



"""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


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece.model""")

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

SCREAMING_SNAKE_CASE_				= 25_00_04
SCREAMING_SNAKE_CASE_				= 25_00_20




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						UpperCamelCase__     ,unittest.TestCase				):
		__lowerCAmelCase    					=					MBartaaTokenizer
		__lowerCAmelCase    					=					MBartaaTokenizerFast
		__lowerCAmelCase    					=					True
		__lowerCAmelCase    					=					True






		def snake_case_  (   self      ):
				super().setUp()

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






		def snake_case_  (   self      ):
				a_     :      List[str] 						=     "<s>"
				a_     :      Union[str, Any] 						=     0

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






		def snake_case_  (   self      ):
				a_     :      List[str] 						=     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_0_5_4      )






		def snake_case_  (   self      ):
				self.assertEqual(self.get_tokenizer().vocab_size			,     1_0_5_4      )






		def snake_case_  (   self      ):
				a_     :      List[str] 						=     MBartaaTokenizer(__A			,     src_lang="en_XX"			,     tgt_lang="ro_RO"			,     keep_accents=__A      )

				a_     :      Union[str, 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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,     )

				a_     :      Tuple 						=     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", "."]			,     )
				a_     :      List[Any] 						=     tokenizer.convert_tokens_to_ids(__A      )
				self.assertListEqual(
				    __A			,     [
				        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]
				    ]			,     )

				a_     :      Union[str, 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 snake_case_  (   self      ):
				a_     :      int 						=     {"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=__A			,     model_name="facebook/mbart-large-50"			,     revision="d3913889c59cd5c9e456b269c376325eabad57e2"			,     )






		def snake_case_  (   self      ):



				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

				a_     :      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})"""      ):
								a_     :      Optional[int] 						=     self.rust_tokenizer_class.from_pretrained(__A			,     **__A      )
								a_     :      List[str] 						=     self.tokenizer_class.from_pretrained(__A			,     **__A      )

								a_     :      str 						=     tempfile.mkdtemp()

								a_     :      Optional[Any] 						=     tokenizer_r.save_pretrained(__A      )
								a_     :      List[str] 						=     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      )      )
								a_     :      Optional[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
								a_     :      Any 						=     tokenizer_r.from_pretrained(__A      )
								a_     :      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
								a_     :      Optional[Any] 						=     tempfile.mkdtemp()

								a_     :      Optional[Any] 						=     tokenizer_r.save_pretrained(__A			,     legacy_format=__A      )
								a_     :      List[Any] 						=     tokenizer_p.save_pretrained(__A      )

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

								# Checks everything loads correctly in the same way
								a_     :      str 						=     tokenizer_r.from_pretrained(__A      )
								a_     :      Optional[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
								a_     :      Any 						=     tempfile.mkdtemp()

								a_     :      List[Any] 						=     tokenizer_r.save_pretrained(__A			,     legacy_format=__A      )
								a_     :      List[str] 						=     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
								a_     :      List[Any] 						=     tokenizer_r.from_pretrained(__A      )
								a_     :      List[str] 						=     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 			snake_case_						(						unittest.TestCase				):
		__lowerCAmelCase    					=					"facebook/mbart-large-50-one-to-many-mmt"
		__lowerCAmelCase    					=					[
		    " 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.",
		]
		__lowerCAmelCase    					=					[
		    "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.",
		]
		__lowerCAmelCase    					=					[EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]






		@classmethod
		def snake_case_  (   cls      ):
				a_     :      Tuple 						=     MBartaaTokenizer.from_pretrained(
				    cls.checkpoint_name			,     src_lang="en_XX"			,     tgt_lang="ro_RO"      )
				a_     :      List[str] 						=     1
				return cls






		def snake_case_  (   self      ):
				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 snake_case_  (   self      ):
				a_     :      List[str] 						=     self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
				self.assertListEqual(self.expected_src_tokens			,     __A      )






		def snake_case_  (   self      ):
				self.assertIn(__A			,     self.tokenizer.all_special_ids      )
				a_     :      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]
				a_     :      Optional[Any] 						=     self.tokenizer.decode(__A			,     skip_special_tokens=__A      )
				a_     :      Any 						=     self.tokenizer.decode(generated_ids[1:]			,     skip_special_tokens=__A      )
				self.assertEqual(__A			,     __A      )
				self.assertNotIn(self.tokenizer.eos_token			,     __A      )






		def snake_case_  (   self      ):
				a_     :      Optional[Any] 						=     ["this is gunna be a long sentence " * 2_0]
				assert isinstance(src_text[0]			,     __A      )
				a_     :      Dict 						=     1_0
				a_     :      List[str] 						=     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 snake_case_  (   self      ):
				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]      )			,     [2_5_0_0_5_3, 2_5_0_0_0_1]      )






		def snake_case_  (   self      ):
				a_     :      str 						=     tempfile.mkdtemp()
				a_     :      Dict 						=     self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(__A      )
				a_     :      str 						=     MBartaaTokenizer.from_pretrained(__A      )
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,     __A      )






