





"""simple docstring"""





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
							from transformers.models.esm.modeling_esm import (
							    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    EsmEmbeddings,
							    create_position_ids_from_input_ids,
							)
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""




				def __init__(  self	:List[Any]    ,     __lowercase	:Any    ,     __lowercase	:str=13    ,     __lowercase	:List[Any]=7    ,     __lowercase	:int=False    ,     __lowercase	:Dict=True    ,     __lowercase	:Optional[int]=False    ,     __lowercase	:str=True    ,     __lowercase	:List[str]=33    ,     __lowercase	:Dict=32    ,     __lowercase	:List[str]=5    ,     __lowercase	:List[str]=4    ,     __lowercase	:int=37    ,     __lowercase	:List[Any]="gelu"    ,     __lowercase	:List[Any]=0.1    ,     __lowercase	:Dict=0.1    ,     __lowercase	:Dict=512    ,     __lowercase	:int=16    ,     __lowercase	:Any=2    ,     __lowercase	:Dict=0.02    ,     __lowercase	:str=3    ,     __lowercase	:str=4    ,     __lowercase	:List[Any]=None    ,     ):
											__lowerCamelCase   :   List[str]       =parent
											__lowerCamelCase   :   Optional[Any]       =batch_size
											__lowerCamelCase   :   Optional[Any]       =seq_length
											__lowerCamelCase   :   Union[str, Any]       =is_training
											__lowerCamelCase   :   Optional[Any]       =use_input_mask
											__lowerCamelCase   :   int       =use_token_type_ids
											__lowerCamelCase   :   int       =use_labels
											__lowerCamelCase   :   List[Any]       =vocab_size
											__lowerCamelCase   :   Any       =hidden_size
											__lowerCamelCase   :   Any       =num_hidden_layers
											__lowerCamelCase   :   int       =num_attention_heads
											__lowerCamelCase   :   List[str]       =intermediate_size
											__lowerCamelCase   :   Dict       =hidden_act
											__lowerCamelCase   :   int       =hidden_dropout_prob
											__lowerCamelCase   :   List[str]       =attention_probs_dropout_prob
											__lowerCamelCase   :   Any       =max_position_embeddings
											__lowerCamelCase   :   List[Any]       =type_vocab_size
											__lowerCamelCase   :   List[Any]       =type_sequence_label_size
											__lowerCamelCase   :   Dict       =initializer_range
											__lowerCamelCase   :   List[str]       =num_labels
											__lowerCamelCase   :   Optional[int]       =num_choices
											__lowerCamelCase   :   Optional[int]       =scope




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   Any       =ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size      )

											__lowerCamelCase   :   Optional[Any]       =None
											if self.use_input_mask:
																		__lowerCamelCase   :   Tuple       =random_attention_mask([self.batch_size, self.seq_length]      )

											__lowerCamelCase   :   Tuple       =None
											__lowerCamelCase   :   Optional[int]       =None
											__lowerCamelCase   :   Optional[int]       =None
											if self.use_labels:
																		__lowerCamelCase   :   Union[str, Any]       =ids_tensor([self.batch_size]    ,     self.type_sequence_label_size      )
																		__lowerCamelCase   :   Optional[Any]       =ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels      )
																		__lowerCamelCase   :   List[str]       =ids_tensor([self.batch_size]    ,     self.num_choices      )

											__lowerCamelCase   :   List[str]       =self.get_config()

											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




				def        __lowercase	(  self	:List[Any]      ):
											return EsmConfig(
											    vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     pad_token_id=1    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     hidden_act=self.hidden_act    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     initializer_range=self.initializer_range    ,     )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:List[Any]    ,     __lowercase	:str    ,     __lowercase	:Any    ,     __lowercase	:List[Any]    ,     __lowercase	:Any    ,     __lowercase	:Union[str, Any]      ):
											__lowerCamelCase   :   str       =EsmModel(config=A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   List[str]       =model(A_    ,     attention_mask=A_      )
											__lowerCamelCase   :   Optional[int]       =model(A_      )
											__lowerCamelCase   :   Tuple       =model(A_      )

											self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)      )
											self.parent.assertEqual(result.pooler_output.shape    ,     (self.batch_size, self.hidden_size)      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:int    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:Optional[Any]    ,     __lowercase	:str    ,     __lowercase	:Dict    ,     __lowercase	:Optional[int]      ):
											__lowerCamelCase   :   str       =EsmForMaskedLM(config=A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   Tuple       =model(A_    ,     attention_mask=A_    ,     labels=A_      )
											self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)      )




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:Any    ,     __lowercase	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:str      ):
											__lowerCamelCase   :   Tuple       =self.num_labels
											__lowerCamelCase   :   int       =EsmForTokenClassification(config=A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   Any       =model(A_    ,     attention_mask=A_    ,     labels=A_      )
											self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)      )




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Any       =self.prepare_config_and_inputs()
											(
											    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    		
											)   :   Dict       =config_and_inputs
											__lowerCamelCase   :   Tuple       ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict

