




'''simple docstring'''



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






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self  ):







             '''simple docstring'''







             self.test()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						False
             while not completed:
                   if counter == 1:
                         self.reset()
                   SCREAMING_SNAKE_CASE   :      str    					=						self.advance()
                   if not self.does_advance(A  ):
                         raise Exception(
                             'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'  )

                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.update(A  )
                   counter += 1

                   if counter > 10_000:
                         raise Exception('update() does not fulfill the constraint.'  )

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







       @abstractmethod
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self,							A=False  ):







             '''simple docstring'''







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






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super(A,							self  ).__init__()

             if not isinstance(A,							A  ) or len(A  ) == 0:
                   raise ValueError(F"`token_ids` has to be a non-empty list, but is {token_ids}."  )
             if any((not isinstance(A,							A  ) 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}."  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						token_ids

             SCREAMING_SNAKE_CASE   :      Tuple    					=						len(self.token_ids  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						-1  # the index of the currently fulfilled step
             SCREAMING_SNAKE_CASE   :      Any    					=						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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

             if self.completed:
                   return False

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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      Dict    					=						False
             SCREAMING_SNAKE_CASE   :      str    					=						False
             SCREAMING_SNAKE_CASE   :      int    					=						False

             if self.does_advance(A  ):
                   self.fulfilled_idx += 1
                   SCREAMING_SNAKE_CASE   :      int    					=						True
                   if self.fulfilled_idx == (self.seqlen - 1):
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						True
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						completed
             else:
                   # failed to make progress.
                   SCREAMING_SNAKE_CASE   :      int    					=						True
                   self.reset()
             return stepped, completed, reset







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False
             SCREAMING_SNAKE_CASE   :      Dict    					=						0







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A=False  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						PhrasalConstraint(self.token_ids  )

             if stateful:
                   SCREAMING_SNAKE_CASE   :      str    					=						self.seqlen
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.fulfilled_idx
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.completed

             return new_constraint






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						max([len(A  ) for one in nested_token_ids]  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}
             for token_ids in nested_token_ids:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						root
                   for tidx, token_id in enumerate(A  ):
                         if token_id not in level:
                               SCREAMING_SNAKE_CASE   :      Dict    					=						{}

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						level[token_id]

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

             SCREAMING_SNAKE_CASE   :      Any    					=						root







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.trie

             for current_token in current_seq:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						start[current_token]

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						list(start.keys()  )

             return next_tokens







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.next_tokens(A  )

             return len(A  ) == 0







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						list(root.values()  )
             if len(A  ) == 0:
                   return 1
             else:
                   return sum([self.count_leaves(A  ) for nn in next_nodes]  )







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.count_leaves(A  )
             return len(A  ) != leaf_count






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super(A,							self  ).__init__()

             if not isinstance(A,							A  ) or len(A  ) == 0:
                   raise ValueError(F"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."  )
             if any(not isinstance(A,							A  ) 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(A,							A  ) 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}."  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						DisjunctiveTrie(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						nested_token_ids

             SCREAMING_SNAKE_CASE   :      int    					=						self.trie.max_height
             SCREAMING_SNAKE_CASE   :      str    					=						[]
             SCREAMING_SNAKE_CASE   :      List[str]    					=						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.trie.next_tokens(self.current_seq  )

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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.trie.next_tokens(self.current_seq  )

             return token_id in next_tokens







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						False
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False

             if self.does_advance(A  ):
                   self.current_seq.append(A  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						True
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						True
                   self.reset()

             SCREAMING_SNAKE_CASE   :      int    					=						self.trie.reached_leaf(self.current_seq  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						completed

             return stepped, completed, reset







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						False
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]







       def        UpperCamelCase_							(      self  ):







             '''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,							A=False  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						DisjunctiveConstraint(self.token_ids  )

             if stateful:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self.seqlen
                   SCREAMING_SNAKE_CASE   :      Dict    					=						self.current_seq
                   SCREAMING_SNAKE_CASE   :      str    					=						self.completed

             return new_constraint






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						constraints

             # max # of steps required to fulfill a given constraint
             SCREAMING_SNAKE_CASE   :      List[str]    					=						max([c.seqlen for c in constraints]  )
             SCREAMING_SNAKE_CASE   :      str    					=						len(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						False

             self.init_state()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      str    					=						[constraint.copy(stateful=A  ) for constraint in self.constraints]







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						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  ):







             '''simple docstring'''







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

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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             self.init_state()

             if token_ids is not None:
                   for token in token_ids:
                         # completes or steps **one** constraint
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.add(A  )

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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						False, False

             if self.completed:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						True
                   SCREAMING_SNAKE_CASE   :      Dict    					=						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

                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.inprogress_constraint.update(A  )
                   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=A  )  )
                         SCREAMING_SNAKE_CASE   :      Dict    					=						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  )
                         SCREAMING_SNAKE_CASE   :      Any    					=						None

                         if len(self.pending_constraints  ) == 0:
                               # we're done!
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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(A  ):
                               SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						pending_constraint.update(A  )

                               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(A  )
                                     SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						None

                               if not complete and stepped:
                                     SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pending_constraint

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

                                     SCREAMING_SNAKE_CASE   :      Any    					=						(
                                         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.

