


'''simple docstring'''



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       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   Any       				=       3_8_4
			UpperCAmelCase__					:   int       				=       7
			if "tiny" in model_name:
						UpperCAmelCase__					:   Union[str, Any]       				=       9_6
						UpperCAmelCase__					:   Any       				=       (2, 2, 6, 2)
						UpperCAmelCase__					:   List[str]       				=       (3, 6, 1_2, 2_4)
			elif "small" in model_name:
						UpperCAmelCase__					:   Tuple       				=       9_6
						UpperCAmelCase__					:   Union[str, Any]       				=       (2, 2, 1_8, 2)
						UpperCAmelCase__					:   Dict       				=       (3, 6, 1_2, 2_4)
			elif "base" in model_name:
						UpperCAmelCase__					:   List[str]       				=       1_2_8
						UpperCAmelCase__					:   Optional[int]       				=       (2, 2, 1_8, 2)
						UpperCAmelCase__					:   Union[str, Any]       				=       (4, 8, 1_6, 3_2)
						UpperCAmelCase__					:   Optional[int]       				=       1_2
						UpperCAmelCase__					:   List[str]       				=       5_1_2
			elif "large" in model_name:
						UpperCAmelCase__					:   List[str]       				=       1_9_2
						UpperCAmelCase__					:   List[Any]       				=       (2, 2, 1_8, 2)
						UpperCAmelCase__					:   List[Any]       				=       (6, 1_2, 2_4, 4_8)
						UpperCAmelCase__					:   Dict       				=       1_2
						UpperCAmelCase__					:   str       				=       7_6_8

			# set label information
			UpperCAmelCase__					:   Optional[int]       				=       1_5_0
			UpperCAmelCase__					:   Optional[int]       				=       """huggingface/label-files"""
			UpperCAmelCase__					:   Dict       				=       """ade20k-id2label.json"""
			UpperCAmelCase__					:   Tuple       				=       json.load(open(hf_hub_download(UpperCamelCase__	, UpperCamelCase__	, repo_type="""dataset"""   )	, """r"""   )   )
			UpperCAmelCase__					:   List[str]       				=       {int(UpperCamelCase__   ): v for k, v in idalabel.items()}
			UpperCAmelCase__					:   Optional[Any]       				=       {v: k for k, v in idalabel.items()}

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

			return config







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Dict       				=       dct.pop(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[int]       				=       val







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







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       x.shape
			UpperCAmelCase__					:   Union[str, Any]       				=       x.reshape(UpperCamelCase__	, 4	, in_channel // 4   )
			UpperCAmelCase__					:   int       				=       x[:, [0, 2, 1, 3], :].transpose(1	, 2   ).reshape(UpperCamelCase__	, UpperCamelCase__   )
			return x







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__				,    UpperCAmelCase__					:   List[Any]       				=       x.shape
			UpperCAmelCase__					:   Optional[Any]       				=       x.reshape(UpperCamelCase__	, in_channel // 4	, 4   )
			UpperCAmelCase__					:   Optional[int]       				=       x[:, :, [0, 2, 1, 3]].transpose(1	, 2   ).reshape(UpperCamelCase__	, UpperCamelCase__   )

			return x







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       x.shape[0]
			UpperCAmelCase__					:   Union[str, Any]       				=       x.reshape(4	, in_channel // 4   )
			UpperCAmelCase__					:   str       				=       x[[0, 2, 1, 3], :].transpose(0	, 1   ).reshape(UpperCamelCase__   )
			return x







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   Any       				=       x.shape[0]
			UpperCAmelCase__					:   int       				=       x.reshape(in_channel // 4	, 4   )
			UpperCAmelCase__					:   List[str]       				=       x[:, [0, 2, 1, 3]].transpose(0	, 1   ).reshape(UpperCamelCase__   )
			return x







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[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""",
			}
			UpperCAmelCase__					:   Tuple       				=       model_name_to_url[model_name]
			UpperCAmelCase__					:   Union[str, Any]       				=       torch.hub.load_state_dict_from_url(UpperCamelCase__	, map_location="""cpu"""	, file_name=UpperCamelCase__   )[
			    """state_dict"""
			]

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

			UpperCAmelCase__					:   Optional[int]       				=       get_upernet_config(UpperCamelCase__   )
			UpperCAmelCase__					:   Tuple       				=       UperNetForSemanticSegmentation(UpperCamelCase__   )
			model.eval()

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

			# rename keys
			UpperCAmelCase__					:   List[Any]       				=       create_rename_keys(UpperCamelCase__   )
			for src, dest in rename_keys:
						rename_key(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			read_in_q_k_v(UpperCamelCase__	, config.backbone_config   )

			# fix downsample parameters
			for key, value in state_dict.items():
						if "downsample" in key:
									if "reduction" in key:
												UpperCAmelCase__					:   Optional[Any]       				=       reverse_correct_unfold_reduction_order(UpperCamelCase__   )
									if "norm" in key:
												UpperCAmelCase__					:   Optional[int]       				=       reverse_correct_unfold_norm_order(UpperCamelCase__   )

			model.load_state_dict(UpperCamelCase__   )

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

			UpperCAmelCase__					:   Dict       				=       SegformerImageProcessor()
			UpperCAmelCase__					:   List[Any]       				=       processor(UpperCamelCase__	, return_tensors="""pt"""   ).pixel_values

			with torch.no_grad():
						UpperCAmelCase__					:   Union[str, Any]       				=       model(UpperCamelCase__   )
						UpperCAmelCase__					:   str       				=       outputs.logits

			print(logits.shape   )
			print("""First values of logits:"""	, logits[0, 0, :3, :3]   )
			# assert values
			if model_name == "upernet-swin-tiny":
						UpperCAmelCase__					:   str       				=       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__					:   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":
						UpperCAmelCase__					:   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":
						UpperCAmelCase__					:   Union[str, 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]	, UpperCamelCase__	, 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(UpperCamelCase__   )
						print(f'''Saving processor to {pytorch_dump_folder_path}'''   )
						processor.save_pretrained(UpperCamelCase__   )

			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)



'''simple docstring'''



import functools







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):

			# Validation
			if not isinstance(UpperCamelCase__	, UpperCamelCase__   ) or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for day in days   ):
						raise ValueError("""The parameter days should be a list of integers"""   )

			if len(UpperCamelCase__   ) != 3 or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for cost in costs   ):
						raise ValueError("""The parameter costs should be a list of three integers"""   )

			if len(UpperCamelCase__   ) == 0:
						return 0

			if min(UpperCamelCase__   ) <= 0:
						raise ValueError("""All days elements should be greater than 0"""   )

			if max(UpperCamelCase__   ) >= 3_6_6:
						raise ValueError("""All days elements should be less than 366"""   )

			UpperCAmelCase__					:   Union[str, Any]       				=       set(UpperCamelCase__   )

			@functools.cache
			def dynamic_programming(UpperCamelCase__   ) -> int:
						if index > 3_6_5:
									return 0

						if index not in days_set:
									return dynamic_programming(index + 1   )

						return min(
						    costs[0] + dynamic_programming(index + 1   )	, costs[1] + dynamic_programming(index + 7   )	, costs[2] + dynamic_programming(index + 3_0   )	, )

			return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()