




import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Optional[int]			=						[]
      if isinstance(snake_case_,snake_case_     ):
            for v in tree.values():
                  shapes.extend(_fetch_dims(snake_case_     )     )
      elif isinstance(snake_case_,(list, tuple)     ):
            for t in tree:
                  shapes.extend(_fetch_dims(snake_case_     )     )
      elif isinstance(snake_case_,torch.Tensor     ):
            shapes.append(tree.shape     )
      else:
            raise ValueError("""Not supported"""     )

      return shapes







@torch.jit.ignore
def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     int			=						[]
      for d in reversed(snake_case_     ):
            idx.append(flat_idx % d     )
            _A							:     List[str]			=						flat_idx // d

      return tuple(reversed(snake_case_     )     )







@torch.jit.ignore
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = None,snake_case_ = None,):

      # start_edges and end_edges both indicate whether, starting from any given
      # dimension, the start/end index is at the top/bottom edge of the
      # corresponding tensor, modeled as a tree
      def reduce_edge_list(snake_case_     ) -> None:
            _A							:     List[str]			=						True
            for i in range(len(snake_case_     )     ):
                  _A							:     List[str]			=						-1 * (i + 1)
                  l[reversed_idx] &= tally
                  _A							:     List[Any]			=						l[reversed_idx]

      if start_edges is None:
            _A							:     str			=						[s == 0 for s in start]
            reduce_edge_list(snake_case_     )
      if end_edges is None:
            _A							:     Any			=						[e == (d - 1) for e, d in zip(snake_case_,snake_case_     )]
            reduce_edge_list(snake_case_     )

      # Base cases. Either start/end are empty and we're done, or the final,
      # one-dimensional tensor can be simply sliced
      if len(snake_case_     ) == 0:
            return [()]
      elif len(snake_case_     ) == 1:
            return [(slice(start[0],end[0] + 1     ),)]

      _A							:     List[Tuple[slice, ...]]			=						[]
      _A							:     List[slice]			=						[]

      # Dimensions common to start and end can be selected directly
      for s, e in zip(snake_case_,snake_case_     ):
            if s == e:
                  path_list.append(slice(snake_case_,s + 1     )     )
            else:
                  break

      _A							:     Tuple[slice, ...]			=						tuple(snake_case_     )
      _A							:     str			=						len(snake_case_     )

      # start == end, and we're done
      if divergence_idx == len(snake_case_     ):
            return [path]

      def upper() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            _A							:     Any			=						start[divergence_idx]
            return tuple(
                path + (slice(snake_case_,sdi + 1     ),) + s
                for s in _get_minimal_slice_set(
                    start[divergence_idx + 1 :],[d - 1 for d in dims[divergence_idx + 1 :]],dims[divergence_idx + 1 :],start_edges=start_edges[divergence_idx + 1 :],end_edges=[True for _ in end_edges[divergence_idx + 1 :]],)     )

      def lower() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            _A							:     Union[str, Any]			=						end[divergence_idx]
            return tuple(
                path + (slice(snake_case_,edi + 1     ),) + s
                for s in _get_minimal_slice_set(
                    [0 for _ in start[divergence_idx + 1 :]],end[divergence_idx + 1 :],dims[divergence_idx + 1 :],start_edges=[True for _ in start_edges[divergence_idx + 1 :]],end_edges=end_edges[divergence_idx + 1 :],)     )

