

import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


a_						     =    Mapping[str, np.ndarray]
a_						     =    Mapping[str, Any]  # Is a nested dict.
a_						     =    0.01






@dataclasses.dataclass(frozen=__A   )
class _UpperCamelCase							:
   '''simple docstring'''







   lowerCamelCase__           =42  # [num_res, num_atom_type, 3]

   # Amino-acid type for each residue represented as an integer between 0 and
   # 20, where 20 is 'X'.
   lowerCamelCase__           =42  # [num_res]

   # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
   # is present and 0.0 if not. This should be used for loss masking.
   lowerCamelCase__           =42  # [num_res, num_atom_type]

   # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
   lowerCamelCase__           =42  # [num_res]

   # B-factors, or temperature factors, of each residue (in sq. angstroms units),
   # representing the displacement of the residue from its ground truth mean
   # value.
   lowerCamelCase__           =42  # [num_res, num_atom_type]

   # Chain indices for multi-chain predictions
   lowerCamelCase__           =None

   # Optional remark about the protein. Included as a comment in output PDB
   # files
   lowerCamelCase__           =None

   # Templates used to generate this protein (prediction-only)
   lowerCamelCase__           =None

   # Chain corresponding to each parent
   lowerCamelCase__           =None

def  lowerCamelCase__ (					_a):
 SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       r"(\[[A-Z]+\]\n)"
 SCREAMING_SNAKE_CASE   :      List[str]				     =       [tag.strip() for tag in re.split(_a							,			_a) if len(_a) > 0]
 SCREAMING_SNAKE_CASE   :      Iterator[Tuple[str, List[str]]]				     =       zip(tags[0::2]							,			[l.split("\n") for l in tags[1::2]])

 SCREAMING_SNAKE_CASE   :      List[str]				     =       ["N", "CA", "C"]
 SCREAMING_SNAKE_CASE   :      List[Any]				     =       None
 SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
 SCREAMING_SNAKE_CASE   :      Any				     =       None
 for g in groups:
  if "[PRIMARY]" == g[0]:
   SCREAMING_SNAKE_CASE   :      List[Any]				     =       g[1][0].strip()
   for i in range(len(_a)):
    if seq[i] not in residue_constants.restypes:
     SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "X"  # FIXME: strings are immutable
   SCREAMING_SNAKE_CASE   :      str				     =       np.array(
       [residue_constants.restype_order.get(_a							,			residue_constants.restype_num) for res_symbol in seq])
  elif "[TERTIARY]" == g[0]:
   SCREAMING_SNAKE_CASE   :      List[List[float]]				     =       []
   for axis in range(3):
    tertiary.append(list(map(_a							,			g[1][axis].split())))
   SCREAMING_SNAKE_CASE   :      str				     =       np.array(_a)
   SCREAMING_SNAKE_CASE   :      int				     =       np.zeros((len(tertiary[0]) // 3, residue_constants.atom_type_num, 3)).astype(np.floataa)
   for i, atom in enumerate(_a):
    SCREAMING_SNAKE_CASE   :      Tuple				     =       np.transpose(tertiary_np[:, i::3])
   atom_positions *= PICO_TO_ANGSTROM
  elif "[MASK]" == g[0]:
   SCREAMING_SNAKE_CASE   :      List[str]				     =       np.array(list(map({"-": 0, "+": 1}.get							,			g[1][0].strip())))
   SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(
       (
           len(_a),
           residue_constants.atom_type_num,
       )).astype(np.floataa)
   for i, atom in enumerate(_a):
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       1
   atom_mask *= mask[..., None]

 assert aatype is not None

 return Protein(
     atom_positions=_a							,			atom_mask=_a							,			aatype=_a							,			residue_index=np.arange(len(_a))							,			b_factors=_a							,			)

def  lowerCamelCase__ (					_a							,			_a = 0):
 SCREAMING_SNAKE_CASE   :      List[str]				     =       []

 SCREAMING_SNAKE_CASE   :      Tuple				     =       prot.remark
 if remark is not None:
  pdb_headers.append(f"REMARK {remark}")

