
"""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__      :    List[Any]    							= get_tests_dir('fixtures/test_sentencepiece.model')

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

UpperCAmelCase__      :    str    							= 2_5_0_0_0_4
UpperCAmelCase__      :    List[str]    							= 2_5_0_0_2_0



@require_sentencepiece
@require_tokenizers
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Tuple					       =   MBartaaTokenizer
				__UpperCamelCase      :						Any					       =   MBartaaTokenizerFast
				__UpperCamelCase      :						Union[str, Any]					       =   True
				__UpperCamelCase      :						Tuple					       =   True



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											super().setUp()

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



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   """<s>"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   0

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



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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(SCREAMING_SNAKE_CASE__    )   ,       10_54    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											self.assertEqual(self.get_tokenizer().vocab_size   ,       10_54    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   MBartaaTokenizer(SCREAMING_SNAKE_CASE__   ,       src_lang="""en_XX"""   ,       tgt_lang="""ro_RO"""   ,       keep_accents=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer.tokenize("""This is a test"""    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )   ,       [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,       )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE__   ,       [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__						:       Union[str, Any]           =   tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE__   ,       [
											        value + tokenizer.fairseq_offset
											        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
											    ]   ,       )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE__   ,       [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 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   {"""input_ids""": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=SCREAMING_SNAKE_CASE__   ,       model_name="""facebook/mbart-large-50"""   ,       revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""   ,       )



				def 	__magic_name__    (self    )					->		List[str]:



											"""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__						:       Any           =   (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__						:       List[str]           =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Any           =   self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )

																									SCREAMING_SNAKE_CASE__						:       List[str]           =   tempfile.mkdtemp()

																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__    )

																									# 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__						:       List[Any]           =   tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f    )
																									self.assertSequenceEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

																									# Checks everything loads correctly in the same way
																									SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       List[str]           =   tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__    )

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

																									shutil.rmtree(SCREAMING_SNAKE_CASE__    )

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

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

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

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

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

																									shutil.rmtree(SCREAMING_SNAKE_CASE__    )

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

																									SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__   ,       legacy_format=SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__    )

																									# 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__						:       str           =   tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__    )

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

																									shutil.rmtree(SCREAMING_SNAKE_CASE__    )






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


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   '''facebook/mbart-large-50-one-to-many-mmt'''
				__UpperCamelCase      :						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.''',
				]
				__UpperCamelCase      :						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.''',
				]
				__UpperCamelCase      :						Tuple					       =   [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]



				@classmethod
				def 	__magic_name__    (cls    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       MBartaaTokenizer           =   MBartaaTokenizer.from_pretrained(
											    cls.checkpoint_name   ,       src_lang="""en_XX"""   ,       tgt_lang="""ro_RO"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   1
											return cls



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]   ,       25_00_01    )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]   ,       25_00_04    )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]   ,       25_00_20    )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]   ,       25_00_38    )



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
											self.assertListEqual(self.expected_src_tokens   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											self.assertIn(SCREAMING_SNAKE_CASE__   ,       self.tokenizer.all_special_ids    )
											SCREAMING_SNAKE_CASE__						:       int           =   [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.tokenizer.decode(SCREAMING_SNAKE_CASE__   ,       skip_special_tokens=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.tokenizer.decode(generated_ids[1:]   ,       skip_special_tokens=SCREAMING_SNAKE_CASE__    )
											self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											self.assertNotIn(self.tokenizer.eos_token   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]    )   ,       [25_00_53, 25_00_01]    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




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



				@require_torch
				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.tokenizer(self.src_text   ,       text_target=self.tgt_text   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




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

											self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											self.assertEqual((2, 14)   ,       batch.input_ids.shape    )
											self.assertEqual((2, 14)   ,       batch.attention_mask.shape    )
											SCREAMING_SNAKE_CASE__						:       int           =   batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens   ,       SCREAMING_SNAKE_CASE__    )
											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 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.tokenizer(self.src_text   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncation=SCREAMING_SNAKE_CASE__   ,       max_length=3   ,       return_tensors="""pt"""    )
											SCREAMING_SNAKE_CASE__						:       int           =   self.tokenizer(
											    text_target=self.tgt_text   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncation=SCREAMING_SNAKE_CASE__   ,       max_length=10   ,       return_tensors="""pt"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   targets["""input_ids"""]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   shift_tokens_right(SCREAMING_SNAKE_CASE__   ,       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 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




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

											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__    )   ,       {
											        # en_XX, A, test, EOS
											        """input_ids""": [[25_00_04, 62, 30_34, 2]],
											        """attention_mask""": [[1, 1, 1, 1]],
											        # ar_AR
											        """forced_bos_token_id""": 25_00_01,
											    }   ,       )











import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_MAPPING,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerModel,
    )
    from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase					,  'hidden_sizes'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_attention_heads'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_encoder_blocks'))





