


"""simple docstring"""


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					torch.FloatTensor
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a=3					,     __a=3					,     __a=("DownEncoderBlock2D",)					,     __a=(64,)					,     __a=2					,     __a=32					,     __a="silu"					,     __a=True					,     ):
										super().__init__()
										__lowerCAmelCase       					=    layers_per_block

										__lowerCAmelCase       					=    torch.nn.Convad(
										    __a					,     block_out_channels[0]					,     kernel_size=3					,     stride=1					,     padding=1					,     )

										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    nn.ModuleList([]			)

										# down
										__lowerCAmelCase       					=    block_out_channels[0]
										for i, down_block_type in enumerate(__a			):
													__lowerCAmelCase       					=    output_channel
													__lowerCAmelCase       					=    block_out_channels[i]
													__lowerCAmelCase       					=    i == len(__a			) - 1

													__lowerCAmelCase       					=    get_down_block(
													    __a					,     num_layers=self.layers_per_block					,     in_channels=__a					,     out_channels=__a					,     add_downsample=not is_final_block					,     resnet_eps=1e-6					,     downsample_padding=0					,     resnet_act_fn=__a					,     resnet_groups=__a					,     attention_head_dim=__a					,     temb_channels=__a					,     )
													self.down_blocks.append(__a			)

										# mid
										__lowerCAmelCase       					=    UNetMidBlockaD(
										    in_channels=block_out_channels[-1]					,     resnet_eps=1e-6					,     resnet_act_fn=__a					,     output_scale_factor=1					,     resnet_time_scale_shift="default"					,     attention_head_dim=block_out_channels[-1]					,     resnet_groups=__a					,     temb_channels=__a					,     )

										# out
										__lowerCAmelCase       					=    nn.GroupNorm(num_channels=block_out_channels[-1]					,     num_groups=__a					,     eps=1e-6			)
										__lowerCAmelCase       					=    nn.SiLU()

										__lowerCAmelCase       					=    2 * out_channels if double_z else out_channels
										__lowerCAmelCase       					=    nn.Convad(block_out_channels[-1]					,     __a					,     3					,     padding=1			)

										__lowerCAmelCase       					=    False



							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    x
										__lowerCAmelCase       					=    self.conv_in(__a			)

										if self.training and self.gradient_checkpointing:

													def create_custom_forward(__a			):
																def custom_forward(*__a			):
																			return module(*__a			)

																return custom_forward

													# down
													if is_torch_version(">="					,     "1.11.0"			):
																for down_block in self.down_blocks:
																			__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(__a			)					,     __a					,     use_reentrant=__a			)
																# middle
																__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(
																    create_custom_forward(self.mid_block			)					,     __a					,     use_reentrant=__a			)
													else:
																for down_block in self.down_blocks:
																			__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(create_custom_forward(__a			)					,     __a			)
																# middle
																__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block			)					,     __a			)

										else:
													# down
													for down_block in self.down_blocks:
																__lowerCAmelCase       					=    down_block(__a			)

													# middle
													__lowerCAmelCase       					=    self.mid_block(__a			)

										# post-process
										__lowerCAmelCase       					=    self.conv_norm_out(__a			)
										__lowerCAmelCase       					=    self.conv_act(__a			)
										__lowerCAmelCase       					=    self.conv_out(__a			)

										return sample
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a=3					,     __a=3					,     __a=("UpDecoderBlock2D",)					,     __a=(64,)					,     __a=2					,     __a=32					,     __a="silu"					,     __a="group"					,     ):
										super().__init__()
										__lowerCAmelCase       					=    layers_per_block

										__lowerCAmelCase       					=    nn.Convad(
										    __a					,     block_out_channels[-1]					,     kernel_size=3					,     stride=1					,     padding=1					,     )

										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    nn.ModuleList([]			)

										__lowerCAmelCase       					=    in_channels if norm_type == "spatial" else None

										# mid
										__lowerCAmelCase       					=    UNetMidBlockaD(
										    in_channels=block_out_channels[-1]					,     resnet_eps=1e-6					,     resnet_act_fn=__a					,     output_scale_factor=1					,     resnet_time_scale_shift="default" if norm_type == "group" else norm_type					,     attention_head_dim=block_out_channels[-1]					,     resnet_groups=__a					,     temb_channels=__a					,     )

										# up
										__lowerCAmelCase       					=    list(reversed(__a			)			)
										__lowerCAmelCase       					=    reversed_block_out_channels[0]
										for i, up_block_type in enumerate(__a			):
													__lowerCAmelCase       					=    output_channel
													__lowerCAmelCase       					=    reversed_block_out_channels[i]

