






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


SCREAMING_SNAKE_CASE__      :			str							=		get_tests_dir("""fixtures/test_sentencepiece.model""")

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

SCREAMING_SNAKE_CASE__      :			Any							=		25_00_04
SCREAMING_SNAKE_CASE__      :			int							=		25_00_20
@require_sentencepiece
@require_tokenizers
class 						lowerCamelCase_				(      lowerCamelCase			,				unittest.TestCase ):
				a__					       =  MBartaaTokenizer
				a__					       =  MBartaaTokenizerFast
				a__					       =  True
				a__					       =  True







				def 			A	(						self     ):
								"""simple docstring"""
								super().setUp()

								# We have a SentencePiece fixture for testing
								__magic_name__		:Any        =      MBartaaTokenizer(__lowerCAmelCase       ,					src_lang='''en_XX'''       ,					tgt_lang='''ro_RO'''       ,					keep_accents=__lowerCAmelCase     )
								tokenizer.save_pretrained(self.tmpdirname     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      '''<s>'''
								__magic_name__		:List[str]        =      0

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		: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 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(self.get_tokenizer().vocab_size       ,					1_0_5_4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      MBartaaTokenizer(__lowerCAmelCase       ,					src_lang='''en_XX'''       ,					tgt_lang='''ro_RO'''       ,					keep_accents=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      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]]       ,					)

								__magic_name__		:Union[str, 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''', '''.''']       ,					)
								__magic_name__		:int        =      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]
								    ]       ,					)

								__magic_name__		:Dict        =      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 			A	(						self     ):
								"""simple docstring"""
								# fmt: off
								__magic_name__		:Union[str, 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 			A	(						self     ):
								"""simple docstring"""
								if not self.test_slow_tokenizer:
												# as we don't have a slow version, we can't compare the outputs between slow and fast versions
												return

								__magic_name__		: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})'''     ):
																__magic_name__		:Dict        =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,					**__lowerCAmelCase     )
																__magic_name__		:Optional[int]        =      self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,					**__lowerCAmelCase     )

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

																__magic_name__		:Optional[int]        =      tokenizer_r.save_pretrained(__lowerCAmelCase     )
																__magic_name__		: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     )     )
																__magic_name__		:Union[str, Any]        =      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
																__magic_name__		:str        =      tokenizer_r.from_pretrained(__lowerCAmelCase     )
																__magic_name__		: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
																__magic_name__		:List[Any]        =      tempfile.mkdtemp()

																__magic_name__		:Optional[Any]        =      tokenizer_r.save_pretrained(__lowerCAmelCase       ,					legacy_format=__lowerCAmelCase     )
																__magic_name__		:Optional[int]        =      tokenizer_p.save_pretrained(__lowerCAmelCase     )

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

																# Checks everything loads correctly in the same way
																__magic_name__		:List[str]        =      tokenizer_r.from_pretrained(__lowerCAmelCase     )
																__magic_name__		:Optional[int]        =      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
																__magic_name__		:Union[str, Any]        =      tempfile.mkdtemp()

																__magic_name__		:Union[str, Any]        =      tokenizer_r.save_pretrained(__lowerCAmelCase       ,					legacy_format=__lowerCAmelCase     )
																__magic_name__		:Optional[Any]        =      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
																__magic_name__		:Tuple        =      tokenizer_r.from_pretrained(__lowerCAmelCase     )
																__magic_name__		:List[str]        =      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 						lowerCamelCase_				(      unittest.TestCase ):
				a__					       =  '''facebook/mbart-large-50-one-to-many-mmt'''
				a__					       =  [
				    ''' 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__					       =  [
				    '''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__					       =  [EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]







