


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__       (       __lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			384
			lowerCAmelCase_      			=			7
			if "tiny" in model_name:
						lowerCAmelCase_      			=			96
						lowerCAmelCase_      			=			(2, 2, 6, 2)
						lowerCAmelCase_      			=			(3, 6, 12, 24)
			elif "small" in model_name:
						lowerCAmelCase_      			=			96
						lowerCAmelCase_      			=			(2, 2, 18, 2)
						lowerCAmelCase_      			=			(3, 6, 12, 24)
			elif "base" in model_name:
						lowerCAmelCase_      			=			128
						lowerCAmelCase_      			=			(2, 2, 18, 2)
						lowerCAmelCase_      			=			(4, 8, 16, 32)
						lowerCAmelCase_      			=			12
						lowerCAmelCase_      			=			512
			elif "large" in model_name:
						lowerCAmelCase_      			=			192
						lowerCAmelCase_      			=			(2, 2, 18, 2)
						lowerCAmelCase_      			=			(6, 12, 24, 48)
						lowerCAmelCase_      			=			12
						lowerCAmelCase_      			=			768

			# set label information
			lowerCAmelCase_      			=			150
			lowerCAmelCase_      			=			'''huggingface/label-files'''
			lowerCAmelCase_      			=			'''ade20k-id2label.json'''
			lowerCAmelCase_      			=			json.load(open(hf_hub_download(__lowerCAmelCase					,							__lowerCAmelCase					,							repo_type="dataset"       )					,							"r"       )       )
			lowerCAmelCase_      			=			{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
			lowerCAmelCase_      			=			{v: k for k, v in idalabel.items()}

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

			return config






def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]

			# 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__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						Dict       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			dct.pop(__lowerCAmelCase       )
			lowerCAmelCase_      			=			val






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
			for i in range(len(backbone_config.depths       )       ):
						lowerCAmelCase_      			=			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)
									lowerCAmelCase_      			=			state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""       )
									lowerCAmelCase_      			=			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
									lowerCAmelCase_      			=			in_proj_weight[:dim, :]
									lowerCAmelCase_      			=			in_proj_bias[: dim]
									lowerCAmelCase_      			=			in_proj_weight[
									    dim : dim * 2, :
									]
									lowerCAmelCase_      			=			in_proj_bias[
									    dim : dim * 2
									]
									lowerCAmelCase_      			=			in_proj_weight[
									    -dim :, :
									]
									lowerCAmelCase_      			=			in_proj_bias[-dim :]
									# fmt: on






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			x.shape
			lowerCAmelCase_      			=			x.reshape(__lowerCAmelCase					,							4					,							in_channel // 4       )
			lowerCAmelCase_      			=			x[:, [0, 2, 1, 3], :].transpose(1					,							2       ).reshape(__lowerCAmelCase					,							__lowerCAmelCase       )
			return x






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			x.shape
			lowerCAmelCase_      			=			x.reshape(__lowerCAmelCase					,							in_channel // 4					,							4       )
			lowerCAmelCase_      			=			x[:, :, [0, 2, 1, 3]].transpose(1					,							2       ).reshape(__lowerCAmelCase					,							__lowerCAmelCase       )

			return x






def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			x.shape[0]
			lowerCAmelCase_      			=			x.reshape(4					,							in_channel // 4       )
			lowerCAmelCase_      			=			x[[0, 2, 1, 3], :].transpose(0					,							1       ).reshape(__lowerCAmelCase       )
			return x






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			x.shape[0]
			lowerCAmelCase_      			=			x.reshape(in_channel // 4					,							4       )
			lowerCAmelCase_      			=			x[:, [0, 2, 1, 3]].transpose(0					,							1       ).reshape(__lowerCAmelCase       )
			return x






def      lowerCamelCase__       (       __lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			{
			    '''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''',
			}
			lowerCAmelCase_      			=			model_name_to_url[model_name]
			lowerCAmelCase_      			=			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       )

			lowerCAmelCase_      			=			get_upernet_config(__lowerCAmelCase       )
			lowerCAmelCase_      			=			UperNetForSemanticSegmentation(__lowerCAmelCase       )
			model.eval()

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

			# rename keys
			lowerCAmelCase_      			=			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:
												lowerCAmelCase_      			=			reverse_correct_unfold_reduction_order(__lowerCAmelCase       )
									if "norm" in key:
												lowerCAmelCase_      			=			reverse_correct_unfold_norm_order(__lowerCAmelCase       )

			model.load_state_dict(__lowerCAmelCase       )

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

			lowerCAmelCase_      			=			SegformerImageProcessor()
			lowerCAmelCase_      			=			processor(__lowerCAmelCase					,							return_tensors="pt"       ).pixel_values

			with torch.no_grad():
						lowerCAmelCase_      			=			model(__lowerCAmelCase       )
						lowerCAmelCase_      			=			outputs.logits

			print(logits.shape       )
			print("First values of logits:"					,							logits[0, 0, :3, :3]       )
			# assert values
			if model_name == "upernet-swin-tiny":
						lowerCAmelCase_      			=			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":
						lowerCAmelCase_      			=			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":
						lowerCAmelCase_      			=			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":
						lowerCAmelCase_      			=			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__":
			_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)





from string import ascii_lowercase, ascii_uppercase






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			if not sentence:
						return ""
			lowerCAmelCase_      			=			dict(zip(__lowerCAmelCase					,							__lowerCAmelCase       )       )
			return lower_to_upper.get(sentence[0]					,							sentence[0]       ) + sentence[1:]


if __name__ == "__main__":
			from doctest import testmod

			testmod()



