





import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				List[str]     ):
		assert isinstance(UpperCamelCase__       , UpperCamelCase__     )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_1", "col_2", "col_3"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""       , [False, True]     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				List[Any]     ):
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE__								=   JsonDatasetReader(UpperCamelCase__       , cache_dir=UpperCamelCase__       , keep_in_memory=UpperCamelCase__     ).read()
		_check_json_dataset(UpperCamelCase__       , UpperCamelCase__     )






@pytest.mark.parametrize(
    """features"""       , [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]       , )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				int       , UpperCamelCase__:				Tuple     ):
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		SCREAMING_SNAKE_CASE__								=   features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE__								=   (
		    Features({feature: Value(UpperCamelCase__     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE__								=   JsonDatasetReader(UpperCamelCase__       , features=UpperCamelCase__       , cache_dir=UpperCamelCase__     ).read()
		_check_json_dataset(UpperCamelCase__       , UpperCamelCase__     )






@pytest.mark.parametrize(
    """features"""       , [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]       , )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
		SCREAMING_SNAKE_CASE__								=   features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE__								=   (
		    Features({feature: Value(UpperCamelCase__     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE__								=   JsonDatasetReader(UpperCamelCase__       , features=UpperCamelCase__       , cache_dir=UpperCamelCase__     ).read()
		assert isinstance(UpperCamelCase__       , UpperCamelCase__     )
		assert dataset.num_rows == 2
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_3", "col_1", "col_2"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Union[str, Any]     ):
		# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
		SCREAMING_SNAKE_CASE__								=   {"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
		SCREAMING_SNAKE_CASE__								=   features.copy()
		SCREAMING_SNAKE_CASE__								=   (
		    Features({feature: Value(UpperCamelCase__     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   JsonDatasetReader(UpperCamelCase__       , features=UpperCamelCase__       , cache_dir=UpperCamelCase__     ).read()
		assert isinstance(UpperCamelCase__       , UpperCamelCase__     )
		assert dataset.num_rows == 2
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_2", "col_3", "col_1"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				List[str]     ):
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		SCREAMING_SNAKE_CASE__								=   JsonDatasetReader(UpperCamelCase__       , cache_dir=UpperCamelCase__       , split=UpperCamelCase__     ).read()
		_check_json_dataset(UpperCamelCase__       , UpperCamelCase__     )
		assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""       , [str, list]     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				int       , UpperCamelCase__:				List[Any]     ):
		if issubclass(UpperCamelCase__       , UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__								=   jsonl_path
		elif issubclass(UpperCamelCase__       , UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__								=   [jsonl_path]
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		SCREAMING_SNAKE_CASE__								=   JsonDatasetReader(UpperCamelCase__       , cache_dir=UpperCamelCase__     ).read()
		_check_json_dataset(UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Dict=("train",)     ):
		assert isinstance(UpperCamelCase__       , UpperCamelCase__     )
		for split in splits:
				SCREAMING_SNAKE_CASE__								=   dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""       , [False, True]     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				List[str]     ):
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE__								=   JsonDatasetReader({"""train""": jsonl_path}       , cache_dir=UpperCamelCase__       , keep_in_memory=UpperCamelCase__     ).read()
		_check_json_datasetdict(UpperCamelCase__       , UpperCamelCase__     )






@pytest.mark.parametrize(
    """features"""       , [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]       , )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		SCREAMING_SNAKE_CASE__								=   features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE__								=   (
		    Features({feature: Value(UpperCamelCase__     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE__								=   JsonDatasetReader({"""train""": jsonl_path}       , features=UpperCamelCase__       , cache_dir=UpperCamelCase__     ).read()
		_check_json_datasetdict(UpperCamelCase__       , UpperCamelCase__     )






@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]     ):
		if split:
				SCREAMING_SNAKE_CASE__								=   {split: jsonl_path}
		else:
				SCREAMING_SNAKE_CASE__								=   """train"""
				SCREAMING_SNAKE_CASE__								=   {"""train""": jsonl_path, """test""": jsonl_path}
		SCREAMING_SNAKE_CASE__								=   tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__								=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		SCREAMING_SNAKE_CASE__								=   JsonDatasetReader(UpperCamelCase__       , cache_dir=UpperCamelCase__     ).read()
		_check_json_datasetdict(UpperCamelCase__       , UpperCamelCase__       , splits=list(path.keys()     )     )
		assert all(dataset[split].split == split for split in path.keys()     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		return json.load(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		return [json.loads(UpperCamelCase__     ) for line in buffer]




class 	UpperCamelCase_							:


							@pytest.mark.parametrize("""lines, load_json_function"""				,					[(True, load_json_lines), (False, load_json)]		)
							def   _snake_case	(				self     :str				,					__A     :int				,					__A     :Optional[Any]				,					__A     :Dict		) -> Tuple:



									"""simple docstring"""



									with io.BytesIO() as buffer:
											JsonDatasetWriter(__A				,					__A				,					lines=__A		).write()
											buffer.seek(0		)
											SCREAMING_SNAKE_CASE__								=   load_json_function(__A		)
									assert isinstance(__A				,					__A		)
									assert isinstance(exported_content[0]				,					__A		)
									assert len(__A		) == 10


							@pytest.mark.parametrize(
							    """orient, container, keys, len_at"""				,					[
							        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
							        ("""split""", dict, {"""columns""", """data"""}, """data"""),
							        ("""index""", dict, set("""0123456789"""		), None),
							        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
							        ("""values""", list, None, None),
							        ("""table""", dict, {"""schema""", """data"""}, """data"""),
							    ]				,					)
							def   _snake_case	(				self     :Optional[int]				,					__A     :Union[str, Any]				,					__A     :Tuple				,					__A     :int				,					__A     :str				,					__A     :Optional[Any]		) -> Optional[Any]:



