



'''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


UpperCamelCase__ :			Optional[int]												=				get_tests_dir('''fixtures/test_sentencepiece.model''')

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

UpperCamelCase__ :			str												=				25_00_04
UpperCamelCase__ :			Dict												=				25_00_20






@require_sentencepiece
@require_tokenizers
class 		_UpperCamelCase						(							_UpperCAmelCase					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	List[Any]    					=	MBartaaTokenizer
			_A   :	Dict    					=	MBartaaTokenizerFast
			_A   :	List[str]    					=	True
			_A   :	List[str]    					=	True

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					super().setUp()

					# We have a SentencePiece fixture for testing
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	MBartaaTokenizer(lowerCamelCase_							,  src_lang="""en_XX"""							,  tgt_lang="""ro_RO"""							,  keep_accents=lowerCamelCase_		)
					tokenizer.save_pretrained(self.tmpdirname		)

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	'''<s>'''
					__SCREAMING_SNAKE_CASE :	Dict					 =	0

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

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	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(lowerCamelCase_		)							,  1_0_5_4		)

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,  1_0_5_4		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	MBartaaTokenizer(lowerCamelCase_							,  src_lang="""en_XX"""							,  tgt_lang="""ro_RO"""							,  keep_accents=lowerCamelCase_		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	tokenizer.tokenize("""This is a test"""		)
					self.assertListEqual(lowerCamelCase_							,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(lowerCamelCase_		)							,  [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]							,  )

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
					self.assertListEqual(
					    lowerCamelCase_							,  [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""", """."""]							,  )
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer.convert_tokens_to_ids(lowerCamelCase_		)
					self.assertListEqual(
					    lowerCamelCase_							,  [
					        value + tokenizer.fairseq_offset
					        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
					    ]							,  )

					__SCREAMING_SNAKE_CASE :	str					 =	tokenizer.convert_ids_to_tokens(lowerCamelCase_		)
					self.assertListEqual(
					    lowerCamelCase_							,  [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 UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=lowerCamelCase_							,  model_name="""facebook/mbart-large-50"""							,  revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""							,  )



			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""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

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(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})"		):
									__SCREAMING_SNAKE_CASE :	int					 =	self.rust_tokenizer_class.from_pretrained(lowerCamelCase_							,  **lowerCamelCase_		)
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.tokenizer_class.from_pretrained(lowerCamelCase_							,  **lowerCamelCase_		)

									__SCREAMING_SNAKE_CASE :	int					 =	tempfile.mkdtemp()

									__SCREAMING_SNAKE_CASE :	List[str]					 =	tokenizer_r.save_pretrained(lowerCamelCase_		)
									__SCREAMING_SNAKE_CASE :	int					 =	tokenizer_p.save_pretrained(lowerCamelCase_		)

									# 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		)		)
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f		)
									self.assertSequenceEqual(lowerCamelCase_							,  lowerCamelCase_		)

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

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

									shutil.rmtree(lowerCamelCase_		)

									# Save tokenizer rust, legacy_format=True
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	tempfile.mkdtemp()

									__SCREAMING_SNAKE_CASE :	str					 =	tokenizer_r.save_pretrained(lowerCamelCase_							,  legacy_format=lowerCamelCase_		)
									__SCREAMING_SNAKE_CASE :	str					 =	tokenizer_p.save_pretrained(lowerCamelCase_		)

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

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

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

									shutil.rmtree(lowerCamelCase_		)

									# Save tokenizer rust, legacy_format=False
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	tempfile.mkdtemp()

									__SCREAMING_SNAKE_CASE :	List[Any]					 =	tokenizer_r.save_pretrained(lowerCamelCase_							,  legacy_format=lowerCamelCase_		)
									__SCREAMING_SNAKE_CASE :	int					 =	tokenizer_p.save_pretrained(lowerCamelCase_		)

