



import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation







def     UpperCAmelCase_		(					snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  384
				lowerCAmelCase__		  =  7
				if "tiny" in model_name:
								lowerCAmelCase__		  =  96
								lowerCAmelCase__		  =  (2, 2, 6, 2)
								lowerCAmelCase__		  =  (3, 6, 12, 24)
				elif "small" in model_name:
								lowerCAmelCase__		  =  96
								lowerCAmelCase__		  =  (2, 2, 18, 2)
								lowerCAmelCase__		  =  (3, 6, 12, 24)
				elif "base" in model_name:
								lowerCAmelCase__		  =  128
								lowerCAmelCase__		  =  (2, 2, 18, 2)
								lowerCAmelCase__		  =  (4, 8, 16, 32)
								lowerCAmelCase__		  =  12
								lowerCAmelCase__		  =  512
				elif "large" in model_name:
								lowerCAmelCase__		  =  192
								lowerCAmelCase__		  =  (2, 2, 18, 2)
								lowerCAmelCase__		  =  (6, 12, 24, 48)
								lowerCAmelCase__		  =  12
								lowerCAmelCase__		  =  768

				# set label information
				lowerCAmelCase__		  =  150
				lowerCAmelCase__		  =  'huggingface/label-files'
				lowerCAmelCase__		  =  'ade20k-id2label.json'
				lowerCAmelCase__		  =  json.load(open(hf_hub_download(snake_case__    ,			snake_case__    ,			repo_type='dataset'					)    ,			'r'					)					)
				lowerCAmelCase__		  =  {int(snake_case__					): v for k, v in idalabel.items()}
				lowerCAmelCase__		  =  {v: k for k, v in idalabel.items()}

				lowerCAmelCase__		  =  SwinConfig(
				    embed_dim=snake_case__    ,			depths=snake_case__    ,			num_heads=snake_case__    ,			window_size=snake_case__    ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,			)
				lowerCAmelCase__		  =  UperNetConfig(
				    backbone_config=snake_case__    ,			auxiliary_in_channels=snake_case__    ,			num_labels=snake_case__    ,			idalabel=snake_case__    ,			labelaid=snake_case__    ,			)

				return config







def     UpperCAmelCase_		(					snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  []

				# fmt: off
				# stem
				rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')					)
				rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')					)
				rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')					)
				rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.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}.blocks.{j}.norm1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight')					)
												rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias')					)

								if i < 3:
												rename_keys.append((f'backbone.stages.{i}.downsample.reduction.weight', f'backbone.encoder.layers.{i}.downsample.reduction.weight')					)
												rename_keys.append((f'backbone.stages.{i}.downsample.norm.weight', f'backbone.encoder.layers.{i}.downsample.norm.weight')					)
												rename_keys.append((f'backbone.stages.{i}.downsample.norm.bias', f'backbone.encoder.layers.{i}.downsample.norm.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     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  dct.pop(snake_case__					)
				lowerCAmelCase__		  =  val







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
				for i in range(len(backbone_config.depths					)					):
								lowerCAmelCase__		  =  num_features[i]
								for j in range(backbone_config.depths[i]					):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												lowerCAmelCase__		  =  state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'					)
												lowerCAmelCase__		  =  state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'					)
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__		  =  in_proj_weight[:dim, :]
												lowerCAmelCase__		  =  in_proj_bias[: dim]
												lowerCAmelCase__		  =  in_proj_weight[
												    dim : dim * 2, :
												]
												lowerCAmelCase__		  =  in_proj_bias[
												    dim : dim * 2
												]
												lowerCAmelCase__		  =  in_proj_weight[
												    -dim :, :
												]
												lowerCAmelCase__		  =  in_proj_bias[-dim :]
												# fmt: on







def     UpperCAmelCase_		(					snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  x.shape
				lowerCAmelCase__		  =  x.reshape(snake_case__    ,			4    ,			in_channel // 4					)
				lowerCAmelCase__		  =  x[:, [0, 2, 1, 3], :].transpose(1    ,			2					).reshape(snake_case__    ,			snake_case__					)
				return x







def     UpperCAmelCase_		(					snake_case__					)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  x.shape
				lowerCAmelCase__		  =  x.reshape(snake_case__    ,			in_channel // 4    ,			4					)
				lowerCAmelCase__		  =  x[:, :, [0, 2, 1, 3]].transpose(1    ,			2					).reshape(snake_case__    ,			snake_case__					)

				return x







def     UpperCAmelCase_		(					snake_case__					)	->     Tuple:



				"""simple docstring"""





				lowerCAmelCase__		  =  x.shape[0]
				lowerCAmelCase__		  =  x.reshape(4    ,			in_channel // 4					)
				lowerCAmelCase__		  =  x[[0, 2, 1, 3], :].transpose(0    ,			1					).reshape(snake_case__					)
				return x







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  x.shape[0]
				lowerCAmelCase__		  =  x.reshape(in_channel // 4    ,			4					)
				lowerCAmelCase__		  =  x[:, [0, 2, 1, 3]].transpose(0    ,			1					).reshape(snake_case__					)
				return x







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  {
				    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
				    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
				    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
				    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
				}
				lowerCAmelCase__		  =  model_name_to_url[model_name]
				lowerCAmelCase__		  =  torch.hub.load_state_dict_from_url(snake_case__    ,			map_location='cpu'    ,			file_name=snake_case__					)[
				    'state_dict'
				]

				for name, param in state_dict.items():
								print(snake_case__    ,			param.shape					)

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

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

				# rename keys
				lowerCAmelCase__		  =  create_rename_keys(snake_case__					)
				for src, dest in rename_keys:
								rename_key(snake_case__    ,			snake_case__    ,			snake_case__					)
				read_in_q_k_v(snake_case__    ,			config.backbone_config					)

				# fix downsample parameters
				for key, value in state_dict.items():
								if "downsample" in key:
												if "reduction" in key:
																lowerCAmelCase__		  =  reverse_correct_unfold_reduction_order(snake_case__					)
												if "norm" in key:
																lowerCAmelCase__		  =  reverse_correct_unfold_norm_order(snake_case__					)

				model.load_state_dict(snake_case__					)

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

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

				with torch.no_grad():
								lowerCAmelCase__		  =  model(snake_case__					)
								lowerCAmelCase__		  =  outputs.logits

				print(logits.shape					)
				print('First values of logits:'    ,			logits[0, 0, :3, :3]					)
				# assert values
				if model_name == "upernet-swin-tiny":
								lowerCAmelCase__		  =  torch.tensor(
								    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]					)
				elif model_name == "upernet-swin-small":
								lowerCAmelCase__		  =  torch.tensor(
								    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]					)
				elif model_name == "upernet-swin-base":
								lowerCAmelCase__		  =  torch.tensor(
								    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]					)
				elif model_name == "upernet-swin-large":
								lowerCAmelCase__		  =  torch.tensor(
								    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]					)
				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__":
	_lowerCAmelCase	:   List[str]    =   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="upernet-swin-tiny",
	    type=str,
	    choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
	    help="Name of the Swin + 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[str]    =   parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