									"""simple docstring"""



									with io.BytesIO() as buffer:
											JsonDatasetWriter(__A				,					__A				,					lines=__A				,					orient=__A		).write()
											buffer.seek(0		)
											SCREAMING_SNAKE_CASE__								=   load_json(__A		)
									assert isinstance(__A				,					__A		)
									if keys:
											if container is dict:
													assert exported_content.keys() == keys
											else:
													assert exported_content[0].keys() == keys
									else:
											assert not hasattr(__A				,					"""keys"""		) and not hasattr(exported_content[0]				,					"""keys"""		)
									if len_at:
											assert len(exported_content[len_at]		) == 10
									else:
											assert len(__A		) == 10


							@pytest.mark.parametrize("""lines, load_json_function"""				,					[(True, load_json_lines), (False, load_json)]		)
							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Tuple				,					__A     :Dict				,					__A     :Any		) -> int:



									"""simple docstring"""



									with io.BytesIO() as buffer:
											JsonDatasetWriter(__A				,					__A				,					lines=__A				,					num_proc=2		).write()
											buffer.seek(0		)
											SCREAMING_SNAKE_CASE__								=   load_json_function(__A		)
									assert isinstance(__A				,					__A		)
									assert isinstance(exported_content[0]				,					__A		)
									assert len(__A		) == 10


							@pytest.mark.parametrize(
							    """orient, container, keys, len_at"""				,					[
							        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
							        ("""split""", dict, {"""columns""", """data"""}, """data"""),
							        ("""index""", dict, set("""0123456789"""		), None),
							        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
							        ("""values""", list, None, None),
							        ("""table""", dict, {"""schema""", """data"""}, """data"""),
							    ]				,					)
							def   _snake_case	(				self     :int				,					__A     :List[str]				,					__A     :List[Any]				,					__A     :List[Any]				,					__A     :str				,					__A     :List[Any]		) -> Optional[int]:



									"""simple docstring"""



									with io.BytesIO() as buffer:
											JsonDatasetWriter(__A				,					__A				,					lines=__A				,					orient=__A				,					num_proc=2		).write()
											buffer.seek(0		)
											SCREAMING_SNAKE_CASE__								=   load_json(__A		)
									assert isinstance(__A				,					__A		)
									if keys:
											if container is dict:
													assert exported_content.keys() == keys
											else:
													assert exported_content[0].keys() == keys
									else:
											assert not hasattr(__A				,					"""keys"""		) and not hasattr(exported_content[0]				,					"""keys"""		)
									if len_at:
											assert len(exported_content[len_at]		) == 10
									else:
											assert len(__A		) == 10


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Union[str, Any]		) -> Optional[Any]:



									"""simple docstring"""



									with pytest.raises(__A		):
											with io.BytesIO() as buffer:
													JsonDatasetWriter(__A				,					__A				,					num_proc=0		)


							@pytest.mark.parametrize("""compression, extension"""				,					[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]		)
							def   _snake_case	(				self     :Dict				,					__A     :List[Any]				,					__A     :Any				,					__A     :str				,					__A     :Tuple				,					__A     :Union[str, Any]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   tmp_path_factory.mktemp("""data"""		) / f'''test.json.{extension}'''
									SCREAMING_SNAKE_CASE__								=   str(shared_datadir / f'''test_file.json.{extension}'''		)
									JsonDatasetWriter(__A				,					__A				,					compression=__A		).write()

									with fsspec.open(__A				,					"""rb"""				,					compression="""infer"""		) as f:
											SCREAMING_SNAKE_CASE__								=   f.read()
									with fsspec.open(__A				,					"""rb"""				,					compression="""infer"""		) as f:
											SCREAMING_SNAKE_CASE__								=   f.read()
									assert exported_content == original_content






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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   SwinConfig.from_pretrained(
		    """microsoft/swin-tiny-patch4-window7-224"""       , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     )
		SCREAMING_SNAKE_CASE__								=   MaskFormerConfig(backbone_config=UpperCamelCase__     )

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

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

		return config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Optional[int]     ):
		SCREAMING_SNAKE_CASE__								=   dct.pop(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   val






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Union[str, 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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[Any]     ):
		# 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 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__								=   Image.open(requests.get(UpperCamelCase__       , stream=UpperCamelCase__     ).raw     )
		return im






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				bool = False     ):
		SCREAMING_SNAKE_CASE__								=   get_maskformer_config(UpperCamelCase__     )

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

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

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

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

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

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

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

		# verify results
		SCREAMING_SNAKE_CASE__								=   prepare_img()
		if "vistas" in model_name:
				SCREAMING_SNAKE_CASE__								=   65
		elif "cityscapes" in model_name:
				SCREAMING_SNAKE_CASE__								=   65_535
		else:
				SCREAMING_SNAKE_CASE__								=   255
		SCREAMING_SNAKE_CASE__								=   True if """ade""" in model_name else False
		SCREAMING_SNAKE_CASE__								=   MaskFormerImageProcessor(ignore_index=UpperCamelCase__       , reduce_labels=UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   image_processor(UpperCamelCase__       , return_tensors="""pt"""     )

		SCREAMING_SNAKE_CASE__								=   model(**UpperCamelCase__     )

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

		if model_name == "maskformer-swin-tiny-ade":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]     )
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       , UpperCamelCase__       , 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(UpperCamelCase__     ).mkdir(exist_ok=UpperCamelCase__     )
				model.save_pretrained(UpperCamelCase__     )
				image_processor.save_pretrained(UpperCamelCase__     )

		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
	)