





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		int				=	GPTaTokenizer
    lowerCamelCase__    :		Any				=	GPTaTokenizerFast
    lowerCamelCase__    :		Optional[int]				=	True
    lowerCamelCase__    :		int				=	{"""add_prefix_space""": True}
    lowerCamelCase__    :		List[str]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
            '''<|endoftext|>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        SCREAMING_SNAKE_CASE__											=			{'''unk_token''': '''<unk>'''}

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''   ,				encoding='''utf-8'''     ) as fp:
            fp.write(json.dumps(A_     ) + '''\n'''     )
        with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return GPTaTokenizer.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return GPTaTokenizerFast.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer(self.vocab_file   ,				self.merges_file   ,				**self.special_tokens_map     )
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_   ,				add_prefix_space=A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        if not self.test_rust_tokenizer:
            return

        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()
        SCREAMING_SNAKE_CASE__											=			self.get_rust_tokenizer(add_prefix_space=A_     )

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''

        # Testing tokenization
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing conversion to ids without special tokens
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_special_tokens=A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.encode(A_   ,				add_special_tokens=A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing conversion to ids with special tokens
        SCREAMING_SNAKE_CASE__											=			self.get_rust_tokenizer(add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.encode(A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing the unknown token
        SCREAMING_SNAKE_CASE__											=			tokens + [rust_tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self   ,				A_=15     ):





        '''simple docstring'''

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                SCREAMING_SNAKE_CASE__											=			self.rust_tokenizer_class.from_pretrained(A_   ,				**A_     )

                # Simple input
                SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
                SCREAMING_SNAKE_CASE__											=			['''This is a simple input 1''', '''This is a simple input 2''']
                SCREAMING_SNAKE_CASE__											=			('''This is a simple input''', '''This is a pair''')
                SCREAMING_SNAKE_CASE__											=			[
                    ('''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(A_   ,				tokenizer_r.encode   ,				A_   ,				max_length=A_   ,				padding='''max_length'''     )

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

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

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

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

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,				pad_token='''<pad>'''     )

        # Simple input
        SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
        SCREAMING_SNAKE_CASE__											=			['''This is a simple input looooooooong''', '''This is a simple input''']
        SCREAMING_SNAKE_CASE__											=			('''This is a simple input''', '''This is a pair''')
        SCREAMING_SNAKE_CASE__											=			[
            ('''This is a simple input loooooong''', '''This is a simple input'''),
            ('''This is a simple pair loooooong''', '''This is a simple pair'''),
        ]

        SCREAMING_SNAKE_CASE__											=			tokenizer.pad_token_id

        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding='''max_length'''   ,				max_length=30   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding=A_   ,				truncate=A_   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(*A_   ,				padding='''max_length'''   ,				max_length=60   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding=A_   ,				truncate=A_   ,				return_tensors='''np'''     )

        # s
        # test single string max_length padding
        self.assertEqual(out_s['''input_ids'''].shape[-1]   ,				30     )
        self.assertTrue(pad_token_id in out_s['''input_ids''']     )
        self.assertTrue(0 in out_s['''attention_mask''']     )

        # s2
        # test automatic padding
        self.assertEqual(out_sa['''input_ids'''].shape[-1]   ,				33     )
        # long slice doesn't have padding
        self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]     )
        self.assertFalse(0 in out_sa['''attention_mask'''][0]     )
        # short slice does have padding
        self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]     )
        self.assertTrue(0 in out_sa['''attention_mask'''][1]     )

        # p
        # test single pair max_length padding
        self.assertEqual(out_p['''input_ids'''].shape[-1]   ,				60     )
        self.assertTrue(pad_token_id in out_p['''input_ids''']     )
        self.assertTrue(0 in out_p['''attention_mask''']     )

        # p2
        # test automatic padding pair
        self.assertEqual(out_pa['''input_ids'''].shape[-1]   ,				52     )
        # long slice pair doesn't have padding
        self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]     )
        self.assertFalse(0 in out_pa['''attention_mask'''][0]     )
        # short slice pair does have padding
        self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]     )
        self.assertTrue(0 in out_pa['''attention_mask'''][1]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''$$$'''
        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,				bos_token=A_   ,				add_bos_token=A_     )

        SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
        SCREAMING_SNAKE_CASE__											=			['''This is a simple input 1''', '''This is a simple input 2''']

