



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__    (		__snake_case					):



  """simple docstring"""






  def __get__(				self:			List[str]				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Union[str, Any]=None   ) -> List[str]:




    '''simple docstring'''




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






def       __lowerCAmelCase      (						A_   : Any					)    ->  List[Any]:
  __UpperCAmelCase						    =       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       __lowerCAmelCase      (						A_   : List[Any]					)    ->  Dict:
  if is_torch_fx_proxy(_lowerCAmelCase					):
    return True
  if is_torch_available():
    import torch

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

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

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

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

  return isinstance(_lowerCAmelCase						,	np.ndarray					)




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  Tuple:
  return isinstance(_lowerCAmelCase						,	np.ndarray					)




def       __lowerCAmelCase      (						A_   : Union[str, Any]					)    ->  Tuple:
  return _is_numpy(_lowerCAmelCase					)




def       __lowerCAmelCase      (						A_   : str					)    ->  Any:
  import torch

  return isinstance(_lowerCAmelCase						,	torch.Tensor					)




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  Tuple:
  return False if not is_torch_available() else _is_torch(_lowerCAmelCase					)




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  Optional[int]:
  import torch

  return isinstance(_lowerCAmelCase						,	torch.device					)




def       __lowerCAmelCase      (						A_   : str					)    ->  Any:
  return False if not is_torch_available() else _is_torch_device(_lowerCAmelCase					)




def       __lowerCAmelCase      (						A_   : Union[str, Any]					)    ->  Union[str, Any]:
  import torch

  if isinstance(_lowerCAmelCase						,	_lowerCAmelCase					):
    if hasattr(_lowerCAmelCase						,	_lowerCAmelCase					):
      __UpperCAmelCase						    =       getattr(_lowerCAmelCase						,	_lowerCAmelCase					)
    else:
      return False
  return isinstance(_lowerCAmelCase						,	torch.dtype					)




def       __lowerCAmelCase      (						A_   : Any					)    ->  str:
  return False if not is_torch_available() else _is_torch_dtype(_lowerCAmelCase					)




def       __lowerCAmelCase      (						A_   : int					)    ->  str:
  import tensorflow as tf

  return isinstance(_lowerCAmelCase						,	tf.Tensor					)




def       __lowerCAmelCase      (						A_   : Optional[Any]					)    ->  Optional[int]:
  return False if not is_tf_available() else _is_tensorflow(_lowerCAmelCase					)




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  Optional[int]:
  import tensorflow as tf

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




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  List[str]:
  return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCAmelCase					)




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  List[str]:
  import jax.numpy as jnp  # noqa: F811

  return isinstance(_lowerCAmelCase						,	jnp.ndarray					)




def       __lowerCAmelCase      (						A_   : List[str]					)    ->  Union[str, Any]:
  return False if not is_flax_available() else _is_jax(_lowerCAmelCase					)




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




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  Dict:
  if isinstance(_lowerCAmelCase						,	(dict, UserDict)					):
    return {k: to_numpy(_lowerCAmelCase					) for k, v in obj.items()}
  elif isinstance(_lowerCAmelCase						,	(list, tuple)					):
    return np.array(_lowerCAmelCase					)
  elif is_tf_tensor(_lowerCAmelCase					):
    return obj.numpy()
  elif is_torch_tensor(_lowerCAmelCase					):
    return obj.detach().cpu().numpy()
  elif is_jax_tensor(_lowerCAmelCase					):
    return np.asarray(_lowerCAmelCase					)
  else:
    return obj


class 			UpperCAmelCase__    (		__snake_case					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       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.'''   )

    __UpperCAmelCase						    =       getattr(self				,				class_fields[0].name   )
    __UpperCAmelCase						    =       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_   ):
        __UpperCAmelCase						    =       first_field.items()
        __UpperCAmelCase						    =       True
      else:
        try:
          __UpperCAmelCase						    =       iter(A_   )
          __UpperCAmelCase						    =       True
        except TypeError:
          __UpperCAmelCase						    =       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
              __UpperCAmelCase						    =       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:
            __UpperCAmelCase						    =       element[1]
      elif first_field is not None:
        __UpperCAmelCase						    =       first_field
    else:
      for field in class_fields:
        __UpperCAmelCase						    =       getattr(self				,				field.name   )
        if v is not None:
          __UpperCAmelCase						    =       v






  def __delitem__(				self:			Optional[Any]				,				*__lowerCAmelCase:			Union[str, Any]				,				**__lowerCAmelCase:			str   ) -> Union[str, Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Dict				,				*__lowerCAmelCase:			Optional[int]				,				**__lowerCAmelCase:			int   ) -> Dict:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Optional[int]				,				*__lowerCAmelCase:			int				,				**__lowerCAmelCase:			List[Any]   ) -> List[Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Any				,				*__lowerCAmelCase:			Dict				,				**__lowerCAmelCase:			List[Any]   ) -> Union[str, Any]:




