



'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


__lowerCAmelCase									=   get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowerCAmelCase									=   256_047
__lowerCAmelCase									=   256_145

@require_sentencepiece
@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Tuple   				=					NllbTokenizer
							lowerCAmelCase       :    Tuple   				=					NllbTokenizerFast
							lowerCAmelCase       :    List[str]   				=					True
							lowerCAmelCase       :    Union[str, Any]   				=					True
							lowerCAmelCase       :    List[str]   				=					{}







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

									# We have a SentencePiece fixture for testing
									_a     :      int        =							NllbTokenizer(_UpperCAmelCase	,keep_accents=_UpperCAmelCase		)
									tokenizer.save_pretrained(self.tmpdirname		)







							def 							__lowercase			(							self					:       Any		):
									_a     :      Optional[Any]        =							NllbTokenizer(_UpperCAmelCase	,keep_accents=_UpperCAmelCase		)

									_a     :      List[str]        =							tokenizer.tokenize('This is a test'		)
									self.assertListEqual(_UpperCAmelCase	,['▁This', '▁is', '▁a', '▁t', 'est']		)

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

									_a     :      Optional[int]        =							tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
									self.assertListEqual(
									    _UpperCAmelCase	,[
									        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     :      Tuple        =							tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)
									self.assertListEqual(
									    _UpperCAmelCase	,[
									        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     :      Dict        =							tokenizer.convert_ids_to_tokens(_UpperCAmelCase		)
									self.assertListEqual(
									    _UpperCAmelCase	,[
									        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 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Tuple        =							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      List[str]        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      Tuple        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)

													_a     :      Tuple        =							tempfile.mkdtemp()

													_a     :      str        =							tokenizer_r.save_pretrained(_UpperCAmelCase		)
													_a     :      str        =							tokenizer_p.save_pretrained(_UpperCAmelCase		)

													# 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     :      int        =							tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f		)
													self.assertSequenceEqual(_UpperCAmelCase	,_UpperCAmelCase		)

													# Checks everything loads correctly in the same way
													_a     :      Union[str, Any]        =							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a     :      Optional[Any]        =							tokenizer_p.from_pretrained(_UpperCAmelCase		)

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

													shutil.rmtree(_UpperCAmelCase		)

													# Save tokenizer rust, legacy_format=True
													_a     :      Any        =							tempfile.mkdtemp()

													_a     :      Tuple        =							tokenizer_r.save_pretrained(_UpperCAmelCase	,legacy_format=_UpperCAmelCase		)
													_a     :      Optional[int]        =							tokenizer_p.save_pretrained(_UpperCAmelCase		)

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

													# Checks everything loads correctly in the same way
													_a     :      Optional[Any]        =							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a     :      Optional[Any]        =							tokenizer_p.from_pretrained(_UpperCAmelCase		)

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

													shutil.rmtree(_UpperCAmelCase		)

													# Save tokenizer rust, legacy_format=False
													_a     :      Dict        =							tempfile.mkdtemp()

													_a     :      Any        =							tokenizer_r.save_pretrained(_UpperCAmelCase	,legacy_format=_UpperCAmelCase		)
													_a     :      Union[str, Any]        =							tokenizer_p.save_pretrained(_UpperCAmelCase		)

													# 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     :      int        =							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a     :      List[str]        =							tokenizer_p.from_pretrained(_UpperCAmelCase		)

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

													shutil.rmtree(_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									if not self.test_seqaseq:
											return

									_a     :      List[Any]        =							self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""		):
													# Longer text that will definitely require truncation.
													_a     :      Any        =							[
													    ' 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.',
													]
													_a     :      Optional[Any]        =							[
													    '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.',
													]
													try:
															_a     :      Optional[int]        =							tokenizer.prepare_seqaseq_batch(
															    src_texts=_UpperCAmelCase	,tgt_texts=_UpperCAmelCase	,max_length=3	,max_target_length=10	,return_tensors='pt'	,src_lang='eng_Latn'	,tgt_lang='ron_Latn'	,)
													except NotImplementedError:
															return
													self.assertEqual(batch.input_ids.shape[1]	,3		)
													self.assertEqual(batch.labels.shape[1]	,10		)
													# max_target_length will default to max_length if not specified
													_a     :      List[str]        =							tokenizer.prepare_seqaseq_batch(
													    _UpperCAmelCase	,tgt_texts=_UpperCAmelCase	,max_length=3	,return_tensors='pt'		)
													self.assertEqual(batch.input_ids.shape[1]	,3		)
													self.assertEqual(batch.labels.shape[1]	,3		)

													_a     :      Dict        =							tokenizer.prepare_seqaseq_batch(
													    src_texts=_UpperCAmelCase	,max_length=3	,max_target_length=10	,return_tensors='pt'		)
													self.assertEqual(batch_encoder_only.input_ids.shape[1]	,3		)
													self.assertEqual(batch_encoder_only.attention_mask.shape[1]	,3		)
													self.assertNotIn('decoder_input_ids'	,_UpperCAmelCase		)







							@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'		)
							def 							__lowercase			(							self					:       List[str]		):
									pass







							def 							__lowercase			(							self					:       Dict		):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      Dict        =							[AddedToken('<special>'	,lstrip=_UpperCAmelCase		)]

													_a     :      Dict        =							self.rust_tokenizer_class.from_pretrained(
													    _UpperCAmelCase	,additional_special_tokens=_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      Tuple        =							tokenizer_r.encode('Hey this is a <special> token'		)