 SCREAMING_SNAKE_CASE   :      Any				     =       prot.parents
 SCREAMING_SNAKE_CASE   :      Tuple				     =       prot.parents_chain_index
 if parents is not None and parents_chain_index is not None:
  SCREAMING_SNAKE_CASE   :      int				     =       [p for i, p in zip(_a							,			_a) if i == chain_id]

 if parents is None or len(_a) == 0:
  SCREAMING_SNAKE_CASE   :      List[str]				     =       ["N/A"]

 pdb_headers.append(f"PARENT {' '.join(_a)}")

 return pdb_headers

def  lowerCamelCase__ (					_a							,			_a):
 SCREAMING_SNAKE_CASE   :      List[str]				     =       []
 SCREAMING_SNAKE_CASE   :      Dict				     =       pdb_str.split("\n")

 SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       prot.remark
 if remark is not None:
  out_pdb_lines.append(f"REMARK {remark}")

 SCREAMING_SNAKE_CASE   :      List[List[str]]
 if prot.parents is not None and len(prot.parents) > 0:
  SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
  if prot.parents_chain_index is not None:
   SCREAMING_SNAKE_CASE   :      Dict[str, List[str]]				     =       {}
   for p, i in zip(prot.parents							,			prot.parents_chain_index):
    parent_dict.setdefault(str(_a)							,			[])
    parent_dict[str(_a)].append(_a)

   SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max([int(_a) for chain_idx in parent_dict])
   for i in range(max_idx + 1):
    SCREAMING_SNAKE_CASE   :      Any				     =       parent_dict.get(str(_a)							,			["N/A"])
    parents_per_chain.append(_a)
  else:
   parents_per_chain.append(list(prot.parents))
 else:
  SCREAMING_SNAKE_CASE   :      str				     =       [["N/A"]]

 def make_parent_line(_a) -> str:
  return f"PARENT {' '.join(_a)}"

 out_pdb_lines.append(make_parent_line(parents_per_chain[0]))

 SCREAMING_SNAKE_CASE   :      List[Any]				     =       0
 for i, l in enumerate(_a):
  if "PARENT" not in l and "REMARK" not in l:
   out_pdb_lines.append(_a)
  if "TER" in l and "END" not in lines[i + 1]:
   chain_counter += 1
   if not chain_counter >= len(_a):
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       parents_per_chain[chain_counter]
   else:
    SCREAMING_SNAKE_CASE   :      str				     =       ["N/A"]

   out_pdb_lines.append(make_parent_line(_a))

 return "\n".join(_a)

def  lowerCamelCase__ (					_a):
 SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       residue_constants.restypes + ["X"]

 def res_atoa(_a) -> str:
  return residue_constants.restype_atoa.get(restypes[r]							,			"UNK")

 SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       residue_constants.atom_types

 SCREAMING_SNAKE_CASE   :      List[str]				     =       []

 SCREAMING_SNAKE_CASE   :      Tuple				     =       prot.atom_mask
 SCREAMING_SNAKE_CASE   :      Dict				     =       prot.aatype
 SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       prot.atom_positions
 SCREAMING_SNAKE_CASE   :      List[str]				     =       prot.residue_index.astype(np.intaa)
 SCREAMING_SNAKE_CASE   :      List[str]				     =       prot.b_factors
 SCREAMING_SNAKE_CASE   :      List[Any]				     =       prot.chain_index

 if np.any(aatype > residue_constants.restype_num):
  raise ValueError("Invalid aatypes.")

 SCREAMING_SNAKE_CASE   :      int				     =       get_pdb_headers(_a)
 if len(_a) > 0:
  pdb_lines.extend(_a)

 SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       aatype.shape[0]
 SCREAMING_SNAKE_CASE   :      List[Any]				     =       1
 SCREAMING_SNAKE_CASE   :      Dict				     =       0
 SCREAMING_SNAKE_CASE   :      Any				     =       string.ascii_uppercase
 SCREAMING_SNAKE_CASE   :      Dict				     =       None
 # Add all atom sites.
 for i in range(_a):
  SCREAMING_SNAKE_CASE   :      Any				     =       res_atoa(aatype[i])
  for atom_name, pos, mask, b_factor in zip(_a							,			atom_positions[i]							,			atom_mask[i]							,			b_factors[i]):
   if mask < 0.5:
    continue