    '''simple docstring'''




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






  def __getitem__(				self:			Optional[int]				,				__lowerCAmelCase:			Any   ) -> Dict:




    '''simple docstring'''




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






  def __setattr__(				self:			int				,				__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			Optional[Any]   ) -> Tuple:




    '''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:			Any				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Dict   ) -> int:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			int   ) -> str:




    '''simple docstring'''




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






class 			UpperCAmelCase__    (		__snake_case		,	__snake_case					):



  """simple docstring"""






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Dict				,				__lowerCAmelCase:			Tuple   ) -> Dict:




    '''simple docstring'''




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






class 			UpperCAmelCase__    (		__snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	str    		= 'longest'
  lowerCAmelCase__		:	Any    		= 'max_length'
  lowerCAmelCase__		:	str    		= 'do_not_pad'






class 			UpperCAmelCase__    (		__snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Dict    		= 'pt'
  lowerCAmelCase__		:	List[Any]    		= 'tf'
  lowerCAmelCase__		:	List[str]    		= 'np'
  lowerCAmelCase__		:	List[Any]    		= 'jax'






class 			UpperCAmelCase__    :



  """simple docstring"""






  def __init__(				self:			str				,				__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       context_managers
    __UpperCAmelCase						    =       ExitStack()






  def __enter__(				self:			str   ) -> int:




    '''simple docstring'''




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






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




    '''simple docstring'''




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






def       __lowerCAmelCase      (						A_   : Tuple					)    ->  List[Any]:
  __UpperCAmelCase						    =       infer_framework(_lowerCAmelCase					)
  if framework == "tf":
    __UpperCAmelCase						    =       inspect.signature(model_class.call					)  # TensorFlow models
  elif framework == "pt":
    __UpperCAmelCase						    =       inspect.signature(model_class.forward					)  # PyTorch models
  else:
    __UpperCAmelCase						    =       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       __lowerCAmelCase      (						A_   : Any					)    ->  int:
  __UpperCAmelCase						    =       model_class.__name__
  __UpperCAmelCase						    =       infer_framework(_lowerCAmelCase					)
  if framework == "tf":
    __UpperCAmelCase						    =       inspect.signature(model_class.call					)  # TensorFlow models
  elif framework == "pt":
    __UpperCAmelCase						    =       inspect.signature(model_class.forward					)  # PyTorch models
  else:
    __UpperCAmelCase						    =       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       __lowerCAmelCase      (						A_   : List[Any]						,	A_   : List[Any] = ""						,	A_   : Optional[int] = "."					)    ->  Optional[int]:

  def _flatten_dict(A_   : Any						,	A_   : int=""						,	A_   : str="."					):
    for k, v in d.items():
      __UpperCAmelCase						    =       str(_lowerCAmelCase					) + delimiter + str(_lowerCAmelCase					) if parent_key else k
      if v and isinstance(_lowerCAmelCase						,	_lowerCAmelCase					):
        yield from flatten_dict(_lowerCAmelCase						,	_lowerCAmelCase						,	delimiter=_lowerCAmelCase					).items()
      else:
        yield key, v

  return dict(_flatten_dict(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase					)					)




@contextmanager
def       __lowerCAmelCase      (						A_   : int						,	A_   : Optional[int] = False					)    ->  List[Any]:
  if use_temp_dir:
    with tempfile.TemporaryDirectory() as tmp_dir:
      yield tmp_dir
  else:
    yield working_dir




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : Optional[int]=None					)    ->  Union[str, Any]:
  if is_numpy_array(_lowerCAmelCase					):
    return np.transpose(_lowerCAmelCase						,	axes=_lowerCAmelCase					)
  elif is_torch_tensor(_lowerCAmelCase					):
    return array.T if axes is None else array.permute(*_lowerCAmelCase					)
  elif is_tf_tensor(_lowerCAmelCase					):
    import tensorflow as tf

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




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Tuple					)    ->  Any:
  if is_numpy_array(_lowerCAmelCase					):
    return np.reshape(_lowerCAmelCase						,	_lowerCAmelCase					)
  elif is_torch_tensor(_lowerCAmelCase					):
    return array.reshape(*_lowerCAmelCase					)
  elif is_tf_tensor(_lowerCAmelCase					):
    import tensorflow as tf

