
'''simple docstring'''



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				(__lowerCamelCase				:List[Any]		):
						_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(__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  						=			SwinConfig(
						    embed_dim=__lowerCamelCase			,     depths=__lowerCamelCase			,     num_heads=__lowerCamelCase			,     window_size=__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				:Dict		):
						_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 A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:str			,     __lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase  						=			dct.pop(__lowerCamelCase		)
						_lowerCAmelCase  						=			val


def A				(__lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:List[str]		):
						_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 A				(__lowerCamelCase				:List[Any]		):
						_lowerCAmelCase						,   _lowerCAmelCase  						=			x.shape
						_lowerCAmelCase  						=			x.reshape(__lowerCamelCase			,     4			,     in_channel // 4		)
						_lowerCAmelCase  						=			x[:, [0, 2, 1, 3], :].transpose(1			,     2		).reshape(__lowerCamelCase			,     __lowerCamelCase		)
						return x


def A				(__lowerCamelCase				:Dict		):
						_lowerCAmelCase						,   _lowerCAmelCase  						=			x.shape
						_lowerCAmelCase  						=			x.reshape(__lowerCamelCase			,     in_channel // 4			,     4		)
						_lowerCAmelCase  						=			x[:, :, [0, 2, 1, 3]].transpose(1			,     2		).reshape(__lowerCamelCase			,     __lowerCamelCase		)

						return x


def A				(__lowerCamelCase				:Dict		):
						_lowerCAmelCase  						=			x.shape[0]
						_lowerCAmelCase  						=			x.reshape(4			,     in_channel // 4		)
						_lowerCAmelCase  						=			x[[0, 2, 1, 3], :].transpose(0			,     1		).reshape(__lowerCamelCase		)
						return x


def A				(__lowerCamelCase				:Any		):
						_lowerCAmelCase  						=			x.shape[0]
						_lowerCAmelCase  						=			x.reshape(in_channel // 4			,     4		)
						_lowerCAmelCase  						=			x[:, [0, 2, 1, 3]].transpose(0			,     1		).reshape(__lowerCamelCase		)
						return x


def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Union[str, Any]		):
						_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(__lowerCamelCase			,     map_location="""cpu"""			,     file_name=__lowerCamelCase		)[
						    """state_dict"""
						]

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

						_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		)
						read_in_q_k_v(__lowerCamelCase			,     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(__lowerCamelCase		)
																		if "norm" in key:
																								_lowerCAmelCase  						=			reverse_correct_unfold_norm_order(__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		)
												_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(
												    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]		)
						elif model_name == "upernet-swin-small":
												_lowerCAmelCase  						=			torch.tensor(
												    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]		)
						elif model_name == "upernet-swin-base":
												_lowerCAmelCase  						=			torch.tensor(
												    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]		)
						elif model_name == "upernet-swin-large":
												_lowerCAmelCase  						=			torch.tensor(
												    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]		)
						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__":
						lowerCamelCase_    =		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_    =		parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


'''simple docstring'''



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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

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

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




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



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

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

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

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

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).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
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