        SCREAMING_SNAKE_CASE__											=			tokenizer.bos_token_id

        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     )

        self.assertEqual(out_s.input_ids[0]   ,				A_     )
        self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.decode(out_s.input_ids     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(out_sa.input_ids     )

        self.assertEqual(decode_s.split()[0]   ,				A_     )
        self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[self.get_tokenizer(do_lower_case=A_   ,				add_bos_token=A_     )]
        for tokenizer in tokenizers:
            with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
                SCREAMING_SNAKE_CASE__											=			'''Encode this.'''
                SCREAMING_SNAKE_CASE__											=			'''This one too please.'''
                SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_special_tokens=A_     )
                encoded_sequence += tokenizer.encode(A_   ,				add_special_tokens=A_     )
                SCREAMING_SNAKE_CASE__											=			tokenizer.encode_plus(
                    A_   ,				A_   ,				add_special_tokens=A_   ,				return_special_tokens_mask=A_   ,				)
                SCREAMING_SNAKE_CASE__											=			encoded_sequence_dict['''input_ids''']
                SCREAMING_SNAKE_CASE__											=			encoded_sequence_dict['''special_tokens_mask''']
                self.assertEqual(len(A_     )   ,				len(A_     )     )

                SCREAMING_SNAKE_CASE__											=			[
                    (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_     )
                ]
                SCREAMING_SNAKE_CASE__											=			[x for x in filtered_sequence if x is not None]
                self.assertEqual(A_   ,				A_     )





@require_tokenizers
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				from_slow=A_     )
        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )
        tokenizer.save_pretrained('''test_opt'''     )

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''./test_opt'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				use_slow=A_     )
        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        # Same as above
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )




    @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				from_slow=A_     )

        SCREAMING_SNAKE_CASE__											=			'''bos'''
        SCREAMING_SNAKE_CASE__											=			tokenizer.get_vocab()['''bos''']

        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        # We changed the bos token
        self.assertEqual(A_   ,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
        tokenizer.save_pretrained('''./tok'''     )
        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''./tok'''     )
        self.assertTrue(tokenizer.is_fast     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]     )







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


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224''' ,							out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
    SCREAMING_SNAKE_CASE__											=			MaskFormerConfig(backbone_config=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    if "ade20k-full" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			8_4_7
        SCREAMING_SNAKE_CASE__											=			'''maskformer-ade20k-full-id2label.json'''
    elif "ade" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_5_0
        SCREAMING_SNAKE_CASE__											=			'''ade20k-id2label.json'''
    elif "coco-stuff" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_7_1
        SCREAMING_SNAKE_CASE__											=			'''maskformer-coco-stuff-id2label.json'''
    elif "coco" in model_name:
        # TODO
        SCREAMING_SNAKE_CASE__											=			1_3_3
        SCREAMING_SNAKE_CASE__											=			'''coco-panoptic-id2label.json'''
    elif "cityscapes" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_9
        SCREAMING_SNAKE_CASE__											=			'''cityscapes-id2label.json'''
    elif "vistas" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			6_5
        SCREAMING_SNAKE_CASE__											=			'''mapillary-vistas-id2label.json'''

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

    return config


def 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    # 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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			dct.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			val


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
    for i in range(len(backbone_config.depths	)	):
        SCREAMING_SNAKE_CASE__											=			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)
            SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''	)
            SCREAMING_SNAKE_CASE__											=			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
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[:dim, :]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[: dim]
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[
                dim : dim * 2, :
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[
                dim : dim * 2
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[
                -dim :, :
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[-dim :]
            # fmt: on


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    # fmt: off
    SCREAMING_SNAKE_CASE__											=			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)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[: hidden_size, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:config.hidden_size]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[hidden_size : hidden_size * 2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-hidden_size :, :]
        SCREAMING_SNAKE_CASE__											=			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)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[: hidden_size, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:config.hidden_size]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[hidden_size : hidden_size * 2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-hidden_size :, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[-hidden_size :]
    # fmt: on


def 							__snake_case							(    )				->						torch.Tensor:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


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

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

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

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

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

    # load 🤗 model
    SCREAMING_SNAKE_CASE__											=			MaskFormerForInstanceSegmentation(lowerCAmelCase_	)
    model.eval()

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

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

    # verify results
    SCREAMING_SNAKE_CASE__											=			prepare_img()
    if "vistas" in model_name:
        SCREAMING_SNAKE_CASE__											=			6_5
    elif "cityscapes" in model_name:
        SCREAMING_SNAKE_CASE__											=			6_5_5_3_5
    else:
        SCREAMING_SNAKE_CASE__											=			2_5_5
    SCREAMING_SNAKE_CASE__											=			True if '''ade''' in model_name else False
    SCREAMING_SNAKE_CASE__											=			MaskFormerImageProcessor(ignore_index=lowerCAmelCase_ ,							reduce_labels=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			image_processor(lowerCAmelCase_ ,							return_tensors='''pt'''	)

    SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)

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

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

    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__":
  _A					:				Optional[Any]				=			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."""
  )

  _A					:				str				=			parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