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




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Any=None					)    ->  str:
  if is_numpy_array(_lowerCAmelCase					):
    return np.squeeze(_lowerCAmelCase						,	axis=_lowerCAmelCase					)
  elif is_torch_tensor(_lowerCAmelCase					):
    return array.squeeze() if axis is None else array.squeeze(dim=_lowerCAmelCase					)
  elif is_tf_tensor(_lowerCAmelCase					):
    import tensorflow as tf

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




def       __lowerCAmelCase      (						A_   : int						,	A_   : Tuple					)    ->  Any:
  if is_numpy_array(_lowerCAmelCase					):
    return np.expand_dims(_lowerCAmelCase						,	_lowerCAmelCase					)
  elif is_torch_tensor(_lowerCAmelCase					):
    return array.unsqueeze(dim=_lowerCAmelCase					)
  elif is_tf_tensor(_lowerCAmelCase					):
    import tensorflow as tf

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




def       __lowerCAmelCase      (						A_   : Any					)    ->  List[str]:
  if is_numpy_array(_lowerCAmelCase					):
    return np.size(_lowerCAmelCase					)
  elif is_torch_tensor(_lowerCAmelCase					):
    return array.numel()
  elif is_tf_tensor(_lowerCAmelCase					):
    import tensorflow as tf

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




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : Optional[int]					)    ->  Any:
  for key, value in auto_map.items():
    if isinstance(_lowerCAmelCase						,	(tuple, list)					):
      __UpperCAmelCase						    =       [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:
      __UpperCAmelCase						    =       F'''{repo_id}--{value}'''

  return auto_map





def       __lowerCAmelCase      (						A_   : Tuple					)    ->  int:
  for base_class in inspect.getmro(_lowerCAmelCase					):
    __UpperCAmelCase						    =       base_class.__module__
    __UpperCAmelCase						    =       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 requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  Any:
  __UpperCAmelCase						    =       3_84
  __UpperCAmelCase						    =       7
  if "tiny" in model_name:
    __UpperCAmelCase						    =       96
    __UpperCAmelCase						    =       (2, 2, 6, 2)
    __UpperCAmelCase						    =       (3, 6, 12, 24)
  elif "small" in model_name:
    __UpperCAmelCase						    =       96
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (3, 6, 12, 24)
  elif "base" in model_name:
    __UpperCAmelCase						    =       1_28
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (4, 8, 16, 32)
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       5_12
  elif "large" in model_name:
    __UpperCAmelCase						    =       1_92
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (6, 12, 24, 48)
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       7_68

  # set label information
  __UpperCAmelCase						    =       1_50
  __UpperCAmelCase						    =       "huggingface/label-files"
  __UpperCAmelCase						    =       "ade20k-id2label.json"
  __UpperCAmelCase						    =       json.load(open(hf_hub_download(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(A_					): v for k, v in idalabel.items()}
  __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}

  __UpperCAmelCase						    =       SwinConfig(
      embed_dim=A_						,	depths=A_						,	num_heads=A_						,	window_size=A_						,	out_features=["stage1", "stage2", "stage3", "stage4"]						,	)
  __UpperCAmelCase						    =       UperNetConfig(
      backbone_config=A_						,	auxiliary_in_channels=A_						,	num_labels=A_						,	idalabel=A_						,	labelaid=A_						,	)

  return config




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       []

  # fmt: off
  # stem
  rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")					)
  rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")					)
  rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")					)
  rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.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.stages.{i}.blocks.{j}.norm1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')					)
      rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')					)

    if i < 3:
      rename_keys.append((F'''backbone.stages.{i}.downsample.reduction.weight''', F'''backbone.encoder.layers.{i}.downsample.reduction.weight''')					)
      rename_keys.append((F'''backbone.stages.{i}.downsample.norm.weight''', F'''backbone.encoder.layers.{i}.downsample.norm.weight''')					)
      rename_keys.append((F'''backbone.stages.{i}.downsample.norm.bias''', F'''backbone.encoder.layers.{i}.downsample.norm.bias''')					)
    rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')					)
    rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')					)

  # decode head
  rename_keys.extend(
      [
          ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
          ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
          ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
          ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
      ]					)
  # fmt: on

  return rename_keys




def       __lowerCAmelCase      (						A_   : List[Any]						,	A_   : List[str]						,	A_   : Optional[Any]					)    ->  List[str]:
  __UpperCAmelCase						    =       dct.pop(A_					)
  __UpperCAmelCase						    =       val




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Dict					)    ->  Tuple:
  __UpperCAmelCase						    =       [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
  for i in range(len(backbone_config.depths					)					):
    __UpperCAmelCase						    =       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)
      __UpperCAmelCase						    =       state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''					)
      __UpperCAmelCase						    =       state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''					)
      # next, add query, keys and values (in that order) to the state dict
      __UpperCAmelCase						    =       in_proj_weight[:dim, :]
      __UpperCAmelCase						    =       in_proj_bias[: dim]
      __UpperCAmelCase						    =       in_proj_weight[
          dim : dim * 2, :
      ]
      __UpperCAmelCase						    =       in_proj_bias[
          dim : dim * 2
      ]
      __UpperCAmelCase						    =       in_proj_weight[
          -dim :, :
      ]
      __UpperCAmelCase						    =       in_proj_bias[-dim :]
      # fmt: on




