

from abc import ABC, abstractmethod
from typing import List, Optional

class    _lowercase				(					_SCREAMING_SNAKE_CASE			):

   def __init__(				self	:    Tuple       ) ->			Union[str, Any]:
         """simple docstring"""





         self.test()

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Dict:
         """simple docstring"""





         A_							       =		0
         A_							       =		False
         while not completed:
               if counter == 1:
                     self.reset()
               A_							       =		self.advance()
               if not self.does_advance(_lowercase       ):
                     raise Exception(
                         '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''       )

               A_       ,A_       ,A_							       =		self.update(_lowercase       )
               counter += 1

               if counter > 1_0_0_0_0:
                     raise Exception('''update() does not fulfill the constraint.'''       )

         if self.remaining() != 0:
               raise Exception('''Custom Constraint is not defined correctly.'''       )

   @abstractmethod
   def     UpperCamelCase					(				self	:    Tuple       ) ->			int:
         """simple docstring"""





         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )

   @abstractmethod
   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    Any       ) ->			Optional[int]:
         """simple docstring"""





         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )

   @abstractmethod
   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    str       ) ->			Dict:
         """simple docstring"""





         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )

   @abstractmethod
   def     UpperCamelCase					(				self	:    str       ) ->			Dict:
         """simple docstring"""





         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )

   @abstractmethod
   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Tuple:
         """simple docstring"""





         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )





   @abstractmethod
   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Optional[Any]=False       ) ->			Any:
         """simple docstring"""





         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )



class    _lowercase				(					_SCREAMING_SNAKE_CASE			):

   def __init__(				self	:    Optional[Any]			,						lowerCamelCase__	:    str       ) ->			List[Any]:
         """simple docstring"""





         super(_lowercase			,						self       ).__init__()

         if not isinstance(_lowercase			,						_lowercase       ) or len(_lowercase       ) == 0:
               raise ValueError(F"`token_ids` has to be a non-empty list, but is {token_ids}."       )
         if any((not isinstance(_lowercase			,						_lowercase       ) or token_id < 0) for token_id in token_ids       ):
               raise ValueError(F"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."       )

         A_							       =		token_ids

         A_							       =		len(self.token_ids       )
         A_							       =		-1  # the index of the currently fulfilled step
         A_							       =		False

   def     UpperCamelCase					(				self	:    Dict       ) ->			Optional[Any]:
         """simple docstring"""





         if self.completed:
               return None
         return self.token_ids[self.fulfilled_idx + 1]

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         if not isinstance(_lowercase			,						_lowercase       ):
               raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(_lowercase       )}"       )

         if self.completed:
               return False

         return token_id == self.token_ids[self.fulfilled_idx + 1]

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    str       ) ->			Dict:
         """simple docstring"""





         if not isinstance(_lowercase			,						_lowercase       ):
               raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(_lowercase       )}"       )

         A_							       =		False
         A_							       =		False
         A_							       =		False

         if self.does_advance(_lowercase       ):
               self.fulfilled_idx += 1
               A_							       =		True
               if self.fulfilled_idx == (self.seqlen - 1):
                     A_							       =		True
               A_							       =		completed
         else:
               # failed to make progress.
               A_							       =		True
               self.reset()
         return stepped, completed, reset

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			str:
         """simple docstring"""





         A_							       =		False
         A_							       =		0

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[Any]:
         """simple docstring"""





         return self.seqlen - (self.fulfilled_idx + 1)





   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    List[str]=False       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		PhrasalConstraint(self.token_ids       )

         if stateful:
               A_							       =		self.seqlen
               A_							       =		self.fulfilled_idx
               A_							       =		self.completed

         return new_constraint



class    _lowercase				:

   def __init__(				self	:    Optional[int]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    List[Any]=True       ) ->			List[str]:
         """simple docstring"""





         A_							       =		max([len(_lowercase       ) for one in nested_token_ids]       )

