



import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	str    		= GPTSanJapaneseTokenizer
  lowerCAmelCase__		:	List[Any]    		= False
  lowerCAmelCase__		:	Optional[Any]    		= {'do_clean_text': False, 'add_prefix_space': False}






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> str:




    '''simple docstring'''




    super().setUp()

    # fmt: off
    __UpperCAmelCase						    =       ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
    # fmt: on
    __UpperCAmelCase						    =       {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
    __UpperCAmelCase						    =       {"unk_token": "<unk>"}

    __UpperCAmelCase						    =       os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]   )
    __UpperCAmelCase						    =       os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["emoji_file"]   )
    with open(self.vocab_file				,				"w"				,				encoding="utf-8"   ) as vocab_writer:
      vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )
    with open(self.emoji_file				,				"w"   ) as emoji_writer:
      emoji_writer.write(json.dumps(__lowerCAmelCase   )   )






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




    '''simple docstring'''




    kwargs.update(self.special_tokens_map   )
    return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname				,				**__lowerCAmelCase   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       "こんにちは、世界。 \nこんばんは、㔺界。😀"
    __UpperCAmelCase						    =       "こんにちは、世界。 \nこんばんは、世界。😀"
    return input_text, output_text






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




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.get_input_output_texts(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase				,				clean_up_tokenization_spaces=__lowerCAmelCase   )
    return text, ids






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Optional[Any]:




    '''simple docstring'''




    pass  # TODO add if relevant






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




    '''simple docstring'''




    pass  # TODO add if relevant






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




    '''simple docstring'''




    pass  # TODO add if relevant






  def 	_UpperCAmelCase				(				self:			str   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizer()

    # Testing tokenization
    __UpperCAmelCase						    =       "こんにちは、世界。　こんばんは、㔺界。"
    __UpperCAmelCase						    =       ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
    __UpperCAmelCase						    =       tokenizer.tokenize(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    # Testing conversion to ids without special tokens
    __UpperCAmelCase						    =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
    __UpperCAmelCase						    =       tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    # Testing conversion to ids with special tokens
    __UpperCAmelCase						    =       tokens + [tokenizer.unk_token]
    __UpperCAmelCase						    =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
    __UpperCAmelCase						    =       tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizer()

    # Testing tokenization
    __UpperCAmelCase						    =       "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
    __UpperCAmelCase						    =       "こんにちは、、、、世界。こんばんは、、、、世界。"
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  @slow
  def 	_UpperCAmelCase				(				self:			List[str]   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

    # Testing tokenization
    __UpperCAmelCase						    =       "こんにちは、世界。"
    __UpperCAmelCase						    =       "こんばんは、㔺界。😀"
    __UpperCAmelCase						    =       "こんにちは、世界。こんばんは、世界。😀"
    __UpperCAmelCase						    =       tokenizer.encode(prefix_text + input_text   )
    __UpperCAmelCase						    =       tokenizer.encode(""				,				prefix_text=prefix_text + input_text   )
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase				,				prefix_text=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  @slow
  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

    # Testing tokenization
    __UpperCAmelCase						    =       "こんにちは、世界。"
    __UpperCAmelCase						    =       "こんばんは、㔺界。😀"

    __UpperCAmelCase						    =       len(tokenizer.encode(__lowerCAmelCase   )   ) - 2
    __UpperCAmelCase						    =       len(tokenizer.encode(__lowerCAmelCase   )   ) - 2

    __UpperCAmelCase						    =       [1] + [0] * (len_prefix + len_text + 1)
    __UpperCAmelCase						    =       [1] * (len_prefix + len_text + 1) + [0]
    __UpperCAmelCase						    =       [1] + [1] * (len_prefix) + [0] * (len_text + 1)

    __UpperCAmelCase						    =       tokenizer(prefix_text + input_text   ).token_type_ids
    __UpperCAmelCase						    =       tokenizer(""				,				prefix_text=prefix_text + input_text   ).token_type_ids
    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				prefix_text=__lowerCAmelCase   ).token_type_ids
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  @slow
  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

    __UpperCAmelCase						    =       tokenizer.encode("あンいワ"   )
    __UpperCAmelCase						    =       tokenizer.encode(""				,				prefix_text="あンいワ"   )
    __UpperCAmelCase						    =       tokenizer.encode("いワ"				,				prefix_text="あン"   )

    self.assertEqual(tokenizer.decode(__lowerCAmelCase   )				,				tokenizer.decode(__lowerCAmelCase   )   )
    self.assertEqual(tokenizer.decode(__lowerCAmelCase   )				,				tokenizer.decode(__lowerCAmelCase   )   )
    self.assertNotEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
    self.assertNotEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
    self.assertEqual(x_token_a[1]				,				x_token_a[-1]   )  # SEG token
    self.assertEqual(x_token_a[1]				,				x_token_a[3]   )  # SEG token






  @slow
  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

    __UpperCAmelCase						    =       [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				padding=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.batch_encode_plus(__lowerCAmelCase				,				padding=__lowerCAmelCase   )

    # fmt: off
    __UpperCAmelCase						    =       [[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
    __UpperCAmelCase						    =       [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
    __UpperCAmelCase						    =       [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
    # fmt: on
    self.assertListEqual(x_token.input_ids				,				__lowerCAmelCase   )
    self.assertListEqual(x_token.token_type_ids				,				__lowerCAmelCase   )
    self.assertListEqual(x_token.attention_mask				,				__lowerCAmelCase   )
    self.assertListEqual(x_token_a.input_ids				,				__lowerCAmelCase   )
    self.assertListEqual(x_token_a.token_type_ids				,				__lowerCAmelCase   )
    self.assertListEqual(x_token_a.attention_mask				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			int   ) -> Union[str, Any]:




    '''simple docstring'''




    pass






  def 	_UpperCAmelCase				(				self:			str   ) -> Union[str, Any]:




    '''simple docstring'''




    pass




import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




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

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

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

  return config




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

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

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

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

  return rename_keys




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




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Dict					)    ->  Tuple:
  __UpperCAmelCase						    =       [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
  for i in range(len(backbone_config.depths					)					):
    __UpperCAmelCase						    =       num_features[i]
    for j in range(backbone_config.depths[i]					):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      __UpperCAmelCase						    =       state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''					)
      __UpperCAmelCase						    =       state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''					)
      # next, add query, keys and values (in that order) to the state dict
      __UpperCAmelCase						    =       in_proj_weight[:dim, :]
      __UpperCAmelCase						    =       in_proj_bias[: dim]
      __UpperCAmelCase						    =       in_proj_weight[
          dim : dim * 2, :
      ]
      __UpperCAmelCase						    =       in_proj_bias[
          dim : dim * 2
      ]
      __UpperCAmelCase						    =       in_proj_weight[
          -dim :, :
      ]
      __UpperCAmelCase						    =       in_proj_bias[-dim :]
      # fmt: on




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




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

  return x




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




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




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

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

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

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

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

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

  model.load_state_dict(A_					)

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

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

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

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

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

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


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

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