				@classmethod
				def 			A	(						cls     ):
								"""simple docstring"""
								__magic_name__		:MBartaaTokenizer        =      MBartaaTokenizer.from_pretrained(
								    cls.checkpoint_name       ,					src_lang='''en_XX'''       ,					tgt_lang='''ro_RO'''     )
								__magic_name__		:List[Any]        =      1
								return cls







				def 			A	(						self     ):
								"""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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertIn(__lowerCAmelCase       ,					self.tokenizer.all_special_ids     )
								__magic_name__		:str        =      [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]
								__magic_name__		:List[Any]        =      self.tokenizer.decode(__lowerCAmelCase       ,					skip_special_tokens=__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      self.tokenizer.decode(generated_ids[1:]       ,					skip_special_tokens=__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )
								self.assertNotIn(self.tokenizer.eos_token       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      ['''this is gunna be a long sentence ''' * 2_0]
								assert isinstance(src_text[0]       ,					__lowerCAmelCase     )
								__magic_name__		:int        =      1_0
								__magic_name__		:Tuple        =      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 			A	(						self     ):
								"""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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      tempfile.mkdtemp()
								__magic_name__		:List[str]        =      self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(__lowerCAmelCase     )
								__magic_name__		:Tuple        =      MBartaaTokenizer.from_pretrained(__lowerCAmelCase     )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,					__lowerCAmelCase     )







				@require_torch
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      self.tokenizer(self.src_text       ,					text_target=self.tgt_text       ,					padding=__lowerCAmelCase       ,					return_tensors='''pt'''     )
								__magic_name__		:List[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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      self.tokenizer(
								    self.src_text       ,					text_target=self.tgt_text       ,					padding=__lowerCAmelCase       ,					truncation=__lowerCAmelCase       ,					max_length=len(self.expected_src_tokens     )       ,					return_tensors='''pt'''       ,					)
								__magic_name__		: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     )
								__magic_name__		:Optional[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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.tokenizer(self.src_text       ,					padding=__lowerCAmelCase       ,					truncation=__lowerCAmelCase       ,					max_length=3       ,					return_tensors='''pt'''     )
								__magic_name__		:Dict        =      self.tokenizer(
								    text_target=self.tgt_text       ,					padding=__lowerCAmelCase       ,					truncation=__lowerCAmelCase       ,					max_length=1_0       ,					return_tensors='''pt'''     )
								__magic_name__		:Union[str, Any]        =      targets['''input_ids''']
								__magic_name__		:Tuple        =      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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      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,
								    }       ,					)














import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      DPTConfig()

				if "large" in checkpoint_url:
								__magic_name__		:str        =      1_0_2_4
								__magic_name__		:Tuple        =      4_0_9_6
								__magic_name__		:Union[str, Any]        =      2_4
								__magic_name__		:int        =      1_6
								__magic_name__		:Dict        =      [5, 1_1, 1_7, 2_3]
								__magic_name__		:Optional[int]        =      [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
								__magic_name__		:Union[str, Any]        =      (1, 3_8_4, 3_8_4)

				if "ade" in checkpoint_url:
								__magic_name__		:Tuple        =      True

								__magic_name__		:int        =      1_5_0
								__magic_name__		:List[Any]        =      '''huggingface/label-files'''
								__magic_name__		:Dict        =      '''ade20k-id2label.json'''
								__magic_name__		:str        =      json.load(open(cached_download(hf_hub_url(snake_case,  snake_case,  repo_type='''dataset''' ) ),  '''r''' ) )
								__magic_name__		:Optional[int]        =      {int(snake_case ): v for k, v in idalabel.items()}
								__magic_name__		:Any        =      idalabel
								__magic_name__		:List[str]        =      {v: k for k, v in idalabel.items()}
								__magic_name__		:Optional[Any]        =      [1, 1_5_0, 4_8_0, 4_8_0]

