



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


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


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

lowerCamelCase		       =      25_00_04
lowerCamelCase		       =      25_00_20


@require_sentencepiece
@require_tokenizers
class 				_a						(       SCREAMING_SNAKE_CASE_       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Optional[int] =       MBartTokenizer
							A				:Union[str, Any] =       MBartTokenizerFast
							A				:Dict =       True
							A				:Optional[Any] =       True



							def 		_A	(							self				):







											"""simple docstring"""



											super().setUp()

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



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= MBartTokenizer(__UpperCAmelCase ,					keep_accents=__UpperCAmelCase				)

											a__	:							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__	:							Tuple      						= 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__	:							List[Any]      						= 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]
											        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
											    ] ,					)

											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 		_A	(							self				):







											"""simple docstring"""



											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__	:							List[Any]      						= (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})'				):
																			a__	:							Tuple      						= self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase				)
																			a__	:							Any      						= self.tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase				)

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

																			a__	:							Dict      						= tokenizer_r.save_pretrained(__UpperCAmelCase				)
																			a__	:							Dict      						= 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__	:							Optional[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__	:							Any      						= tokenizer_r.from_pretrained(__UpperCAmelCase				)
																			a__	:							List[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				)				)
																							# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																							# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																			shutil.rmtree(__UpperCAmelCase				)

																			# Save tokenizer rust, legacy_format=True
																			a__	:							Dict      						= tempfile.mkdtemp()

																			a__	:							Union[str, Any]      						= tokenizer_r.save_pretrained(__UpperCAmelCase ,					legacy_format=__UpperCAmelCase				)
																			a__	:							str      						= 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__	:							Any      						= tokenizer_r.from_pretrained(__UpperCAmelCase				)
																			a__	:							List[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__	:							Any      						= tempfile.mkdtemp()

																			a__	:							Optional[int]      						= tokenizer_r.save_pretrained(__UpperCAmelCase ,					legacy_format=__UpperCAmelCase				)
																			a__	:							Tuple      						= 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__	:							Dict      						= 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				)





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





							'''simple docstring'''

							A				:Optional[Any] =       'facebook/mbart-large-en-ro'
							A				:Tuple =       [
							    ' 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				:Union[str, 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.',
							]
							A				:Optional[Any] =       [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]



							@classmethod
							def 		_A	(							cls				):







											"""simple docstring"""



											a__	:							MBartTokenizer      						= MBartTokenizer.from_pretrained(
											    cls.checkpoint_name ,					src_lang="en_XX" ,					tgt_lang="ro_RO"				)
											a__	:							Optional[int]      						= 1
											return cls



							def 		_A	(							self				):







											"""simple docstring"""



											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] ,					25_0001				)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] ,					25_0004				)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] ,					25_0020				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
											self.assertListEqual(self.expected_src_tokens ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.assertIn(__UpperCAmelCase ,					self.tokenizer.all_special_ids				)
											a__	:							Dict      						= [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
											a__	:							str      						= self.tokenizer.decode(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase				)
											a__	:							List[Any]      						= self.tokenizer.decode(generated_ids[1:] ,					skip_special_tokens=__UpperCAmelCase				)
											self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase				)
											self.assertNotIn(self.tokenizer.eos_token ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= ['this is gunna be a long sentence ' * 20]
											assert isinstance(src_text[0] ,					__UpperCAmelCase				)
											a__	:							Tuple      						= 10
											a__	:							Any      						= self.tokenizer(__UpperCAmelCase ,					max_length=__UpperCAmelCase ,					truncation=__UpperCAmelCase				).input_ids[0]
											self.assertEqual(ids[-2] ,					2				)
											self.assertEqual(ids[-1] ,					__UpperCAmelCase				)
											self.assertEqual(len(__UpperCAmelCase				) ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]				) ,					[25_0026, 25_0001]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= tempfile.mkdtemp()
											a__	:							Dict      						= self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(__UpperCAmelCase				)
											a__	:							Dict      						= MBartTokenizer.from_pretrained(__UpperCAmelCase				)
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,					__UpperCAmelCase				)



							@require_torch
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.tokenizer(self.src_text ,					text_target=self.tgt_text ,					padding=__UpperCAmelCase ,					return_tensors="pt"				)
											a__	:							Any      						= 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 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= 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__	:							Optional[Any]      						= shift_tokens_right(batch["labels"] ,					self.tokenizer.pad_token_id				)

											self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase				)

											self.assertEqual((2, 14) ,					batch.input_ids.shape				)
											self.assertEqual((2, 14) ,					batch.attention_mask.shape				)
											a__	:							Dict      						= batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens ,					__UpperCAmelCase				)
											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 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= self.tokenizer(self.src_text ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					max_length=3 ,					return_tensors="pt"				)
											a__	:							Tuple      						= self.tokenizer(
											    text_target=self.tgt_text ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					max_length=10 ,					return_tensors="pt"				)
											a__	:							Optional[int]      						= targets['input_ids']
											a__	:							Optional[int]      						= shift_tokens_right(__UpperCAmelCase ,					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 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.tokenizer._build_translation_inputs(
											    "A test" ,					return_tensors="pt" ,					src_lang="en_XX" ,					tgt_lang="ar_AR"				)

											self.assertEqual(
											    nested_simplify(__UpperCAmelCase				) ,					{
											        # A, test, EOS, en_XX
											        "input_ids": [[62, 3034, 2, 25_0004]],
											        "attention_mask": [[1, 1, 1, 1]],
											        # ar_AR
											        "forced_bos_token_id": 25_0001,
											    } ,					)







from __future__ import annotations

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = None   )     ->     list[list[str]]:
					SCREAMING_SNAKE_CASE							:     Dict						=				word_bank or []
					# create a table
					SCREAMING_SNAKE_CASE							:     int						=				len(__lowerCAmelCase   ) + 1

					SCREAMING_SNAKE_CASE							:     list[list[list[str]]]						=				[]
					for _ in range(__lowerCAmelCase   ):
										table.append([]   )
					# seed value
					SCREAMING_SNAKE_CASE							:     Tuple						=				[[]]  # because empty string has empty combination

					# iterate through the indices
					for i in range(__lowerCAmelCase   ):
										# condition
										if table[i] != []:
															for word in word_bank:
																				# slice condition
																				if target[i : i + len(__lowerCAmelCase   )] == word:
																									SCREAMING_SNAKE_CASE							:     list[list[str]]						=				[
																									    [word, *way] for way in table[i]
																									]
																									# adds the word to every combination the current position holds
																									# now,push that combination to the table[i+len(word)]
																									table[i + len(__lowerCAmelCase   )] += new_combinations

    # combinations are in reverse order so reverse for better output
					for combination in table[len(__lowerCAmelCase   )]:
										combination.reverse()

					return table[len(__lowerCAmelCase   )]


if __name__ == "__main__":
			print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
			print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
			print(
			    all_construct(
			        """hexagonosaurus""",
			        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
			    )
			)