





import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
	import jax.numpy as jnp




class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def __get__(				self     :Any				,					__A     :Dict				,					__A     :List[Any]=None		) -> List[Any]:



									"""simple docstring"""



									if obj is None:
											return self
									if self.fget is None:
											raise AttributeError("""unreadable attribute"""		)
									SCREAMING_SNAKE_CASE__								=   """__cached_""" + self.fget.__name__
									SCREAMING_SNAKE_CASE__								=   getattr(__A				,					__A				,					__A		)
									if cached is None:
											SCREAMING_SNAKE_CASE__								=   self.fget(__A		)
											setattr(__A				,					__A				,					__A		)
									return cached






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   val.lower()
		if val in {"y", "yes", "t", "true", "on", "1"}:
				return 1
		if val in {"n", "no", "f", "false", "off", "0"}:
				return 0
		raise ValueError(f'''invalid truth value {val!r}'''     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int     ):
		if is_torch_fx_proxy(UpperCamelCase__     ):
				return True
		if is_torch_available():
				import torch

				if isinstance(UpperCamelCase__       , torch.Tensor     ):
						return True
		if is_tf_available():
				import tensorflow as tf

				if isinstance(UpperCamelCase__       , tf.Tensor     ):
						return True

		if is_flax_available():
				import jax.numpy as jnp
				from jax.core import Tracer

				if isinstance(UpperCamelCase__       , (jnp.ndarray, Tracer)     ):
						return True

		return isinstance(UpperCamelCase__       , np.ndarray     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		return isinstance(UpperCamelCase__       , np.ndarray     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		return _is_numpy(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict     ):
		import torch

		return isinstance(UpperCamelCase__       , torch.Tensor     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		return False if not is_torch_available() else _is_torch(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict     ):
		import torch

		return isinstance(UpperCamelCase__       , torch.device     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		return False if not is_torch_available() else _is_torch_device(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		import torch

		if isinstance(UpperCamelCase__       , UpperCamelCase__     ):
				if hasattr(UpperCamelCase__       , UpperCamelCase__     ):
						SCREAMING_SNAKE_CASE__								=   getattr(UpperCamelCase__       , UpperCamelCase__     )
				else:
						return False
		return isinstance(UpperCamelCase__       , torch.dtype     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		return False if not is_torch_available() else _is_torch_dtype(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):
		import tensorflow as tf

		return isinstance(UpperCamelCase__       , tf.Tensor     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int     ):
		return False if not is_tf_available() else _is_tensorflow(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		import tensorflow as tf

		# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
		if hasattr(UpperCamelCase__       , """is_symbolic_tensor"""     ):
				return tf.is_symbolic_tensor(UpperCamelCase__     )
		return type(UpperCamelCase__     ) == tf.Tensor






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):
		return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any     ):
		import jax.numpy as jnp  # noqa: F811

		return isinstance(UpperCamelCase__       , jnp.ndarray     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		return False if not is_flax_available() else _is_jax(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[int]     ):
		if isinstance(UpperCamelCase__       , (dict, UserDict)     ):
				return {k: to_py_obj(UpperCamelCase__     ) for k, v in obj.items()}
		elif isinstance(UpperCamelCase__       , (list, tuple)     ):
				return [to_py_obj(UpperCamelCase__     ) for o in obj]
		elif is_tf_tensor(UpperCamelCase__     ):
				return obj.numpy().tolist()
		elif is_torch_tensor(UpperCamelCase__     ):
				return obj.detach().cpu().tolist()
		elif is_jax_tensor(UpperCamelCase__     ):
				return np.asarray(UpperCamelCase__     ).tolist()
		elif isinstance(UpperCamelCase__       , (np.ndarray, np.number)     ):  # tolist also works on 0d np arrays
				return obj.tolist()
		else:
				return obj