   SCREAMING_SNAKE_CASE   :      Dict				     =       "ATOM"
   SCREAMING_SNAKE_CASE   :      List[str]				     =       atom_name if len(_a) == 4 else f" {atom_name}"
   SCREAMING_SNAKE_CASE   :      Tuple				     =       ""
   SCREAMING_SNAKE_CASE   :      List[Any]				     =       ""
   SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       1.00
   SCREAMING_SNAKE_CASE   :      int				     =       atom_name[0]  # Protein supports only C, N, O, S, this works.
   SCREAMING_SNAKE_CASE   :      Dict				     =       ""

   SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "A"
   if chain_index is not None:
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       chain_tags[chain_index[i]]

   # PDB is a columnar format, every space matters here!
   SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (
       f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
       f"{res_name_a:>3} {chain_tag:>1}"
       f"{residue_index[i]:>4}{insertion_code:>1}   "
       f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
       f"{occupancy:>6.2f}{b_factor:>6.2f}          "
       f"{element:>2}{charge:>2}"
   )
   pdb_lines.append(_a)
   atom_index += 1

  SCREAMING_SNAKE_CASE   :      int				     =       i == n - 1
  if chain_index is not None:
   if i != n - 1 and chain_index[i + 1] != prev_chain_index:
    SCREAMING_SNAKE_CASE   :      Any				     =       True
    SCREAMING_SNAKE_CASE   :      List[Any]				     =       chain_index[i + 1]

  if should_terminate:
   # Close the chain.
   SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "TER"
   SCREAMING_SNAKE_CASE   :      Tuple				     =       (
       f"{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]):>3} {chain_tag:>1}{residue_index[i]:>4}"
   )
   pdb_lines.append(_a)
   atom_index += 1

   if i != n - 1:
    # "prev" is a misnomer here. This happens at the beginning of
    # each new chain.
    pdb_lines.extend(get_pdb_headers(_a							,			_a))

 pdb_lines.append("END")
 pdb_lines.append("")
 return "\n".join(_a)

def  lowerCamelCase__ (					_a):
 return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def  lowerCamelCase__ (					_a							,			_a							,			_a = None							,			_a = None							,			_a = None							,			_a = None							,			_a = None							,			):
 return Protein(
     aatype=features["aatype"]							,			atom_positions=result["final_atom_positions"]							,			atom_mask=result["final_atom_mask"]							,			residue_index=features["residue_index"] + 1							,			b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"])							,			chain_index=_a							,			remark=_a							,			parents=_a							,			parents_chain_index=_a							,			)


import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       384
	SCREAMING_SNAKE_CASE   :      Dict				     =       7
	if "tiny" in model_name:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       96
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (2, 2, 6, 2)
		SCREAMING_SNAKE_CASE   :      Dict				     =       (3, 6, 12, 24)
	elif "small" in model_name:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       96
		SCREAMING_SNAKE_CASE   :      Dict				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      Dict				     =       (3, 6, 12, 24)
	elif "base" in model_name:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       128
		SCREAMING_SNAKE_CASE   :      Tuple				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      str				     =       (4, 8, 16, 32)
		SCREAMING_SNAKE_CASE   :      Any				     =       12
		SCREAMING_SNAKE_CASE   :      int				     =       512
	elif "large" in model_name:
		SCREAMING_SNAKE_CASE   :      Any				     =       192
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (6, 12, 24, 48)
		SCREAMING_SNAKE_CASE   :      Any				     =       12
		SCREAMING_SNAKE_CASE   :      Any				     =       768

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

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       SwinConfig(
	    embed_dim=_a							,			depths=_a							,			num_heads=_a							,			window_size=_a							,			out_features=["stage1", "stage2", "stage3", "stage4"]							,			)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UperNetConfig(
	    backbone_config=_a							,			auxiliary_in_channels=_a							,			num_labels=_a							,			idalabel=_a							,			labelaid=_a							,			)

	return config

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       []

	# fmt: off
	# stem
	rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
	rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
	rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight"))
	rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias"))
	# stages
	for i in range(len(config.backbone_config.depths)):
		for j in range(config.backbone_config.depths[i]):
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias"))

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

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

	return rename_keys

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       dct.pop(_a)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       val