      # If both start and end are at the edges of the subtree rooted at
      # divergence_idx, we can just select the whole subtree at once
      if start_edges[divergence_idx] and end_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx],end[divergence_idx] + 1     ),)     )
      # If just start is at the edge, we can grab almost all of the subtree,
      # treating only the ragged bottom edge as an edge case
      elif start_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx],end[divergence_idx]     ),)     )
            slices.extend(lower()     )
      # Analogous to the previous case, but the top is ragged this time
      elif end_edges[divergence_idx]:
            slices.extend(upper()     )
            slices.append(path + (slice(start[divergence_idx] + 1,end[divergence_idx] + 1     ),)     )
      # If both sides of the range are ragged, we need to handle both sides
      # separately. If there's contiguous meat in between them, we can index it
      # in one big chunk
      else:
            slices.extend(upper()     )
            _A							:     List[str]			=						end[divergence_idx] - start[divergence_idx]
            if middle_ground > 1:
                  slices.append(path + (slice(start[divergence_idx] + 1,end[divergence_idx]     ),)     )
            slices.extend(lower()     )

      return slices







@torch.jit.ignore
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     Optional[int]			=						t.shape[:no_batch_dims]
      _A							:     Tuple			=						list(_flat_idx_to_idx(snake_case_,snake_case_     )     )
      # _get_minimal_slice_set is inclusive
      _A							:     str			=						list(_flat_idx_to_idx(flat_end - 1,snake_case_     )     )

      # Get an ordered list of slices to perform
      _A							:     int			=						_get_minimal_slice_set(
          snake_case_,snake_case_,snake_case_,)

      _A							:     Optional[int]			=						[t[s] for s in slices]

      return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]     ) for s in sliced_tensors]     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_,snake_case_ = False,snake_case_ = None,snake_case_ = False,):
      if not (len(snake_case_     ) > 0):
            raise ValueError("""Must provide at least one input"""     )

      _A							:     int			=						[shape[:no_batch_dims] for shape in _fetch_dims(snake_case_     )]
      _A							:     Tuple			=						tuple([max(snake_case_     ) for s in zip(*snake_case_     )]     )

      def _prep_inputs(snake_case_     ) -> torch.Tensor:
            if not low_mem:
                  if not sum(t.shape[:no_batch_dims]     ) == no_batch_dims:
                        _A							:     Dict			=						t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
                  _A							:     Union[str, Any]			=						t.reshape(-1,*t.shape[no_batch_dims:]     )
            else:
                  _A							:     Dict			=						t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
            return t

      _A							:     Dict[str, Any]			=						tensor_tree_map(_prep_inputs,snake_case_     )
      _A							:     Tuple			=						None
      if _out is not None:
            _A							:     Optional[int]			=						tensor_tree_map(lambda snake_case_     : t.view([-1] + list(t.shape[no_batch_dims:]     )     ),_out     )

      _A							:     Optional[Any]			=						1
      for d in orig_batch_dims:
            flat_batch_dim *= d

      _A							:     str			=						flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

      def _select_chunk(snake_case_     ) -> torch.Tensor:
            return t[i : i + chunk_size] if t.shape[0] != 1 else t

      _A							:     Any			=						0
      _A							:     List[str]			=						prepped_outputs
      for _ in range(snake_case_     ):
            # Chunk the input
            if not low_mem:
                  _A							:     List[Any]			=						_select_chunk
            else:
                  _A							:     str			=						partial(
                      _chunk_slice,flat_start=snake_case_,flat_end=min(snake_case_,i + chunk_size     ),no_batch_dims=len(snake_case_     ),)

            _A							:     Dict[str, Any]			=						tensor_tree_map(snake_case_,snake_case_     )

            # Run the layer on the chunk
            _A							:     Tuple			=						layer(**snake_case_     )

            # Allocate space for the output
            if out is None:
                  _A							:     Optional[Any]			=						tensor_tree_map(lambda snake_case_     : t.new_zeros((flat_batch_dim,) + t.shape[1:]     ),snake_case_     )