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		if isinstance(UpperCamelCase__       , (dict, UserDict)     ):
				return {k: to_numpy(UpperCamelCase__     ) for k, v in obj.items()}
		elif isinstance(UpperCamelCase__       , (list, tuple)     ):
				return np.array(UpperCamelCase__     )
		elif is_tf_tensor(UpperCamelCase__     ):
				return obj.numpy()
		elif is_torch_tensor(UpperCamelCase__     ):
				return obj.detach().cpu().numpy()
		elif is_jax_tensor(UpperCamelCase__     ):
				return np.asarray(UpperCamelCase__     )
		else:
				return obj




class 	UpperCamelCase_							(  UpperCamelCase__     ):


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   fields(self		)

									# Safety and consistency checks
									if not len(__A		):
											raise ValueError(f'''{self.__class__.__name__} has no fields.'''		)
									if not all(field.default is None for field in class_fields[1:]		):
											raise ValueError(f'''{self.__class__.__name__} should not have more than one required field.'''		)

									SCREAMING_SNAKE_CASE__								=   getattr(self				,					class_fields[0].name		)
									SCREAMING_SNAKE_CASE__								=   all(getattr(self				,					field.name		) is None for field in class_fields[1:]		)

									if other_fields_are_none and not is_tensor(__A		):
											if isinstance(__A				,					__A		):
													SCREAMING_SNAKE_CASE__								=   first_field.items()
													SCREAMING_SNAKE_CASE__								=   True
											else:
													try:
															SCREAMING_SNAKE_CASE__								=   iter(__A		)
															SCREAMING_SNAKE_CASE__								=   True
													except TypeError:
															SCREAMING_SNAKE_CASE__								=   False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
											if first_field_iterator:
													for idx, element in enumerate(__A		):
															if (
															    not isinstance(__A				,					(list, tuple)		)
															    or not len(__A		) == 2
															    or not isinstance(element[0]				,					__A		)
															):
																	if idx == 0:
																			# If we do not have an iterator of key/values, set it as attribute
																			SCREAMING_SNAKE_CASE__								=   first_field
																	else:
																			# If we have a mixed iterator, raise an error
																			raise ValueError(
																			    f'''Cannot set key/value for {element}. It needs to be a tuple (key, value).'''		)
																	break
															setattr(self				,					element[0]				,					element[1]		)
															if element[1] is not None:
																	SCREAMING_SNAKE_CASE__								=   element[1]
											elif first_field is not None:
													SCREAMING_SNAKE_CASE__								=   first_field
									else:
											for field in class_fields:
													SCREAMING_SNAKE_CASE__								=   getattr(self				,					field.name		)
													if v is not None:
															SCREAMING_SNAKE_CASE__								=   v


							def __delitem__(				self     :List[str]				,					*__A     :Tuple				,					**__A     :str		) -> List[str]:



									"""simple docstring"""



									raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''		)


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



									"""simple docstring"""



									raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''		)


							def   _snake_case	(				self     :Union[str, Any]				,					*__A     :List[Any]				,					**__A     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''		)


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



									"""simple docstring"""



									raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''		)


							def __getitem__(				self     :List[str]				,					__A     :List[str]		) -> str:



									"""simple docstring"""



									if isinstance(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   dict(self.items()		)
											return inner_dict[k]
									else:
											return self.to_tuple()[k]


							def __setattr__(				self     :List[str]				,					__A     :Dict				,					__A     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									if name in self.keys() and value is not None:
											# Don't call self.__setitem__ to avoid recursion errors
											super().__setitem__(__A				,					__A		)
									super().__setattr__(__A				,					__A		)


							def __setitem__(				self     :Optional[int]				,					__A     :int				,					__A     :Any		) -> Tuple:



									"""simple docstring"""



									super().__setitem__(__A				,					__A		)
									# Don't call self.__setattr__ to avoid recursion errors
									super().__setattr__(__A				,					__A		)


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



									"""simple docstring"""



									return tuple(self[k] for k in self.keys()		)




class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__     ):


