




"""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


A_						=				get_tests_dir('''fixtures/test_sentencepiece.model''')

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

A_						=				25_00_04
A_						=				25_00_20






@require_sentencepiece
@require_tokenizers
class 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					MBartaaTokenizer
 lowercase__ 					=					MBartaaTokenizerFast
 lowercase__ 					=					True
 lowercase__ 					=					True


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		List[str]								=  MBartaaTokenizer(a_,      src_lang="""en_XX""",      tgt_lang="""ro_RO""",      keep_accents=a_		)
   tokenizer.save_pretrained(self.tmpdirname		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  """<s>"""
   _snake_case							:		List[str]								=  0

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


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		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(a_		),      1_054		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,      1_054		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  MBartaaTokenizer(a_,      src_lang="""en_XX""",      tgt_lang="""ro_RO""",      keep_accents=a_		)

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

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(a_		),      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],      )

   _snake_case							:		List[Any]								=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
   self.assertListEqual(
       a_,      [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""", """."""],      )
   _snake_case							:		Any								=  tokenizer.convert_tokens_to_ids(a_		)
   self.assertListEqual(
       a_,      [
           value + tokenizer.fairseq_offset
           for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
       ],      )

   _snake_case							:		Any								=  tokenizer.convert_ids_to_tokens(a_		)
   self.assertListEqual(
       a_,      [SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""],      )


 @slow
 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  {"""input_ids""": [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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=a_,      model_name="""facebook/mbart-large-50""",      revision="""d3913889c59cd5c9e456b269c376325eabad57e2""",      )


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   if not self.test_slow_tokenizer:
     # as we don't have a slow version, we can't compare the outputs between slow and fast versions
     return

   _snake_case							:		Dict								=  (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})"		):
       _snake_case							:		Dict								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)
       _snake_case							:		List[str]								=  self.tokenizer_class.from_pretrained(a_,      **a_		)

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

       _snake_case							:		Tuple								=  tokenizer_r.save_pretrained(a_		)
       _snake_case							:		Tuple								=  tokenizer_p.save_pretrained(a_		)

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

       # Checks everything loads correctly in the same way
       _snake_case							:		List[Any]								=  tokenizer_r.from_pretrained(a_		)
       _snake_case							:		Any								=  tokenizer_p.from_pretrained(a_		)

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

       shutil.rmtree(a_		)

       # Save tokenizer rust, legacy_format=True
       _snake_case							:		Any								=  tempfile.mkdtemp()

       _snake_case							:		List[str]								=  tokenizer_r.save_pretrained(a_,      legacy_format=a_		)
       _snake_case							:		List[str]								=  tokenizer_p.save_pretrained(a_		)

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

       # Checks everything loads correctly in the same way
       _snake_case							:		Any								=  tokenizer_r.from_pretrained(a_		)
       _snake_case							:		List[Any]								=  tokenizer_p.from_pretrained(a_		)

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

       shutil.rmtree(a_		)

       # Save tokenizer rust, legacy_format=False
       _snake_case							:		List[str]								=  tempfile.mkdtemp()

       _snake_case							:		Dict								=  tokenizer_r.save_pretrained(a_,      legacy_format=a_		)
       _snake_case							:		Optional[Any]								=  tokenizer_p.save_pretrained(a_		)

       # 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
       _snake_case							:		Dict								=  tokenizer_r.from_pretrained(a_		)
       _snake_case							:		int								=  tokenizer_p.from_pretrained(a_		)

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

       shutil.rmtree(a_		)







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







 '''simple docstring'''





 lowercase__ 					=					"facebook/mbart-large-50-one-to-many-mmt"
 lowercase__ 					=					[
     " 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.",
 ]
 lowercase__ 					=					[
     "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.",
 ]
 lowercase__ 					=					[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  UpperCamelCase_   (				cls:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		MBartaaTokenizer								=  MBartaaTokenizer.from_pretrained(
       cls.checkpoint_name,      src_lang="""en_XX""",      tgt_lang="""ro_RO"""		)
   _snake_case							:		Union[str, Any]								=  1
   return cls


