

'''simple docstring'''




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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






@require_sentencepiece
@require_tokenizers
class   _SCREAMING_SNAKE_CASE   (   lowercase__		,unittest.TestCase     ):
	__SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					ReformerTokenizer
	__SCREAMING_SNAKE_CASE					:Any      	=					ReformerTokenizerFast
	__SCREAMING_SNAKE_CASE					:List[Any]      	=					True
	__SCREAMING_SNAKE_CASE					:Dict      	=					False
	__SCREAMING_SNAKE_CASE					:Dict      	=					True



	def 			snake_case__  (   self		:   List[Any]      ):
						super().setUp()

						__magic_name__						       =    ReformerTokenizer(_UpperCamelCase			,				keep_accents=_UpperCamelCase      )
						tokenizer.save_pretrained(self.tmpdirname      )



	def 			snake_case__  (   self		:   List[Any]      ):
						__magic_name__						       =    """<s>"""
						__magic_name__						       =    1

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



	def 			snake_case__  (   self		:   Any      ):
						__magic_name__						       =    list(self.get_tokenizer().get_vocab().keys()      )

						self.assertEqual(vocab_keys[0]			,				'''<unk>'''      )
						self.assertEqual(vocab_keys[1]			,				'''<s>'''      )
						self.assertEqual(vocab_keys[-1]			,				'''j'''      )
						self.assertEqual(len(_UpperCamelCase      )			,				1000      )



	def 			snake_case__  (   self		:   Tuple      ):
						self.assertEqual(self.get_tokenizer().vocab_size			,				1000      )



	def 			snake_case__  (   self		:   Union[str, Any]      ):
						if not self.test_rust_tokenizer:
											return

						__magic_name__						       =    self.get_tokenizer()
						__magic_name__						       =    self.get_rust_tokenizer()

						__magic_name__						       =    """I was born in 92000, and this is falsé."""

						__magic_name__						       =    tokenizer.tokenize(_UpperCamelCase      )
						__magic_name__						       =    rust_tokenizer.tokenize(_UpperCamelCase      )
						self.assertListEqual(_UpperCamelCase			,				_UpperCamelCase      )

						__magic_name__						       =    tokenizer.encode(_UpperCamelCase			,				add_special_tokens=_UpperCamelCase      )
						__magic_name__						       =    rust_tokenizer.encode(_UpperCamelCase			,				add_special_tokens=_UpperCamelCase      )
						self.assertListEqual(_UpperCamelCase			,				_UpperCamelCase      )

						__magic_name__						       =    self.get_rust_tokenizer()
						__magic_name__						       =    tokenizer.encode(_UpperCamelCase      )
						__magic_name__						       =    rust_tokenizer.encode(_UpperCamelCase      )
						self.assertListEqual(_UpperCamelCase			,				_UpperCamelCase      )



	def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   str=15      ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																__magic_name__						       =    self.rust_tokenizer_class.from_pretrained(_UpperCamelCase			,				**_UpperCamelCase      )

																# Simple input
																__magic_name__						       =    """This is a simple input"""
																__magic_name__						       =    ["""This is a simple input 1""", """This is a simple input 2"""]
																__magic_name__						       =    ("""This is a simple input""", """This is a pair""")
																__magic_name__						       =    [
																    ("""This is a simple input 1""", """This is a simple input 2"""),
																    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																]

																# Simple input tests
																self.assertRaises(_UpperCamelCase			,				tokenizer_r.encode			,				_UpperCamelCase			,				max_length=_UpperCamelCase			,				padding='''max_length'''      )

																# Simple input
																self.assertRaises(_UpperCamelCase			,				tokenizer_r.encode_plus			,				_UpperCamelCase			,				max_length=_UpperCamelCase			,				padding='''max_length'''      )

																# Simple input
																self.assertRaises(
																    _UpperCamelCase			,				tokenizer_r.batch_encode_plus			,				_UpperCamelCase			,				max_length=_UpperCamelCase			,				padding='''max_length'''			,				)

																# Pair input
																self.assertRaises(_UpperCamelCase			,				tokenizer_r.encode			,				_UpperCamelCase			,				max_length=_UpperCamelCase			,				padding='''max_length'''      )

																# Pair input
																self.assertRaises(_UpperCamelCase			,				tokenizer_r.encode_plus			,				_UpperCamelCase			,				max_length=_UpperCamelCase			,				padding='''max_length'''      )

