






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      (						__snake_case       ):
     __lowerCAmelCase								=					384
     __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								=					128
          __lowerCAmelCase								=					(2, 2, 18, 2)
          __lowerCAmelCase								=					(4, 8, 16, 32)
          __lowerCAmelCase								=					12
          __lowerCAmelCase								=					512
     elif "large" in model_name:
          __lowerCAmelCase								=					192
          __lowerCAmelCase								=					(2, 2, 18, 2)
          __lowerCAmelCase								=					(6, 12, 24, 48)
          __lowerCAmelCase								=					12
          __lowerCAmelCase								=					768

     # set label information
     __lowerCAmelCase								=					150
     __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       __lowerCAmelCase      (						__snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					dct.pop(__snake_case       )
     __lowerCAmelCase								=					val





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __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_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":
          __lowerCAmelCase								=					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":
          __lowerCAmelCase								=					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":
          __lowerCAmelCase								=					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]					, __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__":
 lowerCamelCase			:					int         =	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.'''
 )

 lowerCamelCase			:					Optional[Any]         =	parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     if not isinstance(__snake_case					, __snake_case       ):
          raise ValueError("iterations must be defined as integers"       )
     if not isinstance(__snake_case					, __snake_case       ) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"       )
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"       )

     __lowerCAmelCase								=					""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(__snake_case       )

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()