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""],      250_001		)
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""],      250_004		)
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""],      250_020		)
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""],      250_038		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   self.assertIn(a_,      self.tokenizer.all_special_ids		)
   _snake_case							:		Dict								=  [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
   _snake_case							:		Dict								=  self.tokenizer.decode(a_,      skip_special_tokens=a_		)
   _snake_case							:		Tuple								=  self.tokenizer.decode(generated_ids[1:],      skip_special_tokens=a_		)
   self.assertEqual(a_,      a_		)
   self.assertNotIn(self.tokenizer.eos_token,      a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]		),      [250_053, 250_001]		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



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


 @require_torch
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		int								=  self.tokenizer(self.src_text,      text_target=self.tgt_text,      padding=a_,      return_tensors="""pt"""		)
   _snake_case							:		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  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



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

   self.assertIsInstance(a_,      a_		)

   self.assertEqual((2, 14),      batch.input_ids.shape		)
   self.assertEqual((2, 14),      batch.attention_mask.shape		)
   _snake_case							:		Optional[Any]								=  batch.input_ids.tolist()[0]
   self.assertListEqual(self.expected_src_tokens,      a_		)
   self.assertEqual(2,      batch.decoder_input_ids[0, 0]		)  # decoder_start_token_id
   # Test that special tokens are reset
   self.assertEqual(self.tokenizer.prefix_tokens,      [EN_CODE]		)
   self.assertEqual(self.tokenizer.suffix_tokens,      [self.tokenizer.eos_token_id]		)


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




   '''simple docstring'''



   _snake_case							:		int								=  self.tokenizer(self.src_text,      padding=a_,      truncation=a_,      max_length=3,      return_tensors="""pt"""		)
   _snake_case							:		int								=  self.tokenizer(
       text_target=self.tgt_text,      padding=a_,      truncation=a_,      max_length=10,      return_tensors="""pt"""		)
   _snake_case							:		Union[str, Any]								=  targets["""input_ids"""]
   _snake_case							:		Optional[int]								=  shift_tokens_right(a_,      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  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



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

   self.assertEqual(
       nested_simplify(a_		),      {
           # en_XX, A, test, EOS
           """input_ids""": [[250_004, 62, 3_034, 2]],
           """attention_mask""": [[1, 1, 1, 1]],
           # ar_AR
           """forced_bos_token_id""": 250_001,
       },      )







'''simple docstring'''






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()
UpperCamelCase__:   Optional[int]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   Optional[int]:
							UpperCAmelCase :       Tuple 							=		DPTConfig(embedding_type='''hybrid'''  )

							if "large" in checkpoint_url:
														UpperCAmelCase :       Tuple 							=		1024
														UpperCAmelCase :       List[Any] 							=		4096
														UpperCAmelCase :       str 							=		24
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		[5, 11, 17, 23]
														UpperCAmelCase :       List[Any] 							=		[256, 512, 1024, 1024]
														UpperCAmelCase :       Tuple 							=		(1, 384, 384)

							if "nyu" or "midas" in checkpoint_url:
														UpperCAmelCase :       Optional[Any] 							=		768
														UpperCAmelCase :       Tuple 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       int 							=		[256, 512, 768, 768]
														UpperCAmelCase :       Any 							=		150
														UpperCAmelCase :       Tuple 							=		16
														UpperCAmelCase :       Any 							=		(1, 384, 384)
														UpperCAmelCase :       Optional[Any] 							=		False
														UpperCAmelCase :       Tuple 							=		'''project'''

							if "ade" in checkpoint_url:
														UpperCAmelCase :       Any 							=		True
														UpperCAmelCase :       str 							=		768
														UpperCAmelCase :       Optional[int] 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       List[Any] 							=		150
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		'''huggingface/label-files'''
														UpperCAmelCase :       Tuple 							=		'''ade20k-id2label.json'''
														UpperCAmelCase :       Any 							=		json.load(open(cached_download(hf_hub_url(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )  )   ,      '''r'''  )  )
														UpperCAmelCase :       Optional[Any] 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase :       List[Any] 							=		idalabel
														UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in idalabel.items()}
														UpperCAmelCase :       Union[str, Any] 							=		[1, 150, 480, 480]

