
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 	A_ (							snake_case							:   int ):



						'''simple docstring'''

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

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

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

						return config




def 	A_ (							snake_case							:   Tuple ):



						'''simple docstring'''

						__UpperCamelCase       =			[]

						# 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 	A_ (							snake_case							:   List[Any] ,      snake_case							:   List[Any] ,      snake_case							:   Union[str, Any] ):



						'''simple docstring'''

						__UpperCamelCase       =			dct.pop(snake_case )
						__UpperCamelCase       =			val




def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   str ):



						'''simple docstring'''

						__UpperCamelCase       =			[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
						for i in range(len(backbone_config.depths ) ):
												__UpperCamelCase       =			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)
																		__UpperCamelCase       =			state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight" )
																		__UpperCamelCase       =			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
																		__UpperCamelCase       =			in_proj_weight[:dim, :]
																		__UpperCamelCase       =			in_proj_bias[: dim]
																		__UpperCamelCase       =			in_proj_weight[
																		    dim : dim * 2, :
																		]
																		__UpperCamelCase       =			in_proj_bias[
																		    dim : dim * 2
																		]
																		__UpperCamelCase       =			in_proj_weight[
																		    -dim :, :
																		]
																		__UpperCamelCase       =			in_proj_bias[-dim :]
																		# fmt: on




def 	A_ (							snake_case							:   Tuple ):



						'''simple docstring'''

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




def 	A_ (							snake_case							:   List[str] ):



						'''simple docstring'''

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

						return x




def 	A_ (							snake_case							:   Tuple ):



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Any ):



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Union[str, Any] ,      snake_case							:   Any ,      snake_case							:   int ):



						'''simple docstring'''

						__UpperCamelCase       =			{
						    '''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''',
						}
						__UpperCamelCase       =			model_name_to_url[model_name]
						__UpperCamelCase       =			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 )

						__UpperCamelCase       =			get_upernet_config(snake_case )
						__UpperCamelCase       =			UperNetForSemanticSegmentation(snake_case )
						model.eval()

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

						# rename keys
						__UpperCamelCase       =			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:
																								__UpperCamelCase       =			reverse_correct_unfold_reduction_order(snake_case )
																		if "norm" in key:
																								__UpperCamelCase       =			reverse_correct_unfold_norm_order(snake_case )

						model.load_state_dict(snake_case )

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

						__UpperCamelCase       =			SegformerImageProcessor()
						__UpperCamelCase       =			processor(snake_case ,      return_tensors='''pt''' ).pixel_values

						with torch.no_grad():
												__UpperCamelCase       =			model(snake_case )
												__UpperCamelCase       =			outputs.logits

						print(logits.shape )
						print('''First values of logits:''' ,      logits[0, 0, :3, :3] )
						# assert values
						if model_name == "upernet-swin-tiny":
												__UpperCamelCase       =			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":
												__UpperCamelCase       =			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":
												__UpperCamelCase       =			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":
												__UpperCamelCase       =			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__":
				lowercase__							:							Any									=			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."
				)

				lowercase__							:							str									=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													# make sure scheduler can always be converted to DDIM
													__UpperCamelCase       =			DDIMScheduler.from_config(scheduler.config			)

													self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_			)



							@torch.no_grad()
							def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[ImagePipelineOutput, Tuple]:





													'''simple docstring'''






													if isinstance(self.unet.config.sample_size     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			(
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			__UpperCamelCase       =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) != batch_size:
																			raise ValueError(
																			    F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_			)}, but requested an effective batch"
																			    F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

													__UpperCamelCase       =			randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=self.device     ,	dtype=self.unet.dtype			)

													# set step values
													self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

													for t in self.progress_bar(self.scheduler.timesteps			):
																			# 1. predict noise model_output
																			__UpperCamelCase       =			self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			__UpperCamelCase       =			self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	eta=SCREAMING_SNAKE_CASE_     ,	use_clipped_model_output=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_			).prev_sample

													__UpperCamelCase       =			(image / 2 + 0.5).clamp(0     ,	1			)
													__UpperCamelCase       =			image.cpu().permute(0     ,	2     ,	3     ,	1			).numpy()
													if output_type == "pil":
																			__UpperCamelCase       =			self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)




