
"""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,
											    }   ,       )






"""simple docstring"""




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