							@classmethod
							def   _snake_case	(				cls     :Union[str, Any]				,					__A     :str		) -> str:



									"""simple docstring"""



									raise ValueError(
									    f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()		)}'''		)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "longest"
							lowerCamelCase_							     =       "max_length"
							lowerCamelCase_							     =       "do_not_pad"




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "pt"
							lowerCamelCase_							     =       "tf"
							lowerCamelCase_							     =       "np"
							lowerCamelCase_							     =       "jax"




class 	UpperCamelCase_							:


							def __init__(				self     :Union[str, Any]				,					__A     :List[ContextManager]		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   context_managers
									SCREAMING_SNAKE_CASE__								=   ExitStack()


							def __enter__(				self     :Any		) -> Any:



									"""simple docstring"""



									for context_manager in self.context_managers:
											self.stack.enter_context(__A		)


							def __exit__(				self     :Any				,					*__A     :Union[str, Any]				,					**__A     :Dict		) -> Tuple:



									"""simple docstring"""



									self.stack.__exit__(*__A				,					**__A		)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):
		SCREAMING_SNAKE_CASE__								=   infer_framework(UpperCamelCase__     )
		if framework == "tf":
				SCREAMING_SNAKE_CASE__								=   inspect.signature(model_class.call     )  # TensorFlow models
		elif framework == "pt":
				SCREAMING_SNAKE_CASE__								=   inspect.signature(model_class.forward     )  # PyTorch models
		else:
				SCREAMING_SNAKE_CASE__								=   inspect.signature(model_class.__call__     )  # Flax models

		for p in signature.parameters:
				if p == "return_loss" and signature.parameters[p].default is True:
						return True

		return False






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   model_class.__name__
		SCREAMING_SNAKE_CASE__								=   infer_framework(UpperCamelCase__     )
		if framework == "tf":
				SCREAMING_SNAKE_CASE__								=   inspect.signature(model_class.call     )  # TensorFlow models
		elif framework == "pt":
				SCREAMING_SNAKE_CASE__								=   inspect.signature(model_class.forward     )  # PyTorch models
		else:
				SCREAMING_SNAKE_CASE__								=   inspect.signature(model_class.__call__     )  # Flax models

		if "QuestionAnswering" in model_name:
				return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
		else:
				return [p for p in signature.parameters if "label" in p]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				MutableMapping       , UpperCamelCase__:				str = ""       , UpperCamelCase__:				str = "."     ):

		def _flatten_dict(UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Tuple=""       , UpperCamelCase__:				Optional[Any]="."     ):
				for k, v in d.items():
						SCREAMING_SNAKE_CASE__								=   str(UpperCamelCase__     ) + delimiter + str(UpperCamelCase__     ) if parent_key else k
						if v and isinstance(UpperCamelCase__       , UpperCamelCase__     ):
								yield from flatten_dict(UpperCamelCase__       , UpperCamelCase__       , delimiter=UpperCamelCase__     ).items()
						else:
								yield key, v

		return dict(_flatten_dict(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )     )






@contextmanager
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				bool = False     ):
		if use_temp_dir:
				with tempfile.TemporaryDirectory() as tmp_dir:
						yield tmp_dir
		else:
				yield working_dir






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[int]=None     ):
		if is_numpy_array(UpperCamelCase__     ):
				return np.transpose(UpperCamelCase__       , axes=UpperCamelCase__     )
		elif is_torch_tensor(UpperCamelCase__     ):
				return array.T if axes is None else array.permute(*UpperCamelCase__     )
		elif is_tf_tensor(UpperCamelCase__     ):
				import tensorflow as tf