            # Put the chunk in its pre-allocated space
            if isinstance(snake_case_,snake_case_     ):

                  def assign(snake_case_,snake_case_     ) -> None:
                        for k, v in da.items():
                              if isinstance(snake_case_,snake_case_     ):
                                    assign(snake_case_,da[k]     )
                              else:
                                    if _add_into_out:
                                          v[i : i + chunk_size] += da[k]
                                    else:
                                          _A							:     List[Any]			=						da[k]

                  assign(snake_case_,snake_case_     )
            elif isinstance(snake_case_,snake_case_     ):
                  for xa, xa in zip(snake_case_,snake_case_     ):
                        if _add_into_out:
                              xa[i : i + chunk_size] += xa
                        else:
                              _A							:     Tuple			=						xa
            elif isinstance(snake_case_,torch.Tensor     ):
                  if _add_into_out:
                        out[i : i + chunk_size] += output_chunk
                  else:
                        _A							:     Optional[int]			=						output_chunk
            else:
                  raise ValueError("""Not supported"""     )

            i += chunk_size

      _A							:     int			=						tensor_tree_map(lambda snake_case_     : t.view(orig_batch_dims + t.shape[1:]     ),snake_case_     )

      return out







class 						lowercase							:

     def __init__(  self		,     _a = 512		,     )						->							str:
           _A							:     Union[str, Any]			=						max_chunk_size
           _A							:     Optional[int]			=						None
           _A							:     Optional[tuple]			=						None

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           logging.info("""Tuning chunk size..."""				)

           if min_chunk_size >= self.max_chunk_size:
                 return min_chunk_size

           _A							:     List[int]			=						[2**l for l in range(int(math.log(self.max_chunk_size		,     2				)				) + 1				)]
           _A							:     List[Any]			=						[c for c in candidates if c > min_chunk_size]
           _A							:     Optional[Any]			=						[min_chunk_size] + candidates
           candidates[-1] += 4

           def test_chunk_size(_a				) -> bool:
                 try:
                       with torch.no_grad():
                             fn(*_a		,     chunk_size=_a				)
                       return True
                 except RuntimeError:
                       return False

           _A							:     Optional[int]			=						0
           _A							:     str			=						len(_a				) - 1
           while i > min_viable_chunk_size_index:
                 _A							:     Optional[Any]			=						test_chunk_size(candidates[i]				)
                 if not viable:
                       _A							:     Union[str, Any]			=						(min_viable_chunk_size_index + i) // 2
                 else:
                       _A							:     Union[str, Any]			=						i
                       _A							:     Optional[Any]			=						(i + len(_a				) - 1) // 2

           return candidates[min_viable_chunk_size_index]

     def 					a__	(  self		,     _a		,     _a				)						->							bool:
           _A							:     Optional[Any]			=						True
           for aa, aa in zip(_a		,     _a				):
                 assert type(_a				) == type(_a				)
                 if isinstance(_a		,     (list, tuple)				):
                       consistent &= self._compare_arg_caches(_a		,     _a				)
                 elif isinstance(_a		,     _a				):
                       _A							:     Union[str, Any]			=						[v for _, v in sorted(aa.items()		,     key=lambda _a				: x[0]				)]
                       _A							:     Union[str, Any]			=						[v for _, v in sorted(aa.items()		,     key=lambda _a				: x[0]				)]
                       consistent &= self._compare_arg_caches(_a		,     _a				)
                 else:
                       consistent &= aa == aa

           return consistent



     def 					a__	(  self		,     _a		,     _a		,     _a		,     )						->							int:
           _A							:     Tuple			=						True
           _A							:     tuple			=						tree_map(lambda _a				: a.shape if isinstance(_a		,     torch.Tensor				) else a		,     _a		,     _a				)
           if self.cached_arg_data is not None:
                 # If args have changed shape/value, we need to re-tune
                 assert len(self.cached_arg_data				) == len(_a				)
                 _A							:     List[Any]			=						self._compare_arg_caches(self.cached_arg_data		,     _a				)
           else:
                 # Otherwise, we can reuse the precomputed value
                 _A							:     Tuple			=						False

           if not consistent:
                 _A							:     int			=						self._determine_favorable_chunk_size(
                     _a		,     _a		,     _a		,     )
                 _A							:     int			=						arg_data

           assert self.cached_chunk_size is not None

           return self.cached_chunk_size


'''simple docstring'''

