





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    A				(							UpperCAmelCase				):
					_snake_case       :					List[Any]				       =   384
					if "tiny" in model_name:
										_snake_case       :					Tuple				       =   [3, 3, 9, 3]
										_snake_case       :					List[str]				       =   [96, 192, 384, 768]
					if "small" in model_name:
										_snake_case       :					List[str]				       =   [3, 3, 27, 3]
										_snake_case       :					List[str]				       =   [96, 192, 384, 768]
					if "base" in model_name:
										_snake_case       :					Union[str, Any]				       =   [3, 3, 27, 3]
										_snake_case       :					List[str]				       =   [128, 256, 512, 1_024]
										_snake_case       :					int				       =   512
					if "large" in model_name:
										_snake_case       :					List[Any]				       =   [3, 3, 27, 3]
										_snake_case       :					Any				       =   [192, 384, 768, 1_536]
										_snake_case       :					Dict				       =   768
					if "xlarge" in model_name:
										_snake_case       :					Dict				       =   [3, 3, 27, 3]
										_snake_case       :					Union[str, Any]				       =   [256, 512, 1_024, 2_048]
										_snake_case       :					Dict				       =   1_024

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

					_snake_case       :					int				       =   ConvNextConfig(
					    depths=UpperCAmelCase  ,  hidden_sizes=UpperCAmelCase  ,  out_features=["stage1", "stage2", "stage3", "stage4"]				)
					_snake_case       :					Optional[Any]				       =   UperNetConfig(
					    backbone_config=UpperCAmelCase  ,  auxiliary_in_channels=UpperCAmelCase  ,  num_labels=UpperCAmelCase  ,  idalabel=UpperCAmelCase  ,  labelaid=UpperCAmelCase  ,  )

					return config







def    A				(							UpperCAmelCase				):
					_snake_case       :					List[str]				       =   []

					# 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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   dct.pop(UpperCAmelCase				)
					_snake_case       :					List[Any]				       =   val







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Dict				       =   {
					    "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",
					}
					_snake_case       :					Dict				       =   model_name_to_url[model_name]
					_snake_case       :					Optional[Any]				       =   torch.hub.load_state_dict_from_url(UpperCAmelCase  ,  map_location="cpu"				)["state_dict"]

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

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

					# rename keys
					_snake_case       :					Optional[int]				       =   create_rename_keys(UpperCAmelCase				)
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					model.load_state_dict(UpperCAmelCase				)

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

					_snake_case       :					Tuple				       =   SegformerImageProcessor()
					_snake_case       :					List[Any]				       =   processor(UpperCAmelCase  ,  return_tensors="pt"				).pixel_values

					with torch.no_grad():
										_snake_case       :					List[str]				       =   model(UpperCAmelCase				)

					if model_name == "upernet-convnext-tiny":
										_snake_case       :					Optional[Any]				       =   torch.tensor(
										    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]				)
					elif model_name == "upernet-convnext-small":
										_snake_case       :					Tuple				       =   torch.tensor(
										    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]				)
					elif model_name == "upernet-convnext-base":
										_snake_case       :					str				       =   torch.tensor(
										    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]				)
					elif model_name == "upernet-convnext-large":
										_snake_case       :					List[str]				       =   torch.tensor(
										    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]				)
					elif model_name == "upernet-convnext-xlarge":
										_snake_case       :					Tuple				       =   torch.tensor(
										    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]				)
					print("Logits:"  ,  outputs.logits[0, 0, :3, :3]				)
					assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,  UpperCAmelCase  ,  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(UpperCAmelCase				)
										print(F"""Saving processor to {pytorch_dump_folder_path}"""				)
										processor.save_pretrained(UpperCAmelCase				)

					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 :Union[str, 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.'
							)

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






from math import factorial

__lowerCAmelCase :dict[str, int]     =    {str(digit): factorial(digit) for digit in range(10)}







def    A				(							UpperCAmelCase				):
					if not isinstance(UpperCAmelCase  ,  UpperCAmelCase				):
										raise TypeError("Parameter number must be int"				)

					if number < 0:
										raise ValueError("Parameter number must be greater than or equal to 0"				)

					# Converts number in string to iterate on its digits and adds its factorial.
					return sum(DIGIT_FACTORIAL[digit] for digit in str(UpperCAmelCase				)				)







def    A				(							UpperCAmelCase = 60  ,  UpperCAmelCase = 1_000_000				):

					if not isinstance(UpperCAmelCase  ,  UpperCAmelCase				) or not isinstance(UpperCAmelCase  ,  UpperCAmelCase				):
										raise TypeError("Parameters chain_length and number_limit must be int"				)

					if chain_length <= 0 or number_limit <= 0:
										raise ValueError(
										    "Parameters chain_length and number_limit must be greater than 0"				)

					# the counter for the chains with the exact desired length
					_snake_case       :					Optional[int]				       =   0
					# the cached sizes of the previous chains
					_snake_case       :					dict[int, int]				       =   {}

					for start_chain_element in range(1  ,  UpperCAmelCase				):
										# The temporary set will contain the elements of the chain
										_snake_case       :					Union[str, Any]				       =   set()
										_snake_case       :					Tuple				       =   0

										# Stop computing the chain when you find a cached size, a repeating item or the
										# length is greater then the desired one.
										_snake_case       :					str				       =   start_chain_element
										while (
										    chain_element not in chain_sets_lengths
										    and chain_element not in chain_set
										    and chain_set_length <= chain_length
										):
															chain_set.add(UpperCAmelCase				)
															chain_set_length += 1
															_snake_case       :					Optional[Any]				       =   digit_factorial_sum(UpperCAmelCase				)

										if chain_element in chain_sets_lengths:
															chain_set_length += chain_sets_lengths[chain_element]

										_snake_case       :					List[Any]				       =   chain_set_length

										# If chain contains the exact amount of elements increase the counter
										if chain_set_length == chain_length:
															chains_counter += 1

					return chains_counter


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'''{solution()}''')