch.attention_mask.shape)
								A_									= batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens      ,       _lowercase)
								self.assertEqual(2      ,       batch.decoder_input_ids[0, 0])  # decoder_start_token_id
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens      ,       [EN_CODE])
								self.assertEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id])



					def     __snake_case						(	self     :						str)    ->							Optional[int]:
								A_									= self.tokenizer(self.src_text      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=3      ,       return_tensors='pt')
								A_									= self.tokenizer(
								    text_target=self.tgt_text      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=10      ,       return_tensors='pt')
								A_									= targets['input_ids']
								A_									= shift_tokens_right(_lowercase      ,       self.tokenizer.pad_token_id)

								self.assertEqual(batch.input_ids.shape[1]      ,       3)
								self.assertEqual(batch.decoder_input_ids.shape[1]      ,       10)



					@require_torch
					def     __snake_case						(	self     :						Any)    ->							Union[str, Any]:
								A_									= self.tokenizer._build_translation_inputs(
								    'A test'      ,       return_tensors='pt'      ,       src_lang='en_XX'      ,       tgt_lang='ar_AR')

								self.assertEqual(
								    nested_simplify(_lowercase)      ,       {
								        # en_XX, A, test, EOS
								        'input_ids': [[250_004, 62, 3_034, 2]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 250_001,
								    }      ,       )










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""       ):
			a									=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""       ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""       ) or tensor_key.endswith(""".self.LayerNorm.bias"""       ):
			continue

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint-repo",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