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      UpperCamelCase_						(					snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

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

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

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

 return config







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						[]

 # 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      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       List[str]						,		snake_case_       :       Any      )					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						dct.pop(snake_case_      )
 __lowerCAmelCase											=						val







def      UpperCamelCase_						(					snake_case_       :       str						,		snake_case_       :       str      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
 for i in range(len(backbone_config.depths      )      ):
  __lowerCAmelCase											=						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)
   __lowerCAmelCase											=						state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""      )
   __lowerCAmelCase											=						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
   __lowerCAmelCase											=						in_proj_weight[:dim, :]
   __lowerCAmelCase											=						in_proj_bias[: dim]
   __lowerCAmelCase											=						in_proj_weight[
       dim : dim * 2, :
   ]
   __lowerCAmelCase											=						in_proj_bias[
       dim : dim * 2
   ]
   __lowerCAmelCase											=						in_proj_weight[
       -dim :, :
   ]
   __lowerCAmelCase											=						in_proj_bias[-dim :]
   # fmt: on







def      UpperCamelCase_						(					snake_case_       :       str      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						x.shape
 __lowerCAmelCase											=						x.reshape(snake_case_						,		4						,		in_channel // 4      )
 __lowerCAmelCase											=						x[:, [0, 2, 1, 3], :].transpose(1						,		2      ).reshape(snake_case_						,		snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						x.shape
 __lowerCAmelCase											=						x.reshape(snake_case_						,		in_channel // 4						,		4      )
 __lowerCAmelCase											=						x[:, :, [0, 2, 1, 3]].transpose(1						,		2      ).reshape(snake_case_						,		snake_case_      )

 return x







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						x.shape[0]
 __lowerCAmelCase											=						x.reshape(4						,		in_channel // 4      )
 __lowerCAmelCase											=						x[[0, 2, 1, 3], :].transpose(0						,		1      ).reshape(snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						x.shape[0]
 __lowerCAmelCase											=						x.reshape(in_channel // 4						,		4      )
 __lowerCAmelCase											=						x[:, [0, 2, 1, 3]].transpose(0						,		1      ).reshape(snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Tuple						,		snake_case_       :       Dict      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						{
     """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""",
 }
 __lowerCAmelCase											=						model_name_to_url[model_name]
 __lowerCAmelCase											=						torch.hub.load_state_dict_from_url(snake_case_						,		map_location="""cpu"""						,		file_name=snake_case_      )[
     """state_dict"""
 ]

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

 __lowerCAmelCase											=						get_upernet_config(snake_case_      )
 __lowerCAmelCase											=						UperNetForSemanticSegmentation(snake_case_      )
 model.eval()

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

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

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

 model.load_state_dict(snake_case_      )

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

 __lowerCAmelCase											=						SegformerImageProcessor()
 __lowerCAmelCase											=						processor(snake_case_						,		return_tensors="""pt"""      ).pixel_values

 with torch.no_grad():
  __lowerCAmelCase											=						model(snake_case_      )
  __lowerCAmelCase											=						outputs.logits

 print(logits.shape      )
 print("""First values of logits:"""						,		logits[0, 0, :3, :3]      )
 # assert values
 if model_name == "upernet-swin-tiny":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]      )
 elif model_name == "upernet-swin-small":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]      )
 elif model_name == "upernet-swin-base":
  __lowerCAmelCase											=						torch.tensor(
      [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]      )
 elif model_name == "upernet-swin-large":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]      )
 print("""Logits:"""						,		outputs.logits[0, 0, :3, :3]      )
 assert torch.allclose(outputs.logits[0, 0, :3, :3]						,		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(snake_case_      )
  print(f"""Saving processor to {pytorch_dump_folder_path}"""      )
  processor.save_pretrained(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			:      Dict					      =				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			:      int					      =				parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