                                           SCREAMING_SNAKE_CASE   :      Dict    					=						True

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

             return complete, stepped







       def        UpperCamelCase_							(      self,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						ConstraintListState(self.constraints  )  # we actually never though self.constraints objects
             # throughout this process. So it's at initialization state.

             if stateful:
                   SCREAMING_SNAKE_CASE   :      int    					=						[
                       constraint.copy(stateful=A  ) for constraint in self.complete_constraints
                   ]
                   if self.inprogress_constraint is not None:
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						self.inprogress_constraint.copy(stateful=A  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[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  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				384
  if "tiny" in model_name:
    lowerCAmelCase__     :List[Any]											=				[3, 3, 9, 3]
    lowerCAmelCase__     :Tuple											=				[96, 192, 384, 768]
  if "small" in model_name:
    lowerCAmelCase__     :Union[str, Any]											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[96, 192, 384, 768]
  if "base" in model_name:
    lowerCAmelCase__     :Dict											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[128, 256, 512, 1024]
    lowerCAmelCase__     :Union[str, Any]											=				512
  if "large" in model_name:
    lowerCAmelCase__     :int											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[192, 384, 768, 1536]
    lowerCAmelCase__     :Optional[Any]											=				768
  if "xlarge" in model_name:
    lowerCAmelCase__     :Optional[Any]											=				[3, 3, 27, 3]
    lowerCAmelCase__     :str											=				[256, 512, 1024, 2048]
    lowerCAmelCase__     :Union[str, Any]											=				1024

  # set label information
  lowerCAmelCase__     :Tuple											=				150
  lowerCAmelCase__     :List[Any]											=				'huggingface/label-files'
  lowerCAmelCase__     :Tuple											=				'ade20k-id2label.json'
  lowerCAmelCase__     :Tuple											=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  repo_type='dataset'     )	,  'r'     )     )
  lowerCAmelCase__     :Dict											=				{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
  lowerCAmelCase__     :int											=				{v: k for k, v in idalabel.items()}

  lowerCAmelCase__     :List[str]											=				ConvNextConfig(
      depths=_SCREAMING_SNAKE_CASE	,  hidden_sizes=_SCREAMING_SNAKE_CASE	,  out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
  lowerCAmelCase__     :Union[str, Any]											=				UperNetConfig(
      backbone_config=_SCREAMING_SNAKE_CASE	,  auxiliary_in_channels=_SCREAMING_SNAKE_CASE	,  num_labels=_SCREAMING_SNAKE_CASE	,  idalabel=_SCREAMING_SNAKE_CASE	,  labelaid=_SCREAMING_SNAKE_CASE	,  )

  return config
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				[]

  # 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  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				dct.pop(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				val
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				{
      '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__     :List[Any]											=				model_name_to_url[model_name]
  lowerCAmelCase__     :Optional[Any]											=				torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['state_dict']

  lowerCAmelCase__     :List[Any]											=				get_upernet_config(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				UperNetForSemanticSegmentation(_SCREAMING_SNAKE_CASE     )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    lowerCAmelCase__     :Optional[int]											=				state_dict.pop(_SCREAMING_SNAKE_CASE     )
    if "bn" in key:
      lowerCAmelCase__     :Any											=				key.replace('bn'	,  'batch_norm'     )
    lowerCAmelCase__     :int											=				val

  # rename keys
  lowerCAmelCase__     :Optional[Any]											=				create_rename_keys(_SCREAMING_SNAKE_CASE     )
  for src, dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

  # verify on image
  lowerCAmelCase__     :str											=				'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
  lowerCAmelCase__     :Optional[Any]											=				Image.open(requests.get(_SCREAMING_SNAKE_CASE	,  stream=_SCREAMING_SNAKE_CASE     ).raw     ).convert('RGB'     )

  lowerCAmelCase__     :Tuple											=				SegformerImageProcessor()
  lowerCAmelCase__     :List[Any]											=				processor(_SCREAMING_SNAKE_CASE	,  return_tensors='pt'     ).pixel_values

  with torch.no_grad():
    lowerCAmelCase__     :Optional[Any]											=				model(_SCREAMING_SNAKE_CASE     )

  if model_name == "upernet-convnext-tiny":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]     )
  elif model_name == "upernet-convnext-small":
    lowerCAmelCase__     :Union[str, Any]											=				torch.tensor(
        [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]     )
  elif model_name == "upernet-convnext-base":
    lowerCAmelCase__     :Dict											=				torch.tensor(
        [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]     )
  elif model_name == "upernet-convnext-large":
    lowerCAmelCase__     :List[str]											=				torch.tensor(
        [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]     )
  elif model_name == "upernet-convnext-xlarge":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]     )
  print('Logits:'	,  outputs.logits[0, 0, :3, :3]     )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]	,  _SCREAMING_SNAKE_CASE	,  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(_SCREAMING_SNAKE_CASE     )
    print(F"Saving processor to {pytorch_dump_folder_path}"     )
    processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

  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-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."""
  )

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