				return tf.transpose(UpperCamelCase__       , perm=UpperCamelCase__     )
		elif is_jax_tensor(UpperCamelCase__     ):
				return jnp.transpose(UpperCamelCase__       , axes=UpperCamelCase__     )
		else:
				raise ValueError(f'''Type not supported for transpose: {type(UpperCamelCase__     )}.'''     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				str     ):
		if is_numpy_array(UpperCamelCase__     ):
				return np.reshape(UpperCamelCase__       , UpperCamelCase__     )
		elif is_torch_tensor(UpperCamelCase__     ):
				return array.reshape(*UpperCamelCase__     )
		elif is_tf_tensor(UpperCamelCase__     ):
				import tensorflow as tf

				return tf.reshape(UpperCamelCase__       , UpperCamelCase__     )
		elif is_jax_tensor(UpperCamelCase__     ):
				return jnp.reshape(UpperCamelCase__       , UpperCamelCase__     )
		else:
				raise ValueError(f'''Type not supported for reshape: {type(UpperCamelCase__     )}.'''     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Optional[Any]=None     ):
		if is_numpy_array(UpperCamelCase__     ):
				return np.squeeze(UpperCamelCase__       , axis=UpperCamelCase__     )
		elif is_torch_tensor(UpperCamelCase__     ):
				return array.squeeze() if axis is None else array.squeeze(dim=UpperCamelCase__     )
		elif is_tf_tensor(UpperCamelCase__     ):
				import tensorflow as tf

				return tf.squeeze(UpperCamelCase__       , axis=UpperCamelCase__     )
		elif is_jax_tensor(UpperCamelCase__     ):
				return jnp.squeeze(UpperCamelCase__       , axis=UpperCamelCase__     )
		else:
				raise ValueError(f'''Type not supported for squeeze: {type(UpperCamelCase__     )}.'''     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				str     ):
		if is_numpy_array(UpperCamelCase__     ):
				return np.expand_dims(UpperCamelCase__       , UpperCamelCase__     )
		elif is_torch_tensor(UpperCamelCase__     ):
				return array.unsqueeze(dim=UpperCamelCase__     )
		elif is_tf_tensor(UpperCamelCase__     ):
				import tensorflow as tf

				return tf.expand_dims(UpperCamelCase__       , axis=UpperCamelCase__     )
		elif is_jax_tensor(UpperCamelCase__     ):
				return jnp.expand_dims(UpperCamelCase__       , axis=UpperCamelCase__     )
		else:
				raise ValueError(f'''Type not supported for expand_dims: {type(UpperCamelCase__     )}.'''     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		if is_numpy_array(UpperCamelCase__     ):
				return np.size(UpperCamelCase__     )
		elif is_torch_tensor(UpperCamelCase__     ):
				return array.numel()
		elif is_tf_tensor(UpperCamelCase__     ):
				import tensorflow as tf

				return tf.size(UpperCamelCase__     )
		elif is_jax_tensor(UpperCamelCase__     ):
				return array.size
		else:
				raise ValueError(f'''Type not supported for expand_dims: {type(UpperCamelCase__     )}.'''     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]     ):
		for key, value in auto_map.items():
				if isinstance(UpperCamelCase__       , (tuple, list)     ):
						SCREAMING_SNAKE_CASE__								=   [f'''{repo_id}--{v}''' if (v is not None and """--""" not in v) else v for v in value]
				elif value is not None and "--" not in value:
						SCREAMING_SNAKE_CASE__								=   f'''{repo_id}--{value}'''

		return auto_map






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict     ):
		for base_class in inspect.getmro(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__								=   base_class.__module__
				SCREAMING_SNAKE_CASE__								=   base_class.__name__
				if module.startswith("""tensorflow"""     ) or module.startswith("""keras"""     ) or name == "TFPreTrainedModel":
						return "tf"
				elif module.startswith("""torch"""     ) or name == "PreTrainedModel":
						return "pt"
				elif module.startswith("""flax"""     ) or module.startswith("""jax"""     ) or name == "FlaxPreTrainedModel":
						return "flax"
		else:
				raise TypeError(f'''Could not infer framework from class {model_class}.'''     )






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
	)