									# 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
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	tokenizer_r.from_pretrained(lowerCamelCase_		)
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	tokenizer_p.from_pretrained(lowerCamelCase_		)

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

									shutil.rmtree(lowerCamelCase_		)






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


			'''simple docstring'''

			_A   :	Dict    					=	'''facebook/mbart-large-50-one-to-many-mmt'''
			_A   :	List[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.''',
			]
			_A   :	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   :	int    					=	[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]

			@classmethod
			def UpperCamelCase__      (							cls  :				Optional[Any]		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]							,  2_5_0_0_0_1		)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]							,  2_5_0_0_0_4		)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]							,  2_5_0_0_2_0		)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]							,  2_5_0_0_3_8		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
					self.assertListEqual(self.expected_src_tokens							,  lowerCamelCase_		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					self.assertIn(lowerCamelCase_							,  self.tokenizer.all_special_ids		)
					__SCREAMING_SNAKE_CASE :	Any					 =	[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.tokenizer.decode(lowerCamelCase_							,  skip_special_tokens=lowerCamelCase_		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.tokenizer.decode(generated_ids[1:]							,  skip_special_tokens=lowerCamelCase_		)
					self.assertEqual(lowerCamelCase_							,  lowerCamelCase_		)
					self.assertNotIn(self.tokenizer.eos_token							,  lowerCamelCase_		)

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	['''this is gunna be a long sentence ''' * 2_0]
					assert isinstance(src_text[0]							,  lowerCamelCase_		)
					__SCREAMING_SNAKE_CASE :	int					 =	1_0
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.tokenizer(lowerCamelCase_							,  max_length=lowerCamelCase_							,  truncation=lowerCamelCase_		).input_ids[0]
					self.assertEqual(ids[0]							,  lowerCamelCase_		)
					self.assertEqual(ids[-1]							,  2		)
					self.assertEqual(len(lowerCamelCase_		)							,  lowerCamelCase_		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]		)							,  [2_5_0_0_5_3, 2_5_0_0_0_1]		)

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	tempfile.mkdtemp()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(lowerCamelCase_		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	MBartaaTokenizer.from_pretrained(lowerCamelCase_		)
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,  lowerCamelCase_		)

			@require_torch
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.tokenizer(self.src_text							,  text_target=self.tgt_text							,  padding=lowerCamelCase_							,  return_tensors="""pt"""		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	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 UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.tokenizer(
					    self.src_text							,  text_target=self.tgt_text							,  padding=lowerCamelCase_							,  truncation=lowerCamelCase_							,  max_length=len(self.expected_src_tokens		)							,  return_tensors="""pt"""							,  )
					__SCREAMING_SNAKE_CASE :	List[str]					 =	shift_tokens_right(batch["""labels"""]							,  self.tokenizer.pad_token_id		)

					self.assertIsInstance(lowerCamelCase_							,  lowerCamelCase_		)

					self.assertEqual((2, 1_4)							,  batch.input_ids.shape		)
					self.assertEqual((2, 1_4)							,  batch.attention_mask.shape		)
					__SCREAMING_SNAKE_CASE :	int					 =	batch.input_ids.tolist()[0]
					self.assertListEqual(self.expected_src_tokens							,  lowerCamelCase_		)
					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 UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.tokenizer(self.src_text							,  padding=lowerCamelCase_							,  truncation=lowerCamelCase_							,  max_length=3							,  return_tensors="""pt"""		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.tokenizer(
					    text_target=self.tgt_text							,  padding=lowerCamelCase_							,  truncation=lowerCamelCase_							,  max_length=1_0							,  return_tensors="""pt"""		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	targets['''input_ids''']
					__SCREAMING_SNAKE_CASE :	Any					 =	shift_tokens_right(lowerCamelCase_							,  self.tokenizer.pad_token_id		)

					self.assertEqual(batch.input_ids.shape[1]							,  3		)
					self.assertEqual(batch.decoder_input_ids.shape[1]							,  1_0		)