		@require_torch
		def snake_case_  (   self      ):
				a_     :      Union[str, Any] 						=     self.tokenizer(self.src_text			,     text_target=self.tgt_text			,     padding=__A			,     return_tensors="pt"      )
				a_     :      List[str] 						=     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 snake_case_  (   self      ):
				a_     :      Optional[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"			,     )
				a_     :      int 						=     shift_tokens_right(batch["labels"]			,     self.tokenizer.pad_token_id      )

				self.assertIsInstance(__A			,     __A      )

				self.assertEqual((2, 1_4)			,     batch.input_ids.shape      )
				self.assertEqual((2, 1_4)			,     batch.attention_mask.shape      )
				a_     :      Union[str, 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 snake_case_  (   self      ):
				a_     :      List[str] 						=     self.tokenizer(self.src_text			,     padding=__A			,     truncation=__A			,     max_length=3			,     return_tensors="pt"      )
				a_     :      Union[str, Any] 						=     self.tokenizer(
				    text_target=self.tgt_text			,     padding=__A			,     truncation=__A			,     max_length=1_0			,     return_tensors="pt"      )
				a_     :      int 						=     targets["input_ids"]
				a_     :      Optional[Any] 						=     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]			,     1_0      )






		@require_torch
		def snake_case_  (   self      ):
				a_     :      str 						=     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": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
				        "attention_mask": [[1, 1, 1, 1]],
				        # ar_AR
				        "forced_bos_token_id": 2_5_0_0_0_1,
				    }			,     )




"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      List[Any] 						=     SwinConfig.from_pretrained(
		    "microsoft/swin-tiny-patch4-window7-224", out_features=["stage1", "stage2", "stage3", "stage4"]	)
		a_     :      List[str] 						=     MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE__	)

		a_     :      Dict 						=     "huggingface/label-files"
		if "ade20k-full" in model_name:
				# this should be ok
				a_     :      List[str] 						=     847
				a_     :      Optional[Any] 						=     "maskformer-ade20k-full-id2label.json"
		elif "ade" in model_name:
				# this should be ok
				a_     :      List[str] 						=     150
				a_     :      Tuple 						=     "ade20k-id2label.json"
		elif "coco-stuff" in model_name:
				# this should be ok
				a_     :      Union[str, Any] 						=     171
				a_     :      Union[str, Any] 						=     "maskformer-coco-stuff-id2label.json"
		elif "coco" in model_name:
				# TODO
				a_     :      Optional[Any] 						=     133
				a_     :      List[str] 						=     "coco-panoptic-id2label.json"
		elif "cityscapes" in model_name:
				# this should be ok
				a_     :      Union[str, Any] 						=     19
				a_     :      Any 						=     "cityscapes-id2label.json"
		elif "vistas" in model_name:
				# this should be ok
				a_     :      int 						=     65
				a_     :      Any 						=     "mapillary-vistas-id2label.json"

		a_     :      List[str] 						=     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, repo_type="dataset"	), "r"	)	)
		a_     :      Optional[Any] 						=     {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}

		return config



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		a_     :      str 						=     []
		# stem
		# fmt: off
		rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")	)
		rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")	)
		rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")	)
		rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")	)
		# stages
		for i in range(len(config.backbone_config.depths	)	):
				for j in range(config.backbone_config.depths[i]	):
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")	)

				if i < 3:
						rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")	)
				rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")	)
				rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")	)

		# FPN
		rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")	)
		rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")	)
		rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")	)
		for source_index, target_index in zip(range(3, 0, -1	), range(0, 3	)	):
				rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")	)
				rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")	)
				rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")	)
				rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")	)
				rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")	)
				rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")	)
		rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")	)
		rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")	)

		# Transformer decoder
		for idx in range(config.decoder_config.decoder_layers	):
				# self-attention out projection
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")	)
				# cross-attention out projection
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")	)
				# MLP 1
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")	)
				# MLP 2
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")	)
				# layernorm 1 (self-attention layernorm)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")	)
				# layernorm 2 (cross-attention layernorm)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")	)
				# layernorm 3 (final layernorm)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")	)

		rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")	)
		rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")	)

		# heads on top
		rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")	)

		rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")	)
		rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")	)

		rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")	)
		rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")	)

		for i in range(3	):
				rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")	)
		# fmt: on

		return rename_keys



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Optional[int] 						=     dct.pop(SCREAMING_SNAKE_CASE__	)
		a_     :      List[Any] 						=     val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		a_     :      int 						=     [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
		for i in range(len(backbone_config.depths	)	):
				a_     :      Optional[int] 						=     num_features[i]
				for j in range(backbone_config.depths[i]	):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						a_     :      List[str] 						=     state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
						a_     :      str 						=     state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""	)
						# next, add query, keys and values (in that order) to the state dict
						a_     :      Tuple 						=     in_proj_weight[:dim, :]
						a_     :      Union[str, Any] 						=     in_proj_bias[: dim]
						a_     :      Dict 						=     in_proj_weight[
						    dim : dim * 2, :
						]
						a_     :      Tuple 						=     in_proj_bias[
						    dim : dim * 2
						]
						a_     :      Optional[int] 						=     in_proj_weight[
						    -dim :, :
						]
						a_     :      str 						=     in_proj_bias[-dim :]
						# fmt: on