         A_							       =		{}
         for token_ids in nested_token_ids:
               A_							       =		root
               for tidx, token_id in enumerate(_lowercase       ):
                     if token_id not in level:
                           A_							       =		{}

                     A_							       =		level[token_id]

         if no_subsets and self.has_subsets(_lowercase			,						_lowercase       ):
               raise ValueError(
                   '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
                   F" {nested_token_ids}."       )

         A_							       =		root

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    List[Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		self.trie

         for current_token in current_seq:
               A_							       =		start[current_token]

         A_							       =		list(start.keys()       )

         return next_tokens

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		self.next_tokens(_lowercase       )

         return len(_lowercase       ) == 0

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    Optional[int]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		list(root.values()       )
         if len(_lowercase       ) == 0:
               return 1
         else:
               return sum([self.count_leaves(_lowercase       ) for nn in next_nodes]       )





   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		self.count_leaves(_lowercase       )
         return len(_lowercase       ) != leaf_count



class    _lowercase				(					_SCREAMING_SNAKE_CASE			):

   def __init__(				self	:    Tuple			,						lowerCamelCase__	:    List[Any]       ) ->			Any:
         """simple docstring"""





         super(_lowercase			,						self       ).__init__()

         if not isinstance(_lowercase			,						_lowercase       ) or len(_lowercase       ) == 0:
               raise ValueError(F"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."       )
         if any(not isinstance(_lowercase			,						_lowercase       ) for token_ids in nested_token_ids       ):
               raise ValueError(F"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."       )
         if any(
             any((not isinstance(_lowercase			,						_lowercase       ) or token_id < 0) for token_id in token_ids       )
             for token_ids in nested_token_ids       ):
               raise ValueError(
                   F"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."       )

         A_							       =		DisjunctiveTrie(_lowercase       )
         A_							       =		nested_token_ids

         A_							       =		self.trie.max_height
         A_							       =		[]
         A_							       =		False

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.trie.next_tokens(self.current_seq       )

         if len(_lowercase       ) == 0:
               return None
         else:
               return token_list

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    str       ) ->			str:
         """simple docstring"""





         if not isinstance(_lowercase			,						_lowercase       ):
               raise ValueError(F"`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowercase       )}"       )

         A_							       =		self.trie.next_tokens(self.current_seq       )

         return token_id in next_tokens

   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Dict       ) ->			Any:
         """simple docstring"""





         if not isinstance(_lowercase			,						_lowercase       ):
               raise ValueError(F"`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowercase       )}"       )

         A_							       =		False
         A_							       =		False
         A_							       =		False

         if self.does_advance(_lowercase       ):
               self.current_seq.append(_lowercase       )
               A_							       =		True
         else:
               A_							       =		True
               self.reset()

         A_							       =		self.trie.reached_leaf(self.current_seq       )
         A_							       =		completed

         return stepped, completed, reset

   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		False
         A_							       =		[]

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Union[str, Any]:
         """simple docstring"""





         if self.completed:
               # since this can be completed without reaching max height
               return 0
         else:
               return self.seqlen - len(self.current_seq       )





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    List[str]=False       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		DisjunctiveConstraint(self.token_ids       )

         if stateful:
               A_							       =		self.seqlen
               A_							       =		self.current_seq
               A_							       =		self.completed

         return new_constraint



class    _lowercase				:

   def __init__(				self	:    Dict			,						lowerCamelCase__	:    Optional[int]       ) ->			Any:
         """simple docstring"""





         A_							       =		constraints

         # max # of steps required to fulfill a given constraint
         A_							       =		max([c.seqlen for c in constraints]       )
         A_							       =		len(_lowercase       )
         A_							       =		False

         self.init_state()

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		[]
         A_							       =		None
         A_							       =		[constraint.copy(stateful=_lowercase       ) for constraint in self.constraints]

   def     UpperCamelCase					(				self	:    Dict       ) ->			int:
         """simple docstring"""