													_a     :      Dict        =							tokenizer_r.encode('<special>'	,add_special_tokens=_UpperCAmelCase		)[0]

													self.assertTrue(special_token_id in r_output		)

													if self.test_slow_tokenizer:
															_a     :      Tuple        =							self.rust_tokenizer_class.from_pretrained(
															    _UpperCAmelCase	,additional_special_tokens=_UpperCAmelCase	,**_UpperCAmelCase	,)
															_a     :      Any        =							self.tokenizer_class.from_pretrained(
															    _UpperCAmelCase	,additional_special_tokens=_UpperCAmelCase	,**_UpperCAmelCase		)

															_a     :      Tuple        =							tokenizer_p.encode('Hey this is a <special> token'		)

															_a     :      Optional[Any]        =							tokenizer_cr.encode('Hey this is a <special> token'		)

															self.assertEqual(_UpperCAmelCase	,_UpperCAmelCase		)
															self.assertEqual(_UpperCAmelCase	,_UpperCAmelCase		)
															self.assertTrue(special_token_id in p_output		)
															self.assertTrue(special_token_id in cr_output		)

@require_torch
@require_sentencepiece
@require_tokenizers
class 	__magic_name__						(    unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					'facebook/nllb-200-distilled-600M'
							lowerCAmelCase       :    int   				=					[
							    ' 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       :    Optional[int]   				=					[
							    '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       :    int   				=					[
							    2_5_6_0_4_7,
							    1_6_2_9_7,
							    1_3_4_4_0_8,
							    8_1_6_5,
							    2_4_8_0_6_6,
							    1_4_7_3_4,
							    9_5_0,
							    1_1_3_5,
							    1_0_5_7_2_1,
							    3_5_7_3,
							    8_3,
							    2_7_3_5_2,
							    1_0_8,
							    4_9_4_8_6,
							    2,
							]







							@classmethod
							def 							__lowercase			(							cls					:       Union[str, Any]		):
									_a     :      NllbTokenizer        =							NllbTokenizer.from_pretrained(
									    cls.checkpoint_name	,src_lang='eng_Latn'	,tgt_lang='ron_Latn'		)
									_a     :      Optional[Any]        =							1
									return cls







							def 							__lowercase			(							self					:       Any		):
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']	,256001		)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']	,256002		)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']	,256057		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      int        =							self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
									self.assertListEqual(self.expected_src_tokens	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any		):
									self.assertIn(_UpperCAmelCase	,self.tokenizer.all_special_ids		)
									# fmt: off
									_a     :      Dict        =							[RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
									# fmt: on

									_a     :      Tuple        =							self.tokenizer.decode(_UpperCAmelCase	,skip_special_tokens=_UpperCAmelCase		)
									_a     :      List[str]        =							self.tokenizer.decode(generated_ids[1:]	,skip_special_tokens=_UpperCAmelCase		)
									self.assertEqual(_UpperCAmelCase	,_UpperCAmelCase		)
									self.assertNotIn(self.tokenizer.eos_token	,_UpperCAmelCase		)







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







							def 							__lowercase			(							self					:       Dict		):
									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']		)	,[256203, 3]		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Union[str, Any]        =							tempfile.mkdtemp()
									_a     :      Tuple        =							self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(_UpperCAmelCase		)
									_a     :      Tuple        =							NllbTokenizer.from_pretrained(_UpperCAmelCase		)
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer(
									    self.src_text	,text_target=self.tgt_text	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,max_length=len(self.expected_src_tokens		)	,return_tensors='pt'	,)
									_a     :      Tuple        =							shift_tokens_right(
									    batch['labels']	,self.tokenizer.pad_token_id	,self.tokenizer.lang_code_to_id['ron_Latn']		)

									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

									self.assertEqual((2, 15)	,batch.input_ids.shape		)
									self.assertEqual((2, 15)	,batch.attention_mask.shape		)
									_a     :      Optional[int]        =							batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens	,_UpperCAmelCase		)
									self.assertEqual(_UpperCAmelCase	,batch.decoder_input_ids[0, 0]		)  # EOS
									# 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 							__lowercase			(							self					:       List[str]		):
									_a     :      int        =							self.tokenizer(self.src_text	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,max_length=3	,return_tensors='pt'		)
									_a     :      str        =							self.tokenizer(
									    text_target=self.tgt_text	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,max_length=10	,return_tensors='pt'		)
									_a     :      Union[str, Any]        =							targets['input_ids']
									_a     :      Dict        =							shift_tokens_right(
									    _UpperCAmelCase	,self.tokenizer.pad_token_id	,decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]	,)

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







							@require_torch
							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      int        =							self.tokenizer._build_translation_inputs(
									    'A test'	,return_tensors='pt'	,src_lang='eng_Latn'	,tgt_lang='fra_Latn'		)

									self.assertEqual(
									    nested_simplify(_UpperCAmelCase		)	,{
									        # A, test, EOS, en_XX
									        'input_ids': [[256047, 70, 7356, 2]],
									        'attention_mask': [[1, 1, 1, 1]],
									        # ar_AR
									        'forced_bos_token_id': 256057,
									    }	,)







							@require_torch
							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      List[str]        =							True
									_a     :      str        =							self.tokenizer(
									    'UN Chief says there is no military solution in Syria'	,src_lang='eng_Latn'	,tgt_lang='fra_Latn'		)
									self.assertEqual(
									    inputs.input_ids	,[16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]		)

									_a     :      Tuple        =							False
									_a     :      str        =							self.tokenizer(
									    'UN Chief says there is no military solution in Syria'	,src_lang='eng_Latn'	,tgt_lang='fra_Latn'		)
									self.assertEqual(
									    inputs.input_ids	,[256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]		)









'''simple docstring'''

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




