
'''simple docstring'''
from math import factorial







def 		A_(     A						:		int = 20):
							UpperCamelCase   			=     2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
							# 2, 3,...
							UpperCamelCase   			=     n // 2

							return int(factorial(A) / (factorial(A) * factorial(n - k)))


if __name__ == "__main__":
						import sys

						if len(sys.argv) == 1:
												print(solution(20))
						else:
												try:
																		lowerCAmelCase					:    int           = int(sys.argv[1])
																		print(solution(n))
												except ValueError:
																		print('Invalid entry - please enter a number.')






'''simple docstring'''



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

						# 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    		=					ConvNextConfig(
						    depths=__lowerCamelCase					,      hidden_sizes=__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 A (__lowerCamelCase :Optional[Any]						):
						_lowerCAmelCase    		=					[]

						# 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 (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Tuple						):
						_lowerCAmelCase    		=					dct.pop(__lowerCamelCase						)
						_lowerCAmelCase    		=					val


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any						):
						_lowerCAmelCase    		=					{
						    """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""",
						}
						_lowerCAmelCase    		=					model_name_to_url[model_name]
						_lowerCAmelCase    		=					torch.hub.load_state_dict_from_url(__lowerCamelCase					,      map_location="""cpu"""						)["""state_dict"""]

						_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						)

						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						)

						if model_name == "upernet-convnext-tiny":
												_lowerCAmelCase    		=					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":
												_lowerCAmelCase    		=					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":
												_lowerCAmelCase    		=					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":
												_lowerCAmelCase    		=					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":
												_lowerCAmelCase    		=					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__":
						_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)