			@require_torch
			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_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(lowerCamelCase_		)							,  {
					        # en_XX, A, test, EOS
					        """input_ids""": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
					        """attention_mask""": [[1, 1, 1, 1]],
					        # ar_AR
					        """forced_bos_token_id""": 2_5_0_0_0_1,
					    }							,  )




'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCamelCase__ :			Optional[int]												=				{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCamelCase__ :			Union[str, Any]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	'''maskformer'''
			_A   :	Optional[int]    					=	{'''hidden_size''': '''mask_feature_size'''}
			_A   :	int    					=	['''resnet''', '''swin''']
			_A   :	Any    					=	['''detr''']

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 0.1							,  lowerCAmelCase__  :				bool = False							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				float = 0.02							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 20.0							,  lowerCAmelCase__  :				Optional[bool] = None							,  **lowerCAmelCase__  :				Any							,  ):





					"""simple docstring"""






					if backbone_config is None:
							# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
							__SCREAMING_SNAKE_CASE :	Any					 =	SwinConfig(
							    image_size=3_8_4							,  in_channels=3							,  patch_size=4							,  embed_dim=1_2_8							,  depths=[2, 2, 1_8, 2]							,  num_heads=[4, 8, 1_6, 3_2]							,  window_size=1_2							,  drop_path_rate=0.3							,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							,  )

					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	backbone_config.pop("""model_type"""		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	CONFIG_MAPPING[backbone_model_type]
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	config_class.from_dict(lowerCAmelCase__		)

					# verify that the backbone is supported
					if backbone_config.model_type not in self.backbones_supported:
							logger.warning_once(
							    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
							    F"Supported model types: {','.join(self.backbones_supported		)}"		)

					if decoder_config is None:
							# fall back to https://huggingface.co/facebook/detr-resnet-50
							__SCREAMING_SNAKE_CASE :	List[str]					 =	DetrConfig()
					else:
							# verify that the decoder is supported
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
							    decoder_config.pop("""model_type"""		) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		) else decoder_config.model_type
							)
							if decoder_type not in self.decoders_supported:
									raise ValueError(
									    F"Transformer Decoder {decoder_type} not supported, please use one of"
									    F" {','.join(self.decoders_supported		)}"		)
							if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
									__SCREAMING_SNAKE_CASE :	int					 =	CONFIG_MAPPING[decoder_type]
									__SCREAMING_SNAKE_CASE :	Dict					 =	config_class.from_dict(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	backbone_config
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	decoder_config
					# main feature dimension for the model
					__SCREAMING_SNAKE_CASE :	List[str]					 =	fpn_feature_size
					__SCREAMING_SNAKE_CASE :	int					 =	mask_feature_size
					# initializer
					__SCREAMING_SNAKE_CASE :	str					 =	init_std
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	init_xavier_std
					# Hungarian matcher && loss
					__SCREAMING_SNAKE_CASE :	Tuple					 =	cross_entropy_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	dice_weight
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	mask_weight
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	use_auxiliary_loss
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	no_object_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	output_auxiliary_logits

					__SCREAMING_SNAKE_CASE :	Any					 =	self.decoder_config.encoder_attention_heads
					__SCREAMING_SNAKE_CASE :	int					 =	self.decoder_config.num_hidden_layers
					super().__init__(**lowerCAmelCase__		)

			@classmethod
			def UpperCamelCase__      (							cls  :				List[Any]							,  lowerCAmelCase__  :				PretrainedConfig							,  lowerCAmelCase__  :				PretrainedConfig							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					return cls(
					    backbone_config=lowerCAmelCase__							,  decoder_config=lowerCAmelCase__							,  **lowerCAmelCase__							,  )



			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	copy.deepcopy(self.__dict__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.backbone_config.to_dict()
					__SCREAMING_SNAKE_CASE :	str					 =	self.decoder_config.to_dict()
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.__class__.model_type
					return output