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
	for i in range(len(backbone_config.depths)):
		SCREAMING_SNAKE_CASE   :      Tuple				     =       num_features[i]
		for j in range(backbone_config.depths[i]):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight")
			SCREAMING_SNAKE_CASE   :      List[str]				     =       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
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       in_proj_weight[:dim, :]
			SCREAMING_SNAKE_CASE   :      int				     =       in_proj_bias[: dim]
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_proj_weight[
			    dim : dim * 2, :
			]
			SCREAMING_SNAKE_CASE   :      str				     =       in_proj_bias[
			    dim : dim * 2
			]
			SCREAMING_SNAKE_CASE   :      List[str]				     =       in_proj_weight[
			    -dim :, :
			]
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       in_proj_bias[-dim :]
			# fmt: on

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       x.shape
	SCREAMING_SNAKE_CASE   :      List[str]				     =       x.reshape(_a							,			4							,			in_channel // 4)
	SCREAMING_SNAKE_CASE   :      str				     =       x[:, [0, 2, 1, 3], :].transpose(1							,			2).reshape(_a							,			_a)
	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       x.shape
	SCREAMING_SNAKE_CASE   :      Tuple				     =       x.reshape(_a							,			in_channel // 4							,			4)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       x[:, :, [0, 2, 1, 3]].transpose(1							,			2).reshape(_a							,			_a)

	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      str				     =       x.shape[0]
	SCREAMING_SNAKE_CASE   :      int				     =       x.reshape(4							,			in_channel // 4)
	SCREAMING_SNAKE_CASE   :      int				     =       x[[0, 2, 1, 3], :].transpose(0							,			1).reshape(_a)
	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       x.shape[0]
	SCREAMING_SNAKE_CASE   :      Dict				     =       x.reshape(in_channel // 4							,			4)
	SCREAMING_SNAKE_CASE   :      Dict				     =       x[:, [0, 2, 1, 3]].transpose(0							,			1).reshape(_a)
	return x

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       {
	    "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",
	}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model_name_to_url[model_name]
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.hub.load_state_dict_from_url(_a							,			map_location="cpu"							,			file_name=_a)[
	    "state_dict"
	]

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

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       get_upernet_config(_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       UperNetForSemanticSegmentation(_a)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       state_dict.pop(_a)
		if "bn" in key:
			SCREAMING_SNAKE_CASE   :      Any				     =       key.replace("bn"							,			"batch_norm")
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       val

	# rename keys
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       create_rename_keys(_a)
	for src, dest in rename_keys:
		rename_key(_a							,			_a							,			_a)
	read_in_q_k_v(_a							,			config.backbone_config)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       reverse_correct_unfold_reduction_order(_a)
			if "norm" in key:
				SCREAMING_SNAKE_CASE   :      str				     =       reverse_correct_unfold_norm_order(_a)

	model.load_state_dict(_a)

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

	SCREAMING_SNAKE_CASE   :      Tuple				     =       SegformerImageProcessor()
	SCREAMING_SNAKE_CASE   :      Dict				     =       processor(_a							,			return_tensors="pt").pixel_values

	with torch.no_grad():
		SCREAMING_SNAKE_CASE   :      str				     =       model(_a)
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       outputs.logits

	print(logits.shape)
	print("First values of logits:"							,			logits[0, 0, :3, :3])
	# assert values
	if model_name == "upernet-swin-tiny":
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor(
		    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]])
	elif model_name == "upernet-swin-small":
		SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.tensor(
		    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]])
	elif model_name == "upernet-swin-base":
		SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor(
		    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]])
	elif model_name == "upernet-swin-large":
		SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor(
		    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]])
	print("Logits:"							,			outputs.logits[0, 0, :3, :3])
	assert torch.allclose(outputs.logits[0, 0, :3, :3]							,			_a							,			atol=1E-4)
	print("Looks ok!")

	if pytorch_dump_folder_path is not None:
		print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
		model.save_pretrained(_a)
		print(f"Saving processor to {pytorch_dump_folder_path}")
		processor.save_pretrained(_a)

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


if __name__ == "__main__":
			a_						     =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default='upernet-swin-tiny',
			    type=str,
			    choices=[F'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
			    help='Name of the Swin + UperNet model you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
			)

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