@require_torch
class 		SCREAMING_SNAKE_CASE_				(     __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							List[str]									=		False

				__snake_case :							Dict									=		(
				    (
				        EsmForMaskedLM,
				        EsmModel,
				        EsmForSequenceClassification,
				        EsmForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__snake_case :							Dict									=		()
				__snake_case :							Optional[int]									=		(
				    {
				        """feature-extraction""": EsmModel,
				        """fill-mask""": EsmForMaskedLM,
				        """text-classification""": EsmForSequenceClassification,
				        """token-classification""": EsmForTokenClassification,
				        """zero-shot""": EsmForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__snake_case :							List[Any]									=		True




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   Dict       =EsmModelTester(self      )
											__lowerCamelCase   :   Any       =ConfigTester(self    ,     config_class=A_    ,     hidden_size=37      )




				def        __lowercase	(  self	:Any      ):
											self.config_tester.run_common_tests()




				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase   :   Any       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_      )




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   int       =self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		__lowerCamelCase   :   List[str]       =type
																		self.model_tester.create_and_check_model(*A_      )




				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase   :   List[Any]       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*A_      )




				def        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   List[Any]       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*A_      )




				@slow
				def        __lowercase	(  self	:List[str]      ):

											for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowerCamelCase   :   str       =EsmModel.from_pretrained(A_      )
																		self.assertIsNotNone(A_      )




				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase   :   Dict       =self.model_tester.prepare_config_and_inputs()[0]
											__lowerCamelCase   :   List[str]       =EsmEmbeddings(config=A_      )

											__lowerCamelCase   :   str       =torch.as_tensor([[12, 31, 13, model.padding_idx]]      )
											__lowerCamelCase   :   List[Any]       =torch.as_tensor(
											    [
											        [
											            0 + model.padding_idx + 1,
											            1 + model.padding_idx + 1,
											            2 + model.padding_idx + 1,
											            model.padding_idx,
											        ]
											    ]      )
											__lowerCamelCase   :   int       =create_position_ids_from_input_ids(A_    ,     model.padding_idx      )
											self.assertEqual(position_ids.shape    ,     expected_positions.shape      )
											self.assertTrue(torch.all(torch.eq(A_    ,     A_      )      )      )




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   int       =self.model_tester.prepare_config_and_inputs()[0]
											__lowerCamelCase   :   Tuple       =EsmEmbeddings(config=A_      )

											__lowerCamelCase   :   Optional[Any]       =torch.empty(2    ,     4    ,     30      )
											__lowerCamelCase   :   Union[str, Any]       =[
											    0 + embeddings.padding_idx + 1,
											    1 + embeddings.padding_idx + 1,
											    2 + embeddings.padding_idx + 1,
											    3 + embeddings.padding_idx + 1,
											]
											__lowerCamelCase   :   List[Any]       =torch.as_tensor([expected_single_positions, expected_single_positions]      )
											__lowerCamelCase   :   Any       =embeddings.create_position_ids_from_inputs_embeds(A_      )
											self.assertEqual(position_ids.shape    ,     expected_positions.shape      )
											self.assertTrue(torch.all(torch.eq(A_    ,     A_      )      )      )




				@unittest.skip('''Esm does not support embedding resizing'''      )
				def        __lowercase	(  self	:Any      ):
											pass




				@unittest.skip('''Esm does not support embedding resizing'''      )
				def        __lowercase	(  self	:Optional[int]      ):
											pass




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
				def        __lowercase	(  self	:List[str]      ):
											pass

@require_torch
class 		SCREAMING_SNAKE_CASE_				(     __SCREAMING_SNAKE_CASE ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:List[Any]      ):

											with torch.no_grad():
																		__lowerCamelCase   :   List[str]       =EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )
																		model.eval()
																		__lowerCamelCase   :   Optional[int]       =torch.tensor([[0, 1, 2, 3, 4, 5]]      )
																		__lowerCamelCase   :   str       =model(A_      )[0]

																		__lowerCamelCase   :   Union[str, Any]       =33

																		__lowerCamelCase   :   int       =torch.Size((1, 6, vocab_size)      )
																		self.assertEqual(output.shape    ,     A_      )

																		__lowerCamelCase   :   Union[str, Any]       =torch.tensor(
																		    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]      )
																		self.assertTrue(torch.allclose(output[:, :3, :3]    ,     A_    ,     atol=1e-4      )      )




				@slow
				def        __lowercase	(  self	:Tuple      ):

											with torch.no_grad():
																		__lowerCamelCase   :   str       =EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )
																		model.eval()

																		__lowerCamelCase   :   List[str]       =torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
																		__lowerCamelCase   :   Optional[int]       =model(A_      )[0]
																		# compare the actual values for a slice.
																		__lowerCamelCase   :   Optional[int]       =torch.tensor(
																		    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]      )
																		self.assertTrue(torch.allclose(output[:, :3, :3]    ,     A_    ,     atol=1e-4      )      )











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
  )