				return config, expected_shape

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(snake_case,  snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								__magic_name__		:int        =      name.replace('''pretrained.model''',  '''dpt.encoder''' )
				if "pretrained.model" in name:
								__magic_name__		:str        =      name.replace('''pretrained.model''',  '''dpt.embeddings''' )
				if "patch_embed" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''patch_embed''',  '''patch_embeddings''' )
				if "pos_embed" in name:
								__magic_name__		:List[Any]        =      name.replace('''pos_embed''',  '''position_embeddings''' )
				if "attn.proj" in name:
								__magic_name__		:List[Any]        =      name.replace('''attn.proj''',  '''attention.output.dense''' )
				if "proj" in name and "project" not in name:
								__magic_name__		:Dict        =      name.replace('''proj''',  '''projection''' )
				if "blocks" in name:
								__magic_name__		:str        =      name.replace('''blocks''',  '''layer''' )
				if "mlp.fc1" in name:
								__magic_name__		:Dict        =      name.replace('''mlp.fc1''',  '''intermediate.dense''' )
				if "mlp.fc2" in name:
								__magic_name__		:Tuple        =      name.replace('''mlp.fc2''',  '''output.dense''' )
				if "norm1" in name:
								__magic_name__		:List[Any]        =      name.replace('''norm1''',  '''layernorm_before''' )
				if "norm2" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''norm2''',  '''layernorm_after''' )
				if "scratch.output_conv" in name:
								__magic_name__		:Any        =      name.replace('''scratch.output_conv''',  '''head''' )
				if "scratch" in name:
								__magic_name__		:str        =      name.replace('''scratch''',  '''neck''' )
				if "layer1_rn" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''layer1_rn''',  '''convs.0''' )
				if "layer2_rn" in name:
								__magic_name__		:int        =      name.replace('''layer2_rn''',  '''convs.1''' )
				if "layer3_rn" in name:
								__magic_name__		:str        =      name.replace('''layer3_rn''',  '''convs.2''' )
				if "layer4_rn" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''layer4_rn''',  '''convs.3''' )
				if "refinenet" in name:
								__magic_name__		:int        =      int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								__magic_name__		:List[Any]        =      name.replace(f'''refinenet{layer_idx}''',  f'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
				if "out_conv" in name:
								__magic_name__		:str        =      name.replace('''out_conv''',  '''projection''' )
				if "resConfUnit1" in name:
								__magic_name__		:Dict        =      name.replace('''resConfUnit1''',  '''residual_layer1''' )
				if "resConfUnit2" in name:
								__magic_name__		:int        =      name.replace('''resConfUnit2''',  '''residual_layer2''' )
				if "conv1" in name:
								__magic_name__		:str        =      name.replace('''conv1''',  '''convolution1''' )
				if "conv2" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''conv2''',  '''convolution2''' )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								__magic_name__		:Optional[int]        =      name.replace('''pretrained.act_postprocess1.0.project.0''',  '''neck.reassemble_stage.readout_projects.0.0''' )
				if "pretrained.act_postprocess2.0.project.0" in name:
								__magic_name__		:int        =      name.replace('''pretrained.act_postprocess2.0.project.0''',  '''neck.reassemble_stage.readout_projects.1.0''' )
				if "pretrained.act_postprocess3.0.project.0" in name:
								__magic_name__		:Optional[int]        =      name.replace('''pretrained.act_postprocess3.0.project.0''',  '''neck.reassemble_stage.readout_projects.2.0''' )
				if "pretrained.act_postprocess4.0.project.0" in name:
								__magic_name__		:str        =      name.replace('''pretrained.act_postprocess4.0.project.0''',  '''neck.reassemble_stage.readout_projects.3.0''' )
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								__magic_name__		:Any        =      name.replace('''pretrained.act_postprocess1.3''',  '''neck.reassemble_stage.layers.0.projection''' )
				if "pretrained.act_postprocess1.4" in name:
								__magic_name__		:Any        =      name.replace('''pretrained.act_postprocess1.4''',  '''neck.reassemble_stage.layers.0.resize''' )
				if "pretrained.act_postprocess2.3" in name:
								__magic_name__		:Dict        =      name.replace('''pretrained.act_postprocess2.3''',  '''neck.reassemble_stage.layers.1.projection''' )
				if "pretrained.act_postprocess2.4" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess2.4''',  '''neck.reassemble_stage.layers.1.resize''' )
				if "pretrained.act_postprocess3.3" in name:
								__magic_name__		:List[str]        =      name.replace('''pretrained.act_postprocess3.3''',  '''neck.reassemble_stage.layers.2.projection''' )
				if "pretrained.act_postprocess4.3" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess4.3''',  '''neck.reassemble_stage.layers.3.projection''' )
				if "pretrained.act_postprocess4.4" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess4.4''',  '''neck.reassemble_stage.layers.3.resize''' )
				if "pretrained" in name:
								__magic_name__		:Any        =      name.replace('''pretrained''',  '''dpt''' )
				if "bn" in name:
								__magic_name__		:int        =      name.replace('''bn''',  '''batch_norm''' )
				if "head" in name:
								__magic_name__		:str        =      name.replace('''head''',  '''head.head''' )
				if "encoder.norm" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''encoder.norm''',  '''layernorm''' )
				if "auxlayer" in name:
								__magic_name__		:Dict        =      name.replace('''auxlayer''',  '''auxiliary_head.head''' )

