
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__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     int:
						_lowercase					       =					3_84
						_lowercase					       =					7
						if "tiny" in model_name:
												_lowercase					       =					96
												_lowercase					       =					(2, 2, 6, 2)
												_lowercase					       =					(3, 6, 12, 24)
						elif "small" in model_name:
												_lowercase					       =					96
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(3, 6, 12, 24)
						elif "base" in model_name:
												_lowercase					       =					1_28
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(4, 8, 16, 32)
												_lowercase					       =					12
												_lowercase					       =					5_12
						elif "large" in model_name:
												_lowercase					       =					1_92
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(6, 12, 24, 48)
												_lowercase					       =					12
												_lowercase					       =					7_68

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

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

						return config




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     Any:
						_lowercase					       =					[]

						# 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__  (     SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     int:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Any:
						_lowercase       ,						_lowercase					       =					x.shape
						_lowercase					       =					x.reshape(SCREAMING_SNAKE_CASE_		,   4		,   in_channel // 4       )
						_lowercase					       =					x[:, [0, 2, 1, 3], :].transpose(1		,   2       ).reshape(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						return x




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Dict       )  ->     Any:
						_lowercase       ,						_lowercase					       =					x.shape
						_lowercase					       =					x.reshape(SCREAMING_SNAKE_CASE_		,   in_channel // 4		,   4       )
						_lowercase					       =					x[:, :, [0, 2, 1, 3]].transpose(1		,   2       ).reshape(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )

						return x




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Dict:
						_lowercase					       =					x.shape[0]
						_lowercase					       =					x.reshape(4		,   in_channel // 4       )
						_lowercase					       =					x[[0, 2, 1, 3], :].transpose(0		,   1       ).reshape(SCREAMING_SNAKE_CASE_       )
						return x




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     Optional[int]:
						_lowercase					       =					x.shape[0]
						_lowercase					       =					x.reshape(in_channel // 4		,   4       )
						_lowercase					       =					x[:, [0, 2, 1, 3]].transpose(0		,   1       ).reshape(SCREAMING_SNAKE_CASE_       )
						return x




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[Any]:
						_lowercase					       =					{
						    """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""",
						}
						_lowercase					       =					model_name_to_url[model_name]
						_lowercase					       =					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""		,   file_name=SCREAMING_SNAKE_CASE_       )[
						    """state_dict"""
						]

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

						_lowercase					       =					get_upernet_config(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE_       )
						model.eval()

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

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

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

						model.load_state_dict(SCREAMING_SNAKE_CASE_       )

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

						_lowercase					       =					SegformerImageProcessor()
						_lowercase					       =					processor(SCREAMING_SNAKE_CASE_		,   return_tensors="""pt"""       ).pixel_values

						with torch.no_grad():
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					outputs.logits

						print(logits.shape       )
						print("""First values of logits:"""		,   logits[0, 0, :3, :3]       )
						# assert values
						if model_name == "upernet-swin-tiny":
												_lowercase					       =					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":
												_lowercase					       =					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":
												_lowercase					       =					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":
												_lowercase					       =					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]		,   SCREAMING_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(SCREAMING_SNAKE_CASE_       )
												print(f"""Saving processor to {pytorch_dump_folder_path}"""       )
												processor.save_pretrained(SCREAMING_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__":
		A		: Optional[Any]								=     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		: List[str]								=     parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int = 2_00_00_00       )  ->     int:
						_lowercase					       =					[0 for i in range(n + 1       )]
						_lowercase					       =					1
						_lowercase					       =					1

						for i in range(2		,   int(n**0.5       ) + 1       ):
												if primality_list[i] == 0:
																		for j in range(i * i		,   n + 1		,   SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					1
						_lowercase					       =					0
						for i in range(SCREAMING_SNAKE_CASE_       ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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