




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					3_84
			if "tiny" in model_name:
						SCREAMING_SNAKE_CASE           =					[3, 3, 9, 3]
						SCREAMING_SNAKE_CASE           =					[96, 1_92, 3_84, 7_68]
			if "small" in model_name:
						SCREAMING_SNAKE_CASE           =					[3, 3, 27, 3]
						SCREAMING_SNAKE_CASE           =					[96, 1_92, 3_84, 7_68]
			if "base" in model_name:
						SCREAMING_SNAKE_CASE           =					[3, 3, 27, 3]
						SCREAMING_SNAKE_CASE           =					[1_28, 2_56, 5_12, 10_24]
						SCREAMING_SNAKE_CASE           =					5_12
			if "large" in model_name:
						SCREAMING_SNAKE_CASE           =					[3, 3, 27, 3]
						SCREAMING_SNAKE_CASE           =					[1_92, 3_84, 7_68, 15_36]
						SCREAMING_SNAKE_CASE           =					7_68
			if "xlarge" in model_name:
						SCREAMING_SNAKE_CASE           =					[3, 3, 27, 3]
						SCREAMING_SNAKE_CASE           =					[2_56, 5_12, 10_24, 20_48]
						SCREAMING_SNAKE_CASE           =					10_24

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

			SCREAMING_SNAKE_CASE           =					ConvNextConfig(
			    depths=lowerCamelCase_					,	hidden_sizes=lowerCamelCase_					,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   )
			SCREAMING_SNAKE_CASE           =					UperNetConfig(
			    backbone_config=lowerCamelCase_					,	auxiliary_in_channels=lowerCamelCase_					,	num_labels=lowerCamelCase_					,	idalabel=lowerCamelCase_					,	labelaid=lowerCamelCase_					,	)

			return config
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]

			# fmt: off
			# stem
			rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")   )
			rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")   )
			rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")   )
			rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.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}.{j}.gamma""", f"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.norm.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.norm.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""")   )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""")   )
						if i > 0:
									rename_keys.append((f"""backbone.downsample_layers.{i}.0.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""")   )
									rename_keys.append((f"""backbone.downsample_layers.{i}.0.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""")   )
									rename_keys.append((f"""backbone.downsample_layers.{i}.1.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""")   )
									rename_keys.append((f"""backbone.downsample_layers.{i}.1.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					dct.pop(lowerCamelCase_   )
			SCREAMING_SNAKE_CASE           =					val
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{
			    'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
			    'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
			    'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
			    'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
			    'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
			}
			SCREAMING_SNAKE_CASE           =					model_name_to_url[model_name]
			SCREAMING_SNAKE_CASE           =					torch.hub.load_state_dict_from_url(lowerCamelCase_					,	map_location="""cpu"""   )['state_dict']

			SCREAMING_SNAKE_CASE           =					get_upernet_config(lowerCamelCase_   )
			SCREAMING_SNAKE_CASE           =					UperNetForSemanticSegmentation(lowerCamelCase_   )
			model.eval()

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

			# rename keys
			SCREAMING_SNAKE_CASE           =					create_rename_keys(lowerCamelCase_   )
			for src, dest in rename_keys:
						rename_key(lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_   )

			model.load_state_dict(lowerCamelCase_   )

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

			SCREAMING_SNAKE_CASE           =					SegformerImageProcessor()
			SCREAMING_SNAKE_CASE           =					processor(lowerCamelCase_					,	return_tensors="""pt"""   ).pixel_values

			with torch.no_grad():
						SCREAMING_SNAKE_CASE           =					model(lowerCamelCase_   )

			if model_name == "upernet-convnext-tiny":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]   )
			elif model_name == "upernet-convnext-small":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]   )
			elif model_name == "upernet-convnext-base":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]   )
			elif model_name == "upernet-convnext-large":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]   )
			elif model_name == "upernet-convnext-xlarge":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]   )
			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						:							List[Any]							=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default="""upernet-convnext-tiny""",
		    type=str,
		    choices=[f'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
		    help="""Name of the ConvNext 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						:							int							=  parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









from __future__ import annotations
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			create_state_space_tree(SCREAMING_SNAKE_CASE					,	[]					,	0					,	[0 for i in range(len(SCREAMING_SNAKE_CASE   )   )]   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	):
			'''simple docstring'''

			if index == len(SCREAMING_SNAKE_CASE   ):
						print(SCREAMING_SNAKE_CASE   )
						return

			for i in range(len(SCREAMING_SNAKE_CASE   )   ):
						if not index_used[i]:
									current_sequence.append(sequence[i]   )
									SCREAMING_SNAKE_CASE           =					True
									create_state_space_tree(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	index + 1					,	SCREAMING_SNAKE_CASE   )
									current_sequence.pop()
									SCREAMING_SNAKE_CASE           =					False
__A						:							list[int | str]							=  [3, 1, 2, 4]
generate_all_permutations(sequence)

__A						:							list[int | str]							=  ["A", "B", "C"]




generate_all_permutations(sequence_a)