         A_							       =		0
         if self.inprogress_constraint:
               # extra points for having a constraint mid-fulfilled
               add += self.max_seqlen - self.inprogress_constraint.remaining()

         return (len(self.complete_constraints       ) * self.max_seqlen) + add

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Any:
         """simple docstring"""





         A_							       =		[]
         if self.inprogress_constraint is None:
               for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                     A_							       =		constraint.advance()
                     if isinstance(_lowercase			,						_lowercase       ):
                           token_list.append(_lowercase       )
                     elif isinstance(_lowercase			,						_lowercase       ):
                           token_list.extend(_lowercase       )
         else:
               A_							       =		self.inprogress_constraint.advance()
               if isinstance(_lowercase			,						_lowercase       ):
                     token_list.append(_lowercase       )
               elif isinstance(_lowercase			,						_lowercase       ):
                     token_list.extend(_lowercase       )

         if len(_lowercase       ) == 0:
               return None
         else:
               return token_list

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    str       ) ->			List[Any]:
         """simple docstring"""





         self.init_state()

         if token_ids is not None:
               for token in token_ids:
                     # completes or steps **one** constraint
                     A_       ,A_							       =		self.add(_lowercase       )

                     # the entire list of constraints are fulfilled
                     if self.completed:
                           break

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    str       ) ->			List[str]:
         """simple docstring"""





         if not isinstance(_lowercase			,						_lowercase       ):
               raise ValueError(F"`token_id` should be an `int`, but is `{token_id}`."       )

         A_       ,A_							       =		False, False

         if self.completed:
               A_							       =		True
               A_							       =		False
               return complete, stepped

         if self.inprogress_constraint is not None:
               # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
               # job, simply update the state

               A_       ,A_       ,A_							       =		self.inprogress_constraint.update(_lowercase       )
               if reset:
                     # 1. If the next token breaks the progress, then we must restart.
                     #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

                     #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
                     #     constraint, not the full list of constraints.

                     self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_lowercase       )       )
                     A_							       =		None

               if complete:
                     # 2. If the next token completes the constraint, move it to completed list, set
                     #     inprogress to None. If there are no pending constraints either, then this full list of constraints
                     #     is complete.

                     self.complete_constraints.append(self.inprogress_constraint       )
                     A_							       =		None

                     if len(self.pending_constraints       ) == 0:
                           # we're done!
                           A_							       =		True

         else:
               # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
               # of constraints?

               for cidx, pending_constraint in enumerate(self.pending_constraints       ):
                     if pending_constraint.does_advance(_lowercase       ):
                           A_       ,A_       ,A_							       =		pending_constraint.update(_lowercase       )

                           if not stepped:
                                 raise Exception(
                                     '''`constraint.update(token_id)` is not yielding incremental progress, '''
                                     '''even though `constraint.does_advance(token_id)` is true.'''       )

                           if complete:
                                 self.complete_constraints.append(_lowercase       )
                                 A_							       =		None

                           if not complete and stepped:
                                 A_							       =		pending_constraint

                           if complete or stepped:
                                 # If we made any progress at all, then it's at least not a "pending constraint".

                                 A_							       =		(
                                     self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                                 )

                                 if len(self.pending_constraints       ) == 0 and self.inprogress_constraint is None:
                                       # If there's no longer any pending after this and no inprogress either, then we must be
                                       # complete.

                                       A_							       =		True

                                 break  # prevent accidentally stepping through multiple constraints with just one token.

         return complete, stepped





   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Optional[int]=True       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		ConstraintListState(self.constraints       )  # we actually never though self.constraints objects
         # throughout this process. So it's at initialization state.

         if stateful:
               A_							       =		[
                   constraint.copy(stateful=_lowercase       ) for constraint in self.complete_constraints
               ]
               if self.inprogress_constraint is not None:
                     A_							       =		self.inprogress_constraint.copy(stateful=_lowercase       )
               A_							       =		[constraint.copy() for constraint in self.pending_constraints]

         return new_state







'''simple docstring'''