							return config, expected_shape


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       List[str] 							=		['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   Any:
							if (
							    "pretrained.model" in name
							    and "cls_token" not in name
							    and "pos_embed" not in name
							    and "patch_embed" not in name
							):
														UpperCAmelCase :       Tuple 							=		name.replace('''pretrained.model'''   ,      '''dpt.encoder'''  )
							if "pretrained.model" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''pretrained.model'''   ,      '''dpt.embeddings'''  )
							if "patch_embed" in name:
														UpperCAmelCase :       int 							=		name.replace('''patch_embed'''   ,      ''''''  )
							if "pos_embed" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''pos_embed'''   ,      '''position_embeddings'''  )
							if "attn.proj" in name:
														UpperCAmelCase :       Any 							=		name.replace('''attn.proj'''   ,      '''attention.output.dense'''  )
							if "proj" in name and "project" not in name:
														UpperCAmelCase :       str 							=		name.replace('''proj'''   ,      '''projection'''  )
							if "blocks" in name:
														UpperCAmelCase :       Any 							=		name.replace('''blocks'''   ,      '''layer'''  )
							if "mlp.fc1" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''mlp.fc1'''   ,      '''intermediate.dense'''  )
							if "mlp.fc2" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''mlp.fc2'''   ,      '''output.dense'''  )
							if "norm1" in name and "backbone" not in name:
														UpperCAmelCase :       Dict 							=		name.replace('''norm1'''   ,      '''layernorm_before'''  )
							if "norm2" in name and "backbone" not in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''norm2'''   ,      '''layernorm_after'''  )
							if "scratch.output_conv" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''scratch.output_conv'''   ,      '''head'''  )
							if "scratch" in name:
														UpperCAmelCase :       str 							=		name.replace('''scratch'''   ,      '''neck'''  )
							if "layer1_rn" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''layer1_rn'''   ,      '''convs.0'''  )
							if "layer2_rn" in name:
														UpperCAmelCase :       int 							=		name.replace('''layer2_rn'''   ,      '''convs.1'''  )
							if "layer3_rn" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''layer3_rn'''   ,      '''convs.2'''  )
							if "layer4_rn" in name:
														UpperCAmelCase :       int 							=		name.replace('''layer4_rn'''   ,      '''convs.3'''  )
							if "refinenet" in name:
														UpperCAmelCase :       List[str] 							=		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
														UpperCAmelCase :       str 							=		name.replace(f"""refinenet{layer_idx}"""   ,      f"""fusion_stage.layers.{abs(layer_idx-4  )}"""  )
							if "out_conv" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''out_conv'''   ,      '''projection'''  )
							if "resConfUnit1" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''resConfUnit1'''   ,      '''residual_layer1'''  )
							if "resConfUnit2" in name:
														UpperCAmelCase :       Any 							=		name.replace('''resConfUnit2'''   ,      '''residual_layer2'''  )
							if "conv1" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''conv1'''   ,      '''convolution1'''  )
							if "conv2" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''conv2'''   ,      '''convolution2'''  )
							# readout blocks
							if "pretrained.act_postprocess1.0.project.0" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''pretrained.act_postprocess1.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.0.0'''  )
							if "pretrained.act_postprocess2.0.project.0" in name:
														UpperCAmelCase :       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:
														UpperCAmelCase :       Any 							=		name.replace('''pretrained.act_postprocess3.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.2.0'''  )
							if "pretrained.act_postprocess4.0.project.0" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''pretrained.act_postprocess4.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.3.0'''  )

							# resize blocks
							if "pretrained.act_postprocess1.3" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''pretrained.act_postprocess1.3'''   ,      '''neck.reassemble_stage.layers.0.projection'''  )
							if "pretrained.act_postprocess1.4" in name:
														UpperCAmelCase :       Any 							=		name.replace('''pretrained.act_postprocess1.4'''   ,      '''neck.reassemble_stage.layers.0.resize'''  )
							if "pretrained.act_postprocess2.3" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''pretrained.act_postprocess2.3'''   ,      '''neck.reassemble_stage.layers.1.projection'''  )
							if "pretrained.act_postprocess2.4" in name:
														UpperCAmelCase :       str 							=		name.replace('''pretrained.act_postprocess2.4'''   ,      '''neck.reassemble_stage.layers.1.resize'''  )
							if "pretrained.act_postprocess3.3" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''pretrained.act_postprocess3.3'''   ,      '''neck.reassemble_stage.layers.2.projection'''  )
							if "pretrained.act_postprocess4.3" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''pretrained.act_postprocess4.3'''   ,      '''neck.reassemble_stage.layers.3.projection'''  )
							if "pretrained.act_postprocess4.4" in name:
														UpperCAmelCase :       int 							=		name.replace('''pretrained.act_postprocess4.4'''   ,      '''neck.reassemble_stage.layers.3.resize'''  )
							if "pretrained" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''pretrained'''   ,      '''dpt'''  )
							if "bn" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''bn'''   ,      '''batch_norm'''  )
							if "head" in name:
														UpperCAmelCase :       Any 							=		name.replace('''head'''   ,      '''head.head'''  )
							if "encoder.norm" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''encoder.norm'''   ,      '''layernorm'''  )
							if "auxlayer" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''auxlayer'''   ,      '''auxiliary_head.head'''  )
							if "backbone" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''backbone'''   ,      '''backbone.bit.encoder'''  )