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
		# fmt: off
		a_     :      List[str] 						=     config.decoder_config.hidden_size
		for idx in range(config.decoder_config.decoder_layers	):
				# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
				a_     :      Optional[int] 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""	)
				a_     :      int 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""	)
				# next, add query, keys and values (in that order) to the state dict
				a_     :      int 						=     in_proj_weight[: hidden_size, :]
				a_     :      Tuple 						=     in_proj_bias[:config.hidden_size]
				a_     :      List[Any] 						=     in_proj_weight[hidden_size : hidden_size * 2, :]
				a_     :      Dict 						=     in_proj_bias[hidden_size : hidden_size * 2]
				a_     :      Optional[int] 						=     in_proj_weight[-hidden_size :, :]
				a_     :      List[str] 						=     in_proj_bias[-hidden_size :]
				# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
				a_     :      Union[str, Any] 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""	)
				a_     :      str 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""	)
				# next, add query, keys and values (in that order) to the state dict
				a_     :      Dict 						=     in_proj_weight[: hidden_size, :]
				a_     :      Optional[Any] 						=     in_proj_bias[:config.hidden_size]
				a_     :      List[Any] 						=     in_proj_weight[hidden_size : hidden_size * 2, :]
				a_     :      str 						=     in_proj_bias[hidden_size : hidden_size * 2]
				a_     :      List[Any] 						=     in_proj_weight[-hidden_size :, :]
				a_     :      Dict 						=     in_proj_bias[-hidden_size :]
		# fmt: on



def        lowerCAmelCase_						(							)			->      torch.Tensor:
		a_     :      Any 						=     "http://images.cocodataset.org/val2017/000000039769.jpg"
		a_     :      Optional[Any] 						=     Image.open(requests.get(SCREAMING_SNAKE_CASE__, stream=SCREAMING_SNAKE_CASE__	).raw	)
		return im



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False	)			->      Optional[int]:
		a_     :      List[Any] 						=     get_maskformer_config(SCREAMING_SNAKE_CASE__	)

		# load original state_dict
		with open(SCREAMING_SNAKE_CASE__, "rb"	) as f:
				a_     :      int 						=     pickle.load(SCREAMING_SNAKE_CASE__	)
		a_     :      Optional[int] 						=     data["model"]

		# for name, param in state_dict.items():
		#     print(name, param.shape)

		# rename keys
		a_     :      List[Any] 						=     create_rename_keys(SCREAMING_SNAKE_CASE__	)
		for src, dest in rename_keys:
				rename_key(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__, config.backbone_config	)
		read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# update to torch tensors
		for key, value in state_dict.items():
				a_     :      Tuple 						=     torch.from_numpy(SCREAMING_SNAKE_CASE__	)

		# load 🤗 model
		a_     :      Tuple 						=     MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__	)
		model.eval()

		for name, param in model.named_parameters():
				print(SCREAMING_SNAKE_CASE__, param.shape	)

		a_						,				a_     :      int 						=     model.load_state_dict(SCREAMING_SNAKE_CASE__, strict=SCREAMING_SNAKE_CASE__	)
		assert missing_keys == [
		    "model.pixel_level_module.encoder.model.layernorm.weight",
		    "model.pixel_level_module.encoder.model.layernorm.bias",
		]
		assert len(SCREAMING_SNAKE_CASE__	) == 0, F"""Unexpected keys: {unexpected_keys}"""

		# verify results
		a_     :      List[Any] 						=     prepare_img()
		if "vistas" in model_name:
				a_     :      int 						=     65
		elif "cityscapes" in model_name:
				a_     :      str 						=     65_535
		else:
				a_     :      int 						=     255
		a_     :      List[str] 						=     True if "ade" in model_name else False
		a_     :      Any 						=     MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE__, reduce_labels=SCREAMING_SNAKE_CASE__	)

		a_     :      int 						=     image_processor(SCREAMING_SNAKE_CASE__, return_tensors="pt"	)

		a_     :      Optional[int] 						=     model(**SCREAMING_SNAKE_CASE__	)

		print("Logits:", outputs.class_queries_logits[0, :3, :3]	)

		if model_name == "maskformer-swin-tiny-ade":
				a_     :      Union[str, Any] 						=     torch.tensor(
				    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]	)
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3], SCREAMING_SNAKE_CASE__, atol=1e-4	)
		print("Looks ok!"	)

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

		if push_to_hub:
				print("Pushing model and image processor to the hub..."	)
				model.push_to_hub(F"""nielsr/{model_name}"""	)
				image_processor.push_to_hub(F"""nielsr/{model_name}"""	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""maskformer-swin-tiny-ade""",
					    type=str,
					    help=("""Name of the MaskFormer model you'd like to convert""",),
					)
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
					    type=str,
					    help="""Path to the original state dict (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)