



'''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      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Tuple     =					3_84
			A_     :						Dict     =					7
			if "tiny" in model_name:
						A_     :						Dict     =					96
						A_     :						Dict     =					(2, 2, 6, 2)
						A_     :						int     =					(3, 6, 12, 24)
			elif "small" in model_name:
						A_     :						List[str]     =					96
						A_     :						Dict     =					(2, 2, 18, 2)
						A_     :						Optional[int]     =					(3, 6, 12, 24)
			elif "base" in model_name:
						A_     :						Union[str, Any]     =					1_28
						A_     :						List[Any]     =					(2, 2, 18, 2)
						A_     :						Optional[Any]     =					(4, 8, 16, 32)
						A_     :						Optional[int]     =					12
						A_     :						Dict     =					5_12
			elif "large" in model_name:
						A_     :						str     =					1_92
						A_     :						Tuple     =					(2, 2, 18, 2)
						A_     :						int     =					(6, 12, 24, 48)
						A_     :						str     =					12
						A_     :						List[Any]     =					7_68

			# set label information
			A_     :						Optional[int]     =					1_50
			A_     :						Dict     =					"""huggingface/label-files"""
			A_     :						Dict     =					"""ade20k-id2label.json"""
			A_     :						List[str]     =					json.load(open(hf_hub_download(lowerCamelCase__						,				lowerCamelCase__						,				repo_type="""dataset"""   )						,				"""r"""   )   )
			A_     :						int     =					{int(lowerCamelCase__   ): v for k, v in idalabel.items()}
			A_     :						int     =					{v: k for k, v in idalabel.items()}

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

			return config



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						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      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						str     =					dct.pop(lowerCamelCase__   )
			A_     :						Any     =					val



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_,   A_     :						Any     =					x.shape
			A_     :						List[Any]     =					x.reshape(lowerCamelCase__						,				4						,				in_channel // 4   )
			A_     :						Any     =					x[:, [0, 2, 1, 3], :].transpose(1						,				2   ).reshape(lowerCamelCase__						,				lowerCamelCase__   )
			return x



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_,   A_     :						Tuple     =					x.shape
			A_     :						List[Any]     =					x.reshape(lowerCamelCase__						,				in_channel // 4						,				4   )
			A_     :						List[Any]     =					x[:, :, [0, 2, 1, 3]].transpose(1						,				2   ).reshape(lowerCamelCase__						,				lowerCamelCase__   )

			return x



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[Any]     =					x.shape[0]
			A_     :						int     =					x.reshape(in_channel // 4						,				4   )
			A_     :						Optional[Any]     =					x[:, [0, 2, 1, 3]].transpose(0						,				1   ).reshape(lowerCamelCase__   )
			return x



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[int]     =					{
			    """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_     :						Union[str, Any]     =					model_name_to_url[model_name]
			A_     :						List[Any]     =					torch.hub.load_state_dict_from_url(lowerCamelCase__						,				map_location="""cpu"""						,				file_name=lowerCamelCase__   )[
			    """state_dict"""
			]

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

			A_     :						Union[str, Any]     =					get_upernet_config(lowerCamelCase__   )
			A_     :						Optional[int]     =					UperNetForSemanticSegmentation(lowerCamelCase__   )
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						A_     :						Any     =					state_dict.pop(lowerCamelCase__   )
						if "bn" in key:
									A_     :						Optional[Any]     =					key.replace("""bn"""						,				"""batch_norm"""   )
						A_     :						Union[str, Any]     =					val

			# rename keys
			A_     :						Optional[int]     =					create_rename_keys(lowerCamelCase__   )
			for src, dest in rename_keys:
						rename_key(lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   )
			read_in_q_k_v(lowerCamelCase__						,				config.backbone_config   )

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

			model.load_state_dict(lowerCamelCase__   )

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

			A_     :						List[str]     =					SegformerImageProcessor()
			A_     :						List[Any]     =					processor(lowerCamelCase__						,				return_tensors="""pt"""   ).pixel_values

			with torch.no_grad():
						A_     :						int     =					model(lowerCamelCase__   )
						A_     :						Any     =					outputs.logits

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

			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							: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.'''
						)

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




'''simple docstring'''




lowerCamelCase							:Any       = {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCamelCase							:Any       = {value: key for key, value in MORSE_CODE_DICT.items()}



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(MORSE_CODE_DICT[char] for char in message.upper()   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return "".join(REVERSE_DICT[char] for char in message.split()   )



def      a      (			):
			'''simple docstring'''





			A_     :						List[str]     =					"""Morse code here!"""
			print(lowerCamelCase__   )
			A_     :						str     =					encrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )
			A_     :						Dict     =					decrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )


if __name__ == "__main__":
						main()