													__lowerCAmelCase       					=    i == len(__a			) - 1

													__lowerCAmelCase       					=    get_up_block(
													    __a					,     num_layers=self.layers_per_block + 1					,     in_channels=__a					,     out_channels=__a					,     prev_output_channel=__a					,     add_upsample=not is_final_block					,     resnet_eps=1e-6					,     resnet_act_fn=__a					,     resnet_groups=__a					,     attention_head_dim=__a					,     temb_channels=__a					,     resnet_time_scale_shift=__a					,     )
													self.up_blocks.append(__a			)
													__lowerCAmelCase       					=    output_channel

										# out
										if norm_type == "spatial":
													__lowerCAmelCase       					=    SpatialNorm(block_out_channels[0]					,     __a			)
										else:
													__lowerCAmelCase       					=    nn.GroupNorm(num_channels=block_out_channels[0]					,     num_groups=__a					,     eps=1e-6			)
										__lowerCAmelCase       					=    nn.SiLU()
										__lowerCAmelCase       					=    nn.Convad(block_out_channels[0]					,     __a					,     3					,     padding=1			)

										__lowerCAmelCase       					=    False



							def     snake_case       (  self					,     __a					,     __a=None			):
										__lowerCAmelCase       					=    z
										__lowerCAmelCase       					=    self.conv_in(__a			)

										__lowerCAmelCase       					=    next(iter(self.up_blocks.parameters()			)			).dtype
										if self.training and self.gradient_checkpointing:

													def create_custom_forward(__a			):
																def custom_forward(*__a			):
																			return module(*__a			)

																return custom_forward

													if is_torch_version(">="					,     "1.11.0"			):
																# middle
																__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(
																    create_custom_forward(self.mid_block			)					,     __a					,     __a					,     use_reentrant=__a			)
																__lowerCAmelCase       					=    sample.to(__a			)

																# up
																for up_block in self.up_blocks:
																			__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(__a			)					,     __a					,     __a					,     use_reentrant=__a			)
													else:
																# middle
																__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(
																    create_custom_forward(self.mid_block			)					,     __a					,     __a			)
																__lowerCAmelCase       					=    sample.to(__a			)

																# up
																for up_block in self.up_blocks:
																			__lowerCAmelCase       					=    torch.utils.checkpoint.checkpoint(create_custom_forward(__a			)					,     __a					,     __a			)
										else:
													# middle
													__lowerCAmelCase       					=    self.mid_block(__a					,     __a			)
													__lowerCAmelCase       					=    sample.to(__a			)

													# up
													for up_block in self.up_blocks:
																__lowerCAmelCase       					=    up_block(__a					,     __a			)

        # post-process
										if latent_embeds is None:
													__lowerCAmelCase       					=    self.conv_norm_out(__a			)
										else:
													__lowerCAmelCase       					=    self.conv_norm_out(__a					,     __a			)
										__lowerCAmelCase       					=    self.conv_act(__a			)
										__lowerCAmelCase       					=    self.conv_out(__a			)

										return sample
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a					,     __a					,     __a=None					,     __a="random"					,     __a=False					,     __a=True			):
										super().__init__()
										__lowerCAmelCase       					=    n_e
										__lowerCAmelCase       					=    vq_embed_dim
										__lowerCAmelCase       					=    beta
										__lowerCAmelCase       					=    legacy

										__lowerCAmelCase       					=    nn.Embedding(self.n_e					,     self.vq_embed_dim			)
										self.embedding.weight.data.uniform_(-1.0 / self.n_e					,     1.0 / self.n_e			)

										__lowerCAmelCase       					=    remap
										if self.remap is not None:
													self.register_buffer("used"					,     torch.tensor(np.load(self.remap			)			)			)
													__lowerCAmelCase       					=    self.used.shape[0]
													__lowerCAmelCase       					=    unknown_index  # "random" or "extra" or integer
													if self.unknown_index == "extra":
																__lowerCAmelCase       					=    self.re_embed
																__lowerCAmelCase       					=    self.re_embed + 1
													print(
													    f"Remapping {self.n_e} indices to {self.re_embed} indices. "
													    f"Using {self.unknown_index} for unknown indices."			)
										else:
													__lowerCAmelCase       					=    n_e

										__lowerCAmelCase       					=    sane_index_shape

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    inds.shape
										assert len(__a			) > 1
										__lowerCAmelCase       					=    inds.reshape(ishape[0]					,     -1			)
										__lowerCAmelCase       					=    self.used.to(__a			)
										__lowerCAmelCase       					=    (inds[:, :, None] == used[None, None, ...]).long()
										__lowerCAmelCase       					=    match.argmax(-1			)
										__lowerCAmelCase       					=    match.sum(2			) < 1
										if self.unknown_index == "random":
													__lowerCAmelCase       					=    torch.randint(0					,     self.re_embed					,     size=new[unknown].shape			).to(device=new.device			)
										else:
													__lowerCAmelCase       					=    self.unknown_index
										return new.reshape(__a			)