																# Pair input
																self.assertRaises(
																    _UpperCamelCase			,				tokenizer_r.batch_encode_plus			,				_UpperCamelCase			,				max_length=_UpperCamelCase			,				padding='''max_length'''			,				)



	def 			snake_case__  (   self		:   str      ):
						pass



	def 			snake_case__  (   self		:   Optional[Any]      ):
						__magic_name__						       =    ReformerTokenizer(_UpperCamelCase			,				keep_accents=_UpperCamelCase      )

						__magic_name__						       =    tokenizer.tokenize('''This is a test'''      )
						self.assertListEqual(_UpperCamelCase			,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(_UpperCamelCase      )			,				[285, 46, 10, 170, 382]			,				)

						__magic_name__						       =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
						self.assertListEqual(
						    _UpperCamelCase			,				[
						        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__						       =    tokenizer.convert_tokens_to_ids(_UpperCamelCase      )
						self.assertListEqual(
						    _UpperCamelCase			,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,				)

						__magic_name__						       =    tokenizer.convert_ids_to_tokens(_UpperCamelCase      )
						self.assertListEqual(
						    _UpperCamelCase			,				[
						        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>''',
						        '''.''',
						    ]			,				)



	@cached_property
	def 			snake_case__  (   self		:   Union[str, Any]      ):
						return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''      )



	@slow
	def 			snake_case__  (   self		:   Dict      ):
						__magic_name__						       =    """Hello World!"""
						__magic_name__						       =    [126, 32, 262, 152, 38, 72, 287]

						self.assertListEqual(_UpperCamelCase			,				self.big_tokenizer.encode(_UpperCamelCase      )      )



	@slow
	def 			snake_case__  (   self		:   Optional[int]      ):
						__magic_name__						       =    (
						    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
						    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
						)
						__magic_name__						       =    [
						    108,
						    265,
						    24,
						    111,
						    4,
						    258,
						    156,
						    35,
						    28,
						    275,
						    3,
						    259,
						    297,
						    260,
						    84,
						    4,
						    35,
						    110,
						    44,
						    8,
						    259,
						    91,
						    268,
						    21,
						    11,
						    209,
						    274,
						    109,
						    266,
						    277,
						    117,
						    86,
						    93,
						    315,
						    258,
						    278,
						    258,
						    277,
						    258,
						    0,
						    258,
						    288,
						    258,
						    319,
						    258,
						    0,
						    258,
						    0,
						    258,
						    0,
						    258,
						    0,
						    258,
						    287,
						    258,
						    315,
						    258,
						    289,
						    258,
						    278,
						    99,
						    269,
						    266,
						    262,
						    8,
						    259,
						    241,
						    4,
						    217,
						    230,
						    268,
						    266,
						    55,
						    168,
						    106,
						    75,
						    193,
						    266,
						    223,
						    27,
						    49,
						    26,
						    282,
						    25,
						    264,
						    299,
						    19,
						    26,
						    0,
						    258,
						    277,
						    117,
						    86,
						    93,
						    176,
						    183,
						    270,
						    11,
						    262,
						    42,
						    61,
						    265,
						]

						self.assertListEqual(_UpperCamelCase			,				self.big_tokenizer.encode(_UpperCamelCase      )      )



	@require_torch
	@slow
	def 			snake_case__  (   self		:   Dict      ):
						import torch

						from transformers import ReformerConfig, ReformerModel

						# Build sequence
						__magic_name__						       =    list(self.big_tokenizer.get_vocab().keys()      )[:10]
						__magic_name__						       =    """ """.join(_UpperCamelCase      )
						__magic_name__						       =    self.big_tokenizer.encode_plus(_UpperCamelCase			,				return_tensors='''pt'''      )
						__magic_name__						       =    self.big_tokenizer.batch_encode_plus([sequence, sequence]			,				return_tensors='''pt'''      )

						__magic_name__						       =    ReformerConfig()
						# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
						__magic_name__						       =    encoded_sequence["""input_ids"""].shape
						__magic_name__						       =    ReformerModel(_UpperCamelCase      )

						# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
						assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

						with torch.no_grad():
											model(**_UpperCamelCase      )
											model(**_UpperCamelCase      )



	@slow
	def 			snake_case__  (   self		:   List[str]      ):
						# fmt: off
						__magic_name__						       =    {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """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]]}  # noqa: E501
						# fmt: on

						# This tokenizer does not know some characters like ")".
						# That is the reason why we use very simple texts here.
						# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
						__magic_name__						       =    [
						    """This is a very simple sentence.""",
						    """The quick brown fox jumps over the lazy dog.""",
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=_UpperCamelCase			,				model_name='''google/reformer-crime-and-punishment'''			,				revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a'''			,				padding=_UpperCamelCase			,				sequences=_UpperCamelCase			,				)









