



"""simple docstring"""

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


lowercase__    :  Union[str, Any]							    =	get_tests_dir('''fixtures/test_sentencepiece.model''')


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

lowercase__    :  Dict							    =	25_00_04
lowercase__    :  List[str]							    =	25_00_20




@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			List[str]							=		MBartTokenizer
 _lowerCAmelCase   :			str							=		MBartTokenizerFast
 _lowerCAmelCase   :			Dict							=		True
 _lowerCAmelCase   :			Tuple							=		True

 def 						_snake_case					(     self		:       List[str]				):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_ :     List[str]				=	MBartTokenizer(lowercase_  ,     keep_accents=lowercase_				)
     tokenizer.save_pretrained(self.tmpdirname				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Union[str, Any]				=	MBartTokenizer(lowercase_  ,     keep_accents=lowercase_				)

     snake_case_ :     Union[str, Any]				=	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]]  ,     )

     snake_case_ :     Tuple				=	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''',
             '''.''',
         ]  ,     )
     snake_case_ :     Tuple				=	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]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ]  ,     )

     snake_case_ :     Any				=	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>''',
             '''.''',
         ]  ,     )






 def 						_snake_case					(     self		:       int				):
     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

     snake_case_ :     str				=	(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
             snake_case_ :     Tuple				=	self.rust_tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)
             snake_case_ :     Optional[int]				=	self.tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)

             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     str				=	tokenizer_r.save_pretrained(lowercase_				)
             snake_case_ :     int				=	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				)				)
             snake_case_ :     List[str]				=	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
             snake_case_ :     List[Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     List[Any]				=	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
             snake_case_ :     int				=	tempfile.mkdtemp()

             snake_case_ :     Optional[int]				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     str				=	tokenizer_p.save_pretrained(lowercase_				)

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

             # Checks everything loads correctly in the same way
             snake_case_ :     Optional[Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     List[Any]				=	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
             snake_case_ :     List[Any]				=	tempfile.mkdtemp()

             snake_case_ :     Dict				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     Any				=	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
             snake_case_ :     Optional[int]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     Tuple				=	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):
 _lowerCAmelCase   :			int							=		"""facebook/mbart-large-en-ro"""
 _lowerCAmelCase   :			str							=		[
     """ 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.""",
 ]
 _lowerCAmelCase   :			Tuple							=		[
     """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.""",
 ]
 _lowerCAmelCase   :			List[str]							=		[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]

 @classmethod
 def 						_snake_case					(     cls		:       List[str]				):
     snake_case_ :     MBartTokenizer				=	MBartTokenizer.from_pretrained(
         cls.checkpoint_name  ,     src_lang='''en_XX'''  ,     tgt_lang='''ro_RO'''				)
     snake_case_ :     Dict				=	1
     return cls

 def 						_snake_case					(     self		:       int				):
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']  ,     250001				)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']  ,     250004				)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']  ,     250020				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     List[str]				=	self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
     self.assertListEqual(self.expected_src_tokens  ,     lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     self.assertIn(lowercase_  ,     self.tokenizer.all_special_ids				)
     snake_case_ :     str				=	[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
     snake_case_ :     Dict				=	self.tokenizer.decode(lowercase_  ,     skip_special_tokens=lowercase_				)
     snake_case_ :     List[str]				=	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		:       Tuple				):
     snake_case_ :     List[Any]				=	['''this is gunna be a long sentence ''' * 20]
     assert isinstance(src_text[0]  ,     lowercase_				)
     snake_case_ :     Tuple				=	10
     snake_case_ :     int				=	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		:       str				):
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']				)  ,     [250026, 250001]				)

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     Any				=	tempfile.mkdtemp()
     snake_case_ :     Union[str, Any]				=	self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(lowercase_				)
     snake_case_ :     Tuple				=	MBartTokenizer.from_pretrained(lowercase_				)
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,     lowercase_				)

 @require_torch
 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     Any				=	self.tokenizer(self.src_text  ,     text_target=self.tgt_text  ,     padding=lowercase_  ,     return_tensors='''pt'''				)
     snake_case_ :     str				=	shift_tokens_right(batch['''labels''']  ,     self.tokenizer.pad_token_id				)

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

 @require_torch
 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Tuple				=	self.tokenizer(
         self.src_text  ,     text_target=self.tgt_text  ,     padding=lowercase_  ,     truncation=lowercase_  ,     max_length=len(self.expected_src_tokens				)  ,     return_tensors='''pt'''  ,     )

     snake_case_ :     Any				=	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				)
     snake_case_ :     List[Any]				=	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, EN_CODE]				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Union[str, Any]				=	self.tokenizer(self.src_text  ,     padding=lowercase_  ,     truncation=lowercase_  ,     max_length=3  ,     return_tensors='''pt'''				)
     snake_case_ :     Optional[Any]				=	self.tokenizer(
         text_target=self.tgt_text  ,     padding=lowercase_  ,     truncation=lowercase_  ,     max_length=10  ,     return_tensors='''pt'''				)
     snake_case_ :     Any				=	targets['''input_ids''']
     snake_case_ :     List[str]				=	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		:       Union[str, Any]				):
     snake_case_ :     Tuple				=	self.tokenizer._build_translation_inputs(
         '''A test'''  ,     return_tensors='''pt'''  ,     src_lang='''en_XX'''  ,     tgt_lang='''ar_AR'''				)

     self.assertEqual(
         nested_simplify(lowercase_				)  ,     {
             # A, test, EOS, en_XX
             '''input_ids''': [[62, 3034, 2, 250004]],
             '''attention_mask''': [[1, 1, 1, 1]],
             # ar_AR
             '''forced_bos_token_id''': 250001,
         }  ,     )










"""simple docstring"""

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