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation


def A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					384
						if "tiny" in model_name:
												_lowerCAmelCase    		=					[3, 3, 9, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "small" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "base" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[128, 256, 512, 1024]
												_lowerCAmelCase    		=					512
						if "large" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[192, 384, 768, 1536]
												_lowerCAmelCase    		=					768
						if "xlarge" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[256, 512, 1024, 2048]
												_lowerCAmelCase    		=					1024

						# set label information
						_lowerCAmelCase    		=					150
						_lowerCAmelCase    		=					"""huggingface/label-files"""
						_lowerCAmelCase    		=					"""ade20k-id2label.json"""
						_lowerCAmelCase    		=					json.load(open(hf_hub_download(__lowerCamelCase					,      __lowerCamelCase					,      repo_type="""dataset"""						)					,      """r"""						)						)
						_lowerCAmelCase    		=					{int(__lowerCamelCase						): v for k, v in idalabel.items()}
						_lowerCAmelCase    		=					{v: k for k, v in idalabel.items()}

						_lowerCAmelCase    		=					ConvNextConfig(
						    depths=__lowerCamelCase					,      hidden_sizes=__lowerCamelCase					,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						)
						_lowerCAmelCase    		=					UperNetConfig(
						    backbone_config=__lowerCamelCase					,      auxiliary_in_channels=__lowerCamelCase					,      num_labels=__lowerCamelCase					,      idalabel=__lowerCamelCase					,      labelaid=__lowerCamelCase					,      )

						return config


def A (__lowerCamelCase :Optional[Any]						):
						_lowerCAmelCase    		=					[]

						# fmt: off
						# stem
						rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")						)
						rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")						)
						rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")						)
						rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.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}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')						)
												if i > 0:
																		rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight')						)
																		rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias')						)
																		rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight')						)
																		rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.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 A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Tuple						):
						_lowerCAmelCase    		=					dct.pop(__lowerCamelCase						)
						_lowerCAmelCase    		=					val


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any						):
						_lowerCAmelCase    		=					{
						    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
						    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
						    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
						    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
						    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
						}
						_lowerCAmelCase    		=					model_name_to_url[model_name]
						_lowerCAmelCase    		=					torch.hub.load_state_dict_from_url(__lowerCamelCase					,      map_location="""cpu"""						)["""state_dict"""]

						_lowerCAmelCase    		=					get_upernet_config(__lowerCamelCase						)
						_lowerCAmelCase    		=					UperNetForSemanticSegmentation(__lowerCamelCase						)
						model.eval()

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

						# rename keys
						_lowerCAmelCase    		=					create_rename_keys(__lowerCamelCase						)
						for src, dest in rename_keys:
												rename_key(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)

						model.load_state_dict(__lowerCamelCase						)

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

						_lowerCAmelCase    		=					SegformerImageProcessor()
						_lowerCAmelCase    		=					processor(__lowerCamelCase					,      return_tensors="""pt"""						).pixel_values

						with torch.no_grad():
												_lowerCAmelCase    		=					model(__lowerCamelCase						)

						if model_name == "upernet-convnext-tiny":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]						)
						elif model_name == "upernet-convnext-small":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]						)
						elif model_name == "upernet-convnext-base":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]						)
						elif model_name == "upernet-convnext-large":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]						)
						elif model_name == "upernet-convnext-xlarge":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]						)
						print("""Logits:"""					,      outputs.logits[0, 0, :3, :3]						)
						assert torch.allclose(outputs.logits[0, 0, :3, :3]					,      __lowerCamelCase					,      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(__lowerCamelCase						)
												print(f'Saving processor to {pytorch_dump_folder_path}'						)
												processor.save_pretrained(__lowerCamelCase						)

						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__":
						_lowercase          =						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default="""upernet-convnext-tiny""",
						    type=str,
						    choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
						    help="""Name of the ConvNext 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."""
						)

						_lowercase          =						parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