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=64					,  lowercase=3					,  lowercase=4					,  lowercase=[2, 2, 2, 2]					,  lowercase=[8, 4, 2, 1]					,  lowercase=[16, 32, 64, 128]					,  lowercase=[1, 4, 8, 16]					,  lowercase=[1, 2, 4, 8]					,  lowercase=True					,  lowercase=True					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=0.02					,  lowercase=3					,  lowercase=None					,  )			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				parent
          a__						:	int =				batch_size
          a__						:	Tuple =				image_size
          a__						:	Union[str, Any] =				num_channels
          a__						:	str =				num_encoder_blocks
          a__						:	Dict =				sr_ratios
          a__						:	Dict =				depths
          a__						:	Union[str, Any] =				hidden_sizes
          a__						:	str =				downsampling_rates
          a__						:	Tuple =				num_attention_heads
          a__						:	Optional[Any] =				is_training
          a__						:	Union[str, Any] =				use_labels
          a__						:	Any =				hidden_act
          a__						:	Optional[int] =				hidden_dropout_prob
          a__						:	int =				attention_probs_dropout_prob
          a__						:	Optional[Any] =				initializer_range
          a__						:	Tuple =				num_labels
          a__						:	Union[str, Any] =				scope

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Optional[int] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	str =				None
          if self.use_labels:
                 a__						:	Dict =				ids_tensor([self.batch_size, self.image_size, self.image_size]					,  self.num_labels)

          a__						:	Any =				self.get_config()
          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          return SegformerConfig(
              image_size=self.image_size					,  num_channels=self.num_channels					,  num_encoder_blocks=self.num_encoder_blocks					,  depths=self.depths					,  hidden_sizes=self.hidden_sizes					,  num_attention_heads=self.num_attention_heads					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Dict =				SegformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				model(lowercase)
          a__						:	Optional[Any] =				self.image_size // (self.downsampling_rates[-1] * 2)
          self.parent.assertEqual(
              result.last_hidden_state.shape					,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.num_labels
          a__						:	List[str] =				SegformerForSemanticSegmentation(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          a__						:	int =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				1
          a__						:	Optional[int] =				SegformerForSemanticSegmentation(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Union[str, Any] =				torch.randint(0					,  1					,  (self.batch_size, self.image_size, self.image_size)).to(lowercase)
          a__						:	Optional[Any] =				model(lowercase					,  labels=lowercase)
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Any =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	str =				config_and_inputs
          a__						:	Optional[int] =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   __A   :				List[str]         =  (
       {
           '''feature-extraction''': SegformerModel,
           '''image-classification''': SegformerForImageClassification,
           '''image-segmentation''': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  True
   __A   :				Any         =  False
   __A   :				Any         =  False
   __A   :				str         =  False

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Union[str, Any] =				SegformerModelTester(self)
          a__						:	Optional[Any] =				SegformerConfigTester(self					,  config_class=lowercase)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	int =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*lowercase)

   @unittest.skip('SegFormer does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          pass

   @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				model_class(lowercase)
                 a__						:	Dict =				inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__						:	Optional[int] =				[*signature.parameters.keys()]

                 a__						:	Union[str, Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__ ,							a__						:	Dict =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 a__						:	str =				True
                 a__						:	List[str] =				False
                 a__						:	int =				True
                 a__						:	List[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[Any] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 a__						:	Dict =				sum(self.model_tester.depths)
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 a__						:	Dict =				True
                 a__						:	int =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[int] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first attentions (first block, first layer)
                 a__						:	Tuple =				(self.model_tester.image_size // 4) ** 2
                 a__						:	List[str] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

                 # verify the last attentions (last block, last layer)
                 a__						:	str =				(self.model_tester.image_size // 32) ** 2
                 a__						:	Optional[int] =				(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                 self.assertListEqual(
                     list(attentions[-1].shape[-3:])					,  [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,  )
                 a__						:	Dict =				len(lowercase)

                 # Check attention is always last and order is fine
                 a__						:	List[Any] =				True
                 a__						:	Any =				True
                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 self.assertEqual(out_len + 1					,  len(lowercase))

                 a__						:	int =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)
                 # verify the first attentions (first block, first layer)
                 a__						:	List[Any] =				(self.model_tester.image_size // 4) ** 2
                 a__						:	Union[str, Any] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          def check_hidden_states_output(lowercase					,  lowercase					,  lowercase):
                 a__						:	Optional[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 a__						:	Union[str, Any] =				outputs.hidden_states

                 a__						:	Any =				self.model_tester.num_encoder_blocks
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:])					,  [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]					,  )

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				True
                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__						:	int =				True

                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          if not self.model_tester.is_training:
                 return

          a__ ,							a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 if model_class in get_values(lowercase):
                        continue

                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.train()
                 a__						:	str =				self._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)
                 a__						:	Optional[int] =				model(**lowercase).loss
                 loss.backward()

   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          pass

   @slow
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				SegformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	str =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





@require_torch
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	int =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Optional[int] =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[int] =				model(lowercase)

          a__						:	Union[str, Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Dict =				torch.tensor(
              [
                  [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
                  [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
                  [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-4))

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained(
              'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(lowercase)

          a__						:	Dict =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[Any] =				model(lowercase)

          a__						:	List[Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Optional[Any] =				torch.tensor(
              [
                  [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
                  [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
                  [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-1))

   @slow
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Any =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	Optional[int] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Union[str, Any] =				model(lowercase)

          a__						:	int =				outputs.logits.detach().cpu()

          a__						:	List[str] =				image_processor.post_process_semantic_segmentation(outputs=lowercase					,  target_sizes=[(500, 300)])
          a__						:	Optional[Any] =				torch.Size((500, 300))
          self.assertEqual(segmentation[0].shape					,  lowercase)

          a__						:	Any =				image_processor.post_process_semantic_segmentation(outputs=lowercase)
          a__						:	Union[str, Any] =				torch.Size((128, 128))
          self.assertEqual(segmentation[0].shape					,  lowercase)