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  int:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       x.shape
  __UpperCAmelCase						    =       x.reshape(A_						,	4						,	in_channel // 4					)
  __UpperCAmelCase						    =       x[:, [0, 2, 1, 3], :].transpose(1						,	2					).reshape(A_						,	A_					)
  return x




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  int:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       x.shape
  __UpperCAmelCase						    =       x.reshape(A_						,	in_channel // 4						,	4					)
  __UpperCAmelCase						    =       x[:, :, [0, 2, 1, 3]].transpose(1						,	2					).reshape(A_						,	A_					)

  return x




def       __lowerCAmelCase      (						A_   : Union[str, Any]					)    ->  str:
  __UpperCAmelCase						    =       x.shape[0]
  __UpperCAmelCase						    =       x.reshape(4						,	in_channel // 4					)
  __UpperCAmelCase						    =       x[[0, 2, 1, 3], :].transpose(0						,	1					).reshape(A_					)
  return x




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  str:
  __UpperCAmelCase						    =       x.shape[0]
  __UpperCAmelCase						    =       x.reshape(in_channel // 4						,	4					)
  __UpperCAmelCase						    =       x[:, [0, 2, 1, 3]].transpose(0						,	1					).reshape(A_					)
  return x




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str						,	A_   : Any					)    ->  Dict:
  __UpperCAmelCase						    =       {
      "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
      "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
      "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
      "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
  }
  __UpperCAmelCase						    =       model_name_to_url[model_name]
  __UpperCAmelCase						    =       torch.hub.load_state_dict_from_url(A_						,	map_location="cpu"						,	file_name=A_					)[
      "state_dict"
  ]

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

  __UpperCAmelCase						    =       get_upernet_config(A_					)
  __UpperCAmelCase						    =       UperNetForSemanticSegmentation(A_					)
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    __UpperCAmelCase						    =       state_dict.pop(A_					)
    if "bn" in key:
      __UpperCAmelCase						    =       key.replace("bn"						,	"batch_norm"					)
    __UpperCAmelCase						    =       val

  # rename keys
  __UpperCAmelCase						    =       create_rename_keys(A_					)
  for src, dest in rename_keys:
    rename_key(A_						,	A_						,	A_					)
  read_in_q_k_v(A_						,	config.backbone_config					)

  # fix downsample parameters
  for key, value in state_dict.items():
    if "downsample" in key:
      if "reduction" in key:
        __UpperCAmelCase						    =       reverse_correct_unfold_reduction_order(A_					)
      if "norm" in key:
        __UpperCAmelCase						    =       reverse_correct_unfold_norm_order(A_					)

  model.load_state_dict(A_					)

  # verify on image
  __UpperCAmelCase						    =       "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					).convert("RGB"					)

  __UpperCAmelCase						    =       SegformerImageProcessor()
  __UpperCAmelCase						    =       processor(A_						,	return_tensors="pt"					).pixel_values

  with torch.no_grad():
    __UpperCAmelCase						    =       model(A_					)
    __UpperCAmelCase						    =       outputs.logits

  print(logits.shape					)
  print("First values of logits:"						,	logits[0, 0, :3, :3]					)
  # assert values
  if model_name == "upernet-swin-tiny":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]					)
  elif model_name == "upernet-swin-small":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]					)
  elif model_name == "upernet-swin-base":
    __UpperCAmelCase						    =       torch.tensor(
        [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]					)
  elif model_name == "upernet-swin-large":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]					)
  print("Logits:"						,	outputs.logits[0, 0, :3, :3]					)
  assert torch.allclose(outputs.logits[0, 0, :3, :3]						,	A_						,	atol=1e-4					)
  print("Looks ok!"					)

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
    model.save_pretrained(A_					)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''					)
    processor.save_pretrained(A_					)

  if push_to_hub:
    print(F'''Pushing model and processor for {model_name} to hub'''					)
    model.push_to_hub(F'''openmmlab/{model_name}'''					)
    processor.push_to_hub(F'''openmmlab/{model_name}'''					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""upernet-swin-tiny""",
           type=str,
           choices=[F"upernet-swin-{size}" for size in ["""tiny""", """small""", """base""", """large"""]],
           help="""Name of the Swin + UperNet model you'd like to convert.""",
       )
       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_							=      parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



