




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

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

						_A							:  Any    =		SwinConfig(
						    embed_dim=snake_case_,depths=snake_case_,num_heads=snake_case_,window_size=snake_case_,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""],)
						_A							:  Any    =		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_  ):
						_A							:  Optional[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_			(  snake_case_,snake_case_,snake_case_  ):
						_A							:  str    =		dct.pop(snake_case_  )
						_A							:  Union[str, Any]    =		val







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
						_A							:  Dict    =		[int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
						for i in range(len(backbone_config.depths  )  ):
												_A							:  str    =		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							:  Union[str, Any]    =		state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''  )
																		_A							:  Optional[Any]    =		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							:  Optional[int]    =		in_proj_weight[:dim, :]
																		_A							:  Tuple    =		in_proj_bias[: dim]
																		_A							:  Union[str, Any]    =		in_proj_weight[
																		    dim : dim * 2, :
																		]
																		_A							:  Tuple    =		in_proj_bias[
																		    dim : dim * 2
																		]
																		_A							:  int    =		in_proj_weight[
																		    -dim :, :
																		]
																		_A							:  Union[str, Any]    =		in_proj_bias[-dim :]
																		# fmt: on







def        lowerCAmelCase_			(  snake_case_  ):
						_A							:  Optional[int]    =		x.shape
						_A							:  Optional[Any]    =		x.reshape(snake_case_,4,in_channel // 4  )
						_A							:  Tuple    =		x[:, [0, 2, 1, 3], :].transpose(1,2  ).reshape(snake_case_,snake_case_  )
						return x







def        lowerCAmelCase_			(  snake_case_  ):
						_A							:  Optional[Any]    =		x.shape
						_A							:  str    =		x.reshape(snake_case_,in_channel // 4,4  )
						_A							:  Any    =		x[:, :, [0, 2, 1, 3]].transpose(1,2  ).reshape(snake_case_,snake_case_  )

						return x







def        lowerCAmelCase_			(  snake_case_  ):
						_A							:  Any    =		x.shape[0]
						_A							:  List[Any]    =		x.reshape(4,in_channel // 4  )
						_A							:  Dict    =		x[[0, 2, 1, 3], :].transpose(0,1  ).reshape(snake_case_  )
						return x







def        lowerCAmelCase_			(  snake_case_  ):
						_A							:  List[str]    =		x.shape[0]
						_A							:  Union[str, Any]    =		x.reshape(in_channel // 4,4  )
						_A							:  str    =		x[:, [0, 2, 1, 3]].transpose(0,1  ).reshape(snake_case_  )
						return x







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
						_A							:  Dict    =		{
						    """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							:  Optional[int]    =		model_name_to_url[model_name]
						_A							:  int    =		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  )

						_A							:  Tuple    =		get_upernet_config(snake_case_  )
						_A							:  Optional[Any]    =		UperNetForSemanticSegmentation(snake_case_  )
						model.eval()

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

						# rename keys
						_A							:  Tuple    =		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:
																								_A							:  Union[str, Any]    =		reverse_correct_unfold_reduction_order(snake_case_  )
																		if "norm" in key:
																								_A							:  str    =		reverse_correct_unfold_norm_order(snake_case_  )

						model.load_state_dict(snake_case_  )

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

						_A							:  int    =		SegformerImageProcessor()
						_A							:  List[Any]    =		processor(snake_case_,return_tensors="""pt"""  ).pixel_values

						with torch.no_grad():
												_A							:  Any    =		model(snake_case_  )
												_A							:  str    =		outputs.logits

						print(logits.shape  )
						print("""First values of logits:""",logits[0, 0, :3, :3]  )
						# assert values
						if model_name == "upernet-swin-tiny":
												_A							:  List[Any]    =		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":
												_A							:  Optional[Any]    =		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":
												_A							:  Union[str, Any]    =		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":
												_A							:  List[Any]    =		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],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__":
		_snake_case            = 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."
		)

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







def        lowerCAmelCase_			(  snake_case_ = 1000000  ):
      _A							:  Any    =		limit + 1
      _A							:  Tuple    =		[0] * limit
      for first_term in range(1,snake_case_  ):
            for n in range(snake_case_,snake_case_,snake_case_  ):
                  _A							:  Optional[int]    =		first_term + n / first_term
                  if common_difference % 4:  # d must be divisble by 4
                        continue
                  else:
                        common_difference /= 4
                        if (
                            first_term > common_difference
                            and first_term < 4 * common_difference
                        ):  # since x,y,z are positive integers
                              frequency[n] += 1  # so z>0 and a>d ,also 4d<a

      _A							:  List[str]    =		sum(1 for x in frequency[1:limit] if x == 10  )

      return count


if __name__ == "__main__":
  print(f"""{solution() = }""")