'''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()
_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    SwinConfig.from_pretrained(
         '''microsoft/swin-tiny-patch4-window7-224'''		,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
     __magic_name__						       =    MaskFormerConfig(backbone_config=a			)

     __magic_name__						       =    '''huggingface/label-files'''
     if "ade20k-full" in model_name:
          # this should be ok
          __magic_name__						       =    847
          __magic_name__						       =    '''maskformer-ade20k-full-id2label.json'''
     elif "ade" in model_name:
          # this should be ok
          __magic_name__						       =    150
          __magic_name__						       =    '''ade20k-id2label.json'''
     elif "coco-stuff" in model_name:
          # this should be ok
          __magic_name__						       =    171
          __magic_name__						       =    '''maskformer-coco-stuff-id2label.json'''
     elif "coco" in model_name:
          # TODO
          __magic_name__						       =    133
          __magic_name__						       =    '''coco-panoptic-id2label.json'''
     elif "cityscapes" in model_name:
          # this should be ok
          __magic_name__						       =    19
          __magic_name__						       =    '''cityscapes-id2label.json'''
     elif "vistas" in model_name:
          # this should be ok
          __magic_name__						       =    65
          __magic_name__						       =    '''mapillary-vistas-id2label.json'''

     __magic_name__						       =    json.load(open(hf_hub_download(a		,						a		,						repo_type='''dataset'''			)		,						'''r'''			)			)
     __magic_name__						       =    {int(a			): v for k, v in idalabel.items()}

     return config




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    []
     # 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 						UpperCamelCase	(			a		,						a		,						a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    dct.pop(a			)
     __magic_name__						       =    val




def 						UpperCamelCase	(			a		,						a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
     for i in range(len(backbone_config.depths			)			):
          __magic_name__						       =    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)
               __magic_name__						       =    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
               __magic_name__						       =    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
               __magic_name__						       =    in_proj_weight[:dim, :]
               __magic_name__						       =    in_proj_bias[: dim]
               __magic_name__						       =    in_proj_weight[
                   dim : dim * 2, :
               ]
               __magic_name__						       =    in_proj_bias[
                   dim : dim * 2
               ]
               __magic_name__						       =    in_proj_weight[
                   -dim :, :
               ]
               __magic_name__						       =    in_proj_bias[-dim :]
               # fmt: on




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     # fmt: off
     __magic_name__						       =    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)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    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)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    in_proj_bias[-hidden_size :]
     # fmt: on




def 						UpperCamelCase	(			)				->     torch.Tensor:






     '''simple docstring'''
     __magic_name__						       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __magic_name__						       =    Image.open(requests.get(a		,						stream=a			).raw			)
     return im




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a		,						a		,						a = False			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    get_maskformer_config(a			)

     # load original state_dict
     with open(a		,						'''rb'''			) as f:
          __magic_name__						       =    pickle.load(a			)
     __magic_name__						       =    data['''model''']

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

     # rename keys
     __magic_name__						       =    create_rename_keys(a			)
     for src, dest in rename_keys:
          rename_key(a		,						a		,						a			)
     read_in_swin_q_k_v(a		,						config.backbone_config			)
     read_in_decoder_q_k_v(a		,						a			)

     # update to torch tensors
     for key, value in state_dict.items():
          __magic_name__						       =    torch.from_numpy(a			)

     # load 🤗 model
     __magic_name__						       =    MaskFormerForInstanceSegmentation(a			)
     model.eval()

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

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

     # verify results
     __magic_name__						       =    prepare_img()
     if "vistas" in model_name:
          __magic_name__						       =    65
     elif "cityscapes" in model_name:
          __magic_name__						       =    6_5535
     else:
          __magic_name__						       =    255
     __magic_name__						       =    True if '''ade''' in model_name else False
     __magic_name__						       =    MaskFormerImageProcessor(ignore_index=a		,						reduce_labels=a			)

     __magic_name__						       =    image_processor(a		,						return_tensors='''pt'''			)

     __magic_name__						       =    model(**a			)

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

     if model_name == "maskformer-swin-tiny-ade":
          __magic_name__						       =    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]		,						a		,						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(a			).mkdir(exist_ok=a			)
          model.save_pretrained(a			)
          image_processor.save_pretrained(a			)

     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__":
      _lowerCAmelCase         =						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."
      )

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






