



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_  ):
			lowercase__					  =				384
			if "tiny" in model_name:
						lowercase__					  =				[3, 3, 9, 3]
						lowercase__					  =				[96, 192, 384, 768]
			if "small" in model_name:
						lowercase__					  =				[3, 3, 27, 3]
						lowercase__					  =				[96, 192, 384, 768]
			if "base" in model_name:
						lowercase__					  =				[3, 3, 27, 3]
						lowercase__					  =				[128, 256, 512, 1024]
						lowercase__					  =				512
			if "large" in model_name:
						lowercase__					  =				[3, 3, 27, 3]
						lowercase__					  =				[192, 384, 768, 1536]
						lowercase__					  =				768
			if "xlarge" in model_name:
						lowercase__					  =				[3, 3, 27, 3]
						lowercase__					  =				[256, 512, 1024, 2048]
						lowercase__					  =				1024

			# set label information
			lowercase__					  =				150
			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__					  =				ConvNextConfig(
			    depths=SCREAMING_SNAKE_CASE_						,  hidden_sizes=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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				[]

			# 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_  ):
			lowercase__					  =				dct.pop(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				val



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				{
			    "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",
			}
			lowercase__					  =				model_name_to_url[model_name]
			lowercase__					  =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_						,  map_location="cpu"  )["state_dict"]

			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_  )

			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_  )

			if model_name == "upernet-convnext-tiny":
						lowercase__					  =				torch.tensor(
						    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]  )
			elif model_name == "upernet-convnext-small":
						lowercase__					  =				torch.tensor(
						    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]  )
			elif model_name == "upernet-convnext-base":
						lowercase__					  =				torch.tensor(
						    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]  )
			elif model_name == "upernet-convnext-large":
						lowercase__					  =				torch.tensor(
						    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]  )
			elif model_name == "upernet-convnext-xlarge":
						lowercase__					  =				torch.tensor(
						    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]  )
			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__":
							lowercase_								=     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."""
							)

							lowercase_								=     parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import math



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			if 0 not in (x, y):
						# We use the relation x^y = y*log10(x), where 10 is the base.
						return y * math.logaa(SCREAMING_SNAKE_CASE_  )
			else:
						if x == 0:  # 0 raised to any number is 0
									return 0
						elif y == 0:
									return 1  # any number raised to 0 is 1
			raise AssertionError("This should never happen"  )


if __name__ == "__main__":  # Main function
							# Read two numbers from input and typecast them to int using map function.
							# Here x is the base and y is the power.
							lowercase_								=     """Enter the base and the power separated by a comma: """
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))

							# We find the log of each number, using the function res(), which takes two
							# arguments.
							lowercase_								=     res(xa, ya)
							lowercase_								=     res(xa, ya)

							# We check for the largest number
							if resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							elif resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							else:
														print("""Both are equal""")