							if ".." in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''..'''   ,      '''.'''  )

							if "stem.conv" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''stem.conv'''   ,      '''bit.embedder.convolution'''  )
							if "blocks" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''blocks'''   ,      '''layers'''  )
							if "convolution" in name and "backbone" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''convolution'''   ,      '''conv'''  )
							if "layer" in name and "backbone" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''layer'''   ,      '''layers'''  )
							if "backbone.bit.encoder.bit" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''backbone.bit.encoder.bit'''   ,      '''backbone.bit'''  )
							if "embedder.conv" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''embedder.conv'''   ,      '''embedder.convolution'''  )
							if "backbone.bit.encoder.stem.norm" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''backbone.bit.encoder.stem.norm'''   ,      '''backbone.bit.embedder.norm'''  )
							return name


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


def  snake_case_     (					)      ->   List[str]:
							UpperCAmelCase :       Optional[int] 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Optional[int] 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							return im


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						List[str]  )      ->   Any:
							UpperCAmelCase							,		UpperCAmelCase :       int 							=		get_dpt_config(_lowerCAmelCase  )
							# load original state_dict from URL
							# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
							UpperCAmelCase :       List[Any] 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )
							# remove certain keys
							remove_ignore_keys_(_lowerCAmelCase  )
							# rename keys
							for key in state_dict.copy().keys():
														UpperCAmelCase :       Any 							=		state_dict.pop(_lowerCAmelCase  )
														UpperCAmelCase :       List[Any] 							=		val
							# read in qkv matrices
							read_in_q_k_v(_lowerCAmelCase   ,      _lowerCAmelCase  )

							# load HuggingFace model
							UpperCAmelCase :       Optional[Any] 							=		DPTForSemanticSegmentation(_lowerCAmelCase  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )
							model.eval()

							# Check outputs on an image
							UpperCAmelCase :       int 							=		480 if '''ade''' in checkpoint_url else 384
							UpperCAmelCase :       List[Any] 							=		DPTImageProcessor(size=_lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		prepare_img()
							UpperCAmelCase :       Optional[int] 							=		image_processor(_lowerCAmelCase   ,      return_tensors='''pt'''  )

							# forward pass
							UpperCAmelCase :       Any 							=		model(**_lowerCAmelCase  ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase  ).predicted_depth

							if show_prediction:
														UpperCAmelCase :       Dict 							=		(
														    torch.nn.functional.interpolate(
														        outputs.unsqueeze(1  )   ,      size=(image.size[1], image.size[0])   ,      mode='''bicubic'''   ,      align_corners=_lowerCAmelCase   ,      )
														    .squeeze()
														    .cpu()
														    .numpy()
														)

														Image.fromarray((prediction / prediction.max()) * 255  ).show()

							if pytorch_dump_folder_path is not None:
														Path(_lowerCAmelCase  ).mkdir(exist_ok=_lowerCAmelCase  )
														print(f"""Saving model to {pytorch_dump_folder_path}"""  )
														model.save_pretrained(_lowerCAmelCase  )
														print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
														image_processor.save_pretrained(_lowerCAmelCase  )

							if push_to_hub:
														model.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )
														image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )


if __name__ == "__main__":
						UpperCamelCase__:   Dict								=			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=False,
						    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.",
						)
						parser.add_argument(
						    "--show_prediction",
						    action="store_true",
						)

						UpperCamelCase__:   Tuple								=			parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)





