


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 							__lowercase     (				__lowerCAmelCase							:       Optional[int]     ):
							a__    =  3_8_4
							if "tiny" in model_name:
														a__    =  [3, 3, 9, 3]
														a__    =  [9_6, 1_9_2, 3_8_4, 7_6_8]
							if "small" in model_name:
														a__    =  [3, 3, 2_7, 3]
														a__    =  [9_6, 1_9_2, 3_8_4, 7_6_8]
							if "base" in model_name:
														a__    =  [3, 3, 2_7, 3]
														a__    =  [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
														a__    =  5_1_2
							if "large" in model_name:
														a__    =  [3, 3, 2_7, 3]
														a__    =  [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
														a__    =  7_6_8
							if "xlarge" in model_name:
														a__    =  [3, 3, 2_7, 3]
														a__    =  [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
														a__    =  1_0_2_4

							# set label information
							a__    =  1_5_0
							a__    =  'huggingface/label-files'
							a__    =  'ade20k-id2label.json'
							a__    =  json.load(open(hf_hub_download(snake_case__      ,					snake_case__      ,					repo_type='dataset'     )      ,					'r'     )     )
							a__    =  {int(snake_case__     ): v for k, v in idalabel.items()}
							a__    =  {v: k for k, v in idalabel.items()}

							a__    =  ConvNextConfig(
							    depths=snake_case__      ,					hidden_sizes=snake_case__      ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
							a__    =  UperNetConfig(
							    backbone_config=snake_case__      ,					auxiliary_in_channels=snake_case__      ,					num_labels=snake_case__      ,					idalabel=snake_case__      ,					labelaid=snake_case__      ,					)

							return config






def 							__lowercase     (				__lowerCAmelCase							:       Any     ):
							a__    =  []

							# 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 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       Any     ):
							a__    =  dct.pop(snake_case__     )
							a__    =  val






def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Tuple     ):
							a__    =  {
							    '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',
							}
							a__    =  model_name_to_url[model_name]
							a__    =  torch.hub.load_state_dict_from_url(snake_case__      ,					map_location='cpu'     )['state_dict']

							a__    =  get_upernet_config(snake_case__     )
							a__    =  UperNetForSemanticSegmentation(snake_case__     )
							model.eval()

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

							# rename keys
							a__    =  create_rename_keys(snake_case__     )
							for src, dest in rename_keys:
														rename_key(snake_case__      ,					snake_case__      ,					snake_case__     )

							model.load_state_dict(snake_case__     )

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

							a__    =  SegformerImageProcessor()
							a__    =  processor(snake_case__      ,					return_tensors='pt'     ).pixel_values

							with torch.no_grad():
														a__    =  model(snake_case__     )

							if model_name == "upernet-convnext-tiny":
														a__    =  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":
														a__    =  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":
														a__    =  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":
														a__    =  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":
														a__    =  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]      ,					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(snake_case__     )
														print(F'Saving processor to {pytorch_dump_folder_path}'     )
														processor.save_pretrained(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__":
						snake_case							: str      =   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.'''
						)

						snake_case							: Any      =   parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if length <= 0 or not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise ValueError('Length must be a positive integer.'     )
							return [n * (2 * n - 1) for n in range(__lowerCAmelCase     )]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))




