






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 						_a    (      a :Tuple )							->			Optional[Any]:
 a		  =      384
 a		  =      7
 if "tiny" in model_name:
  a		  =      96
  a		  =      (2, 2, 6, 2)
  a		  =      (3, 6, 12, 24)
 elif "small" in model_name:
  a		  =      96
  a		  =      (2, 2, 18, 2)
  a		  =      (3, 6, 12, 24)
 elif "base" in model_name:
  a		  =      128
  a		  =      (2, 2, 18, 2)
  a		  =      (4, 8, 16, 32)
  a		  =      12
  a		  =      512
 elif "large" in model_name:
  a		  =      192
  a		  =      (2, 2, 18, 2)
  a		  =      (6, 12, 24, 48)
  a		  =      12
  a		  =      768

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

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

 return config
def 						_a    (      a :Dict )							->			List[str]:
 a		  =      []

 # 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 						_a    (      a :Optional[int]	,							a :Optional[Any]	,							a :List[Any] )							->			Optional[int]:
 a		  =      dct.pop(a )
 a		  =      val
def 						_a    (      a :Union[str, Any]	,							a :str )							->			Any:
 a		  =      [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
 for i in range(len(backbone_config.depths ) ):
  a		  =      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)
   a		  =      state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight""" )
   a		  =      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
   a		  =      in_proj_weight[:dim, :]
   a		  =      in_proj_bias[: dim]
   a		  =      in_proj_weight[
       dim : dim * 2, :
   ]
   a		  =      in_proj_bias[
       dim : dim * 2
   ]
   a		  =      in_proj_weight[
       -dim :, :
   ]
   a		  =      in_proj_bias[-dim :]
   # fmt: on
def 						_a    (      a :List[str] )							->			List[str]:
 a  ,     a		  =      x.shape
 a		  =      x.reshape(a	,							4	,							in_channel // 4 )
 a		  =      x[:, [0, 2, 1, 3], :].transpose(1	,							2 ).reshape(a	,							a )
 return x
def 						_a    (      a :Optional[Any] )							->			Any:
 a  ,     a		  =      x.shape
 a		  =      x.reshape(a	,							in_channel // 4	,							4 )
 a		  =      x[:, :, [0, 2, 1, 3]].transpose(1	,							2 ).reshape(a	,							a )

 return x
def 						_a    (      a :Dict )							->			Any:
 a		  =      x.shape[0]
 a		  =      x.reshape(4	,							in_channel // 4 )
 a		  =      x[[0, 2, 1, 3], :].transpose(0	,							1 ).reshape(a )
 return x
def 						_a    (      a :Any )							->			List[str]:
 a		  =      x.shape[0]
 a		  =      x.reshape(in_channel // 4	,							4 )
 a		  =      x[:, [0, 2, 1, 3]].transpose(0	,							1 ).reshape(a )
 return x
def 						_a    (      a :Union[str, Any]	,							a :str	,							a :Optional[int] )							->			Any:
 a		  =      {
     '''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''',
 }
 a		  =      model_name_to_url[model_name]
 a		  =      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 )

 a		  =      get_upernet_config(a )
 a		  =      UperNetForSemanticSegmentation(a )
 model.eval()

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

 # rename keys
 a		  =      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:
    a		  =      reverse_correct_unfold_reduction_order(a )
   if "norm" in key:
    a		  =      reverse_correct_unfold_norm_order(a )

 model.load_state_dict(a )

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

 a		  =      SegformerImageProcessor()
 a		  =      processor(a	,							return_tensors='''pt''' ).pixel_values

 with torch.no_grad():
  a		  =      model(a )
  a		  =      outputs.logits

 print(logits.shape )
 print('''First values of logits:'''	,							logits[0, 0, :3, :3] )
 # assert values
 if model_name == "upernet-swin-tiny":
  a		  =      torch.tensor(
      [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]] )
 elif model_name == "upernet-swin-small":
  a		  =      torch.tensor(
      [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]] )
 elif model_name == "upernet-swin-base":
  a		  =      torch.tensor(
      [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]] )
 elif model_name == "upernet-swin-large":
  a		  =      torch.tensor(
      [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]] )
 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__":
     UpperCAmelCase__				   =		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."
     )

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''llama'''
      __snake_case       =  ['''past_key_values''']







      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Union[str, Any]=32_000 ,					__UpperCAmelCase      :						str=4_096 ,					__UpperCAmelCase      :						int=11_008 ,					__UpperCAmelCase      :						Tuple=32 ,					__UpperCAmelCase      :						Optional[int]=32 ,					__UpperCAmelCase      :						List[str]=None ,					__UpperCAmelCase      :						Union[str, Any]="silu" ,					__UpperCAmelCase      :						Tuple=2_048 ,					__UpperCAmelCase      :						Optional[Any]=0.02 ,					__UpperCAmelCase      :						Any=1e-6 ,					__UpperCAmelCase      :						Union[str, Any]=True ,					__UpperCAmelCase      :						Optional[int]=0 ,					__UpperCAmelCase      :						Optional[int]=1 ,					__UpperCAmelCase      :						Optional[int]=2 ,					__UpperCAmelCase      :						Tuple=1 ,					__UpperCAmelCase      :						List[str]=False ,					__UpperCAmelCase      :						Tuple=None ,					**__UpperCAmelCase      :						Tuple ,					)    ->str:




       """simple docstring"""


       a		  =      vocab_size
       a		  =      max_position_embeddings
       a		  =      hidden_size
       a		  =      intermediate_size
       a		  =      num_hidden_layers
       a		  =      num_attention_heads

       # for backward compatibility
       if num_key_value_heads is None:
        a		  =      num_attention_heads

       a		  =      num_key_value_heads
       a		  =      hidden_act
       a		  =      initializer_range
       a		  =      rms_norm_eps
       a		  =      pretraining_tp
       a		  =      use_cache
       a		  =      rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=__UpperCAmelCase ,					bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					tie_word_embeddings=__UpperCAmelCase ,					**__UpperCAmelCase ,					)







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Tuple:




       """simple docstring"""


       if self.rope_scaling is None:
        return

       if not isinstance(self.rope_scaling ,					__UpperCAmelCase  ) or len(self.rope_scaling  ) != 2:
        raise ValueError(
            '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
            F"""got {self.rope_scaling}"""  )
       a		  =      self.rope_scaling.get('''type''' ,					__UpperCAmelCase  )
       a		  =      self.rope_scaling.get('''factor''' ,					__UpperCAmelCase  )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""  )
       if rope_scaling_factor is None or not isinstance(__UpperCAmelCase ,					__UpperCAmelCase  ) or rope_scaling_factor <= 1.0:
        raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""  )


