




'''simple docstring'''
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


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


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

_SCREAMING_SNAKE_CASE			=						5_00_03
_SCREAMING_SNAKE_CASE			=						5_00_02
@require_sentencepiece
@require_tokenizers
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		PLBartTokenizer
					_UpperCamelCase							=		None
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						Union[str, Any])    ->							Tuple:
								super().setUp()

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



					def     __snake_case						(	self     :						Dict)    ->							Any:
								A_									= PLBartTokenizer(_lowercase      ,       language_codes='base'      ,       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>',
								        '.',
								    ]      ,       )

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

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

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



					def     __snake_case						(	self     :						List[str])    ->							List[str]:
								A_									= PLBartTokenizer(_lowercase      ,       language_codes='multi'      ,       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>',
								        '.',
								    ]      ,       )
								A_									= tokenizer.vocab_size
								A_									= [tokenizer.convert_ids_to_tokens(_lowercase) for x in range(end - 7      ,       _lowercase)]

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





@require_torch
@require_sentencepiece
@require_tokenizers
class   __UpperCAmelCase				(     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     __snake_case						(	cls     :						List[str])    ->							Tuple:
								A_									= PLBartTokenizer.from_pretrained(
								    cls.checkpoint_name      ,       language_codes='base'      ,       src_lang='python'      ,       tgt_lang='en_XX')
								A_									= 1
								return cls



					def     __snake_case						(	self     :						str)    ->							int:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__']      ,       50_001)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__']      ,       50_002)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__']      ,       50_003)



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



					def     __snake_case						(	self     :						List[str])    ->							int:
								self.assertIn(_lowercase      ,       self.tokenizer.all_special_ids)
								A_									= [EN_CODE, 9_037, 33_442, 57, 752, 153, 14, 56, 18, 9, 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     :						str)    ->							List[str]:
								A_									= ['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 20]
								self.assertIsInstance(src_text[0]      ,       _lowercase)
								A_									= 10
								A_									= self.tokenizer(_lowercase      ,       max_length=_lowercase      ,       truncation=_lowercase).input_ids[0]
								self.assertEqual(ids[-2]      ,       2)
								self.assertEqual(ids[-1]      ,       _lowercase)
								self.assertEqual(len(_lowercase)      ,       _lowercase)



					def     __snake_case						(	self     :						Dict)    ->							Optional[Any]:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__'])      ,       [50_004, 50_001])



					def     __snake_case						(	self     :						Tuple)    ->							str:
								A_									= tempfile.mkdtemp()
								A_									= self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(_lowercase)
								A_									= PLBartTokenizer.from_pretrained(_lowercase)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,       _lowercase)



					@require_torch
					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								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
								self.assertEqual(batch.input_ids[1][-2:].tolist()      ,       [2, PYTHON_CODE])
								self.assertEqual(batch.decoder_input_ids[1][0]      ,       _lowercase)
								self.assertEqual(batch.decoder_input_ids[1][-1]      ,       2)
								self.assertEqual(batch.labels[1][-2:].tolist()      ,       [2, EN_CODE])



					@require_torch
					def     __snake_case						(	self     :						List[str])    ->							int:
								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, 26)      ,       batch.input_ids.shape)
								self.assertEqual((2, 26)      ,       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, -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     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								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     :						List[str])    ->							Tuple:
								A_									= self.tokenizer._build_translation_inputs(
								    'A test'      ,       return_tensors='pt'      ,       src_lang='en_XX'      ,       tgt_lang='java')

								self.assertEqual(
								    nested_simplify(_lowercase)      ,       {
								        # A, test, EOS, en_XX
								        'input_ids': [[150, 242, 2, 50_003]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # java
								        'forced_bos_token_id': 50_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)

