






from string import ascii_uppercase

_a      	=  {str(ord(c) - 55): c for c in ascii_uppercase}


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase ,		__lowerCAmelCase	):
												raise TypeError('int() can\'t convert non-string with explicit base'	)
						if num < 0:
												raise ValueError('parameter must be positive int'	)
						if isinstance(__lowerCAmelCase ,		__lowerCAmelCase	):
												raise TypeError('\'str\' object cannot be interpreted as an integer'	)
						if isinstance(__lowerCAmelCase ,		__lowerCAmelCase	):
												raise TypeError('\'float\' object cannot be interpreted as an integer'	)
						if base in (0, 1):
												raise ValueError('base must be >= 2'	)
						if base > 36:
												raise ValueError('base must be <= 36'	)
						_UpperCAmelCase									=			''
						_UpperCAmelCase									=			0
						_UpperCAmelCase									=			0
						while div != 1:
												_UpperCAmelCase					, _UpperCAmelCase									=			divmod(__lowerCAmelCase ,		__lowerCAmelCase	)
												if base >= 11 and 9 < mod < 36:
																		_UpperCAmelCase									=			ALPHABET_VALUES[str(__lowerCAmelCase	)]
												else:
																		_UpperCAmelCase									=			str(__lowerCAmelCase	)
												new_value += actual_value
												_UpperCAmelCase									=			num // base
												_UpperCAmelCase									=			div
												if div == 0:
																		return str(new_value[::-1]	)
												elif div == 1:
																		new_value += str(__lowerCAmelCase	)
																		return str(new_value[::-1]	)

						return new_value[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		for base in range(2, 37):
				for num in range(1000):
						assert int(decimal_to_any(num, base), base) == num, (
						    num,
						    base,
						    decimal_to_any(num, base),
						    int(decimal_to_any(num, base), base),
						)


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

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

							SCREAMING_SNAKE_CASE__						:       str           =   SwinConfig(
							    embed_dim=_snake_case   ,depths=_snake_case   ,num_heads=_snake_case   ,window_size=_snake_case   ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   ,)
							SCREAMING_SNAKE_CASE__						:       int           =   UperNetConfig(
							    backbone_config=_snake_case   ,auxiliary_in_channels=_snake_case   ,num_labels=_snake_case   ,idalabel=_snake_case   ,labelaid=_snake_case   ,)

							return config

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   dct.pop(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   val

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

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

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   x.shape
							SCREAMING_SNAKE_CASE__						:       Any           =   x.reshape(_snake_case   ,in_channel // 4   ,4						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   x[:, :, [0, 2, 1, 3]].transpose(1   ,2						).reshape(_snake_case   ,_snake_case						)

							return x

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Tuple           =   x.shape[0]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   x.reshape(4   ,in_channel // 4						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   x[[0, 2, 1, 3], :].transpose(0   ,1						).reshape(_snake_case						)
							return x

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   x.shape[0]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   x.reshape(in_channel // 4   ,4						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   x[:, [0, 2, 1, 3]].transpose(0   ,1						).reshape(_snake_case						)
							return x

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       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""",
							}
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model_name_to_url[model_name]
							SCREAMING_SNAKE_CASE__						:       Optional[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						)

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   get_upernet_config(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   UperNetForSemanticSegmentation(_snake_case						)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   state_dict.pop(_snake_case						)
														if "bn" in key:
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   key.replace("""bn"""   ,"""batch_norm"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   val

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

							model.load_state_dict(_snake_case						)

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

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   SegformerImageProcessor()
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   processor(_snake_case   ,return_tensors="""pt"""						).pixel_values

							with torch.no_grad():
														SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   outputs.logits

							print(logits.shape						)
							print("""First values of logits:"""   ,logits[0, 0, :3, :3]						)
							# assert values
							if model_name == "upernet-swin-tiny":
														SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.tensor(
														    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]						)
							elif model_name == "upernet-swin-small":
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.tensor(
														    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]						)
							elif model_name == "upernet-swin-base":
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.tensor(
														    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]						)
							elif model_name == "upernet-swin-large":
														SCREAMING_SNAKE_CASE__						:       Dict           =   torch.tensor(
														    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]						)
							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__":
					UpperCAmelCase__      :    List[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.'
					)

					UpperCAmelCase__      :    List[str]    							= parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