				return name

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				for i in range(config.num_hidden_layers ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								__magic_name__		:int        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
								__magic_name__		:Union[str, Any]        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
								# next, add query, keys and values (in that order) to the state dict
								__magic_name__		:Optional[Any]        =      in_proj_weight[: config.hidden_size, :]
								__magic_name__		:str        =      in_proj_bias[: config.hidden_size]
								__magic_name__		:List[str]        =      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__magic_name__		:Optional[Any]        =      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__magic_name__		:int        =      in_proj_weight[
								    -config.hidden_size :, :
								]
								__magic_name__		:Union[str, Any]        =      in_proj_bias[-config.hidden_size :]

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:Union[str, Any]        =      Image.open(requests.get(snake_case,  stream=snake_case ).raw )
				return im

@torch.no_grad()
def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:Any        =      get_dpt_config(snake_case )
				# load original state_dict from URL
				__magic_name__		:List[str]        =      torch.hub.load_state_dict_from_url(snake_case,  map_location='''cpu''' )
				# remove certain keys
				remove_ignore_keys_(snake_case )
				# rename keys
				for key in state_dict.copy().keys():
								__magic_name__		:int        =      state_dict.pop(snake_case )
								__magic_name__		:Tuple        =      val
				# read in qkv matrices
				read_in_q_k_v(snake_case,  snake_case )

				# load HuggingFace model
				__magic_name__		:int        =      DPTForSemanticSegmentation(snake_case ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(snake_case )
				model.load_state_dict(snake_case )
				model.eval()

				# Check outputs on an image
				__magic_name__		:Union[str, Any]        =      4_8_0 if '''ade''' in checkpoint_url else 3_8_4
				__magic_name__		:int        =      DPTImageProcessor(size=snake_case )

				__magic_name__		:Dict        =      prepare_img()
				__magic_name__		:List[str]        =      image_processor(snake_case,  return_tensors='''pt''' )

				# forward pass
				__magic_name__		:Dict        =      model(**snake_case ).logits if '''ade''' in checkpoint_url else model(**snake_case ).predicted_depth

				# Assert logits
				__magic_name__		:int        =      torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
				if "ade" in checkpoint_url:
								__magic_name__		:Any        =      torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
				assert outputs.shape == torch.Size(snake_case )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3],  snake_case,  atol=1E-4 )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3],  snake_case )
				)

				Path(snake_case ).mkdir(exist_ok=snake_case )
				print(f'''Saving model to {pytorch_dump_folder_path}''' )
				model.save_pretrained(snake_case )
				print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(snake_case )

				if push_to_hub:
								print('''Pushing model to hub...''' )
								model.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add model''',  use_temp_dir=snake_case,  )
								image_processor.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add image processor''',  use_temp_dir=snake_case,  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
					    type=str,
					    help="""URL of the original DPT checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					)
					parser.add_argument(
					    """--model_name""",
					    default="""dpt-large""",
					    type=str,
					    help="""Name of the model, in case you're pushing to the hub.""",
					)

					SCREAMING_SNAKE_CASE__      :			int							=		parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