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    inds.shape
										assert len(__a			) > 1
										__lowerCAmelCase       					=    inds.reshape(ishape[0]					,     -1			)
										__lowerCAmelCase       					=    self.used.to(__a			)
										if self.re_embed > self.used.shape[0]:  # extra token
													__lowerCAmelCase       					=    0  # simply set to zero
										__lowerCAmelCase       					=    torch.gather(used[None, :][inds.shape[0] * [0], :]					,     1					,     __a			)
										return back.reshape(__a			)

							def     snake_case       (  self					,     __a			):
										# reshape z -> (batch, height, width, channel) and flatten
										__lowerCAmelCase       					=    z.permute(0					,     2					,     3					,     1			).contiguous()
										__lowerCAmelCase       					=    z.view(-1					,     self.vq_embed_dim			)

										# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
										__lowerCAmelCase       					=    torch.argmin(torch.cdist(__a					,     self.embedding.weight			)					,     dim=1			)

										__lowerCAmelCase       					=    self.embedding(__a			).view(z.shape			)
										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    None

										# compute loss for embedding
										if not self.legacy:
													__lowerCAmelCase       					=    self.beta * torch.mean((z_q.detach() - z) ** 2			) + torch.mean((z_q - z.detach()) ** 2			)
										else:
													__lowerCAmelCase       					=    torch.mean((z_q.detach() - z) ** 2			) + self.beta * torch.mean((z_q - z.detach()) ** 2			)

										# preserve gradients
										__lowerCAmelCase       					=    z + (z_q - z).detach()

										# reshape back to match original input shape
										__lowerCAmelCase       					=    z_q.permute(0					,     3					,     1					,     2			).contiguous()

										if self.remap is not None:
													__lowerCAmelCase       					=    min_encoding_indices.reshape(z.shape[0]					,     -1			)  # add batch axis
													__lowerCAmelCase       					=    self.remap_to_used(__a			)
													__lowerCAmelCase       					=    min_encoding_indices.reshape(-1					,     1			)  # flatten

										if self.sane_index_shape:
													__lowerCAmelCase       					=    min_encoding_indices.reshape(z_q.shape[0]					,     z_q.shape[2]					,     z_q.shape[3]			)

										return z_q, loss, (perplexity, min_encodings, min_encoding_indices)



							def     snake_case       (  self					,     __a					,     __a			):
										# shape specifying (batch, height, width, channel)
										if self.remap is not None:
													__lowerCAmelCase       					=    indices.reshape(shape[0]					,     -1			)  # add batch axis
													__lowerCAmelCase       					=    self.unmap_to_all(__a			)
													__lowerCAmelCase       					=    indices.reshape(-1			)  # flatten again

										# get quantized latent vectors
										__lowerCAmelCase       					=    self.embedding(__a			)

										if shape is not None:
													__lowerCAmelCase       					=    z_q.view(__a			)
													# reshape back to match original input shape
													__lowerCAmelCase       					=    z_q.permute(0					,     3					,     1					,     2			).contiguous()

										return z_q
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    parameters
										__lowerCAmelCase					,			__lowerCAmelCase       					=    torch.chunk(__a					,     2					,     dim=1			)
										__lowerCAmelCase       					=    torch.clamp(self.logvar					,     -3_0.0					,     2_0.0			)
										__lowerCAmelCase       					=    deterministic
										__lowerCAmelCase       					=    torch.exp(0.5 * self.logvar			)
										__lowerCAmelCase       					=    torch.exp(self.logvar			)
										if self.deterministic:
													__lowerCAmelCase       					=    __lowerCAmelCase       					=    torch.zeros_like(
													    self.mean					,     device=self.parameters.device					,     dtype=self.parameters.dtype			)

							def     snake_case       (  self					,     __a = None			):
										# make sure sample is on the same device as the parameters and has same dtype
										__lowerCAmelCase       					=    randn_tensor(
										    self.mean.shape					,     generator=__a					,     device=self.parameters.device					,     dtype=self.parameters.dtype			)
										__lowerCAmelCase       					=    self.mean + self.std * sample
										return x

							def     snake_case       (  self					,     __a=None			):
										if self.deterministic:
													return torch.Tensor([0.0]			)
										else:
													if other is None:
																return 0.5 * torch.sum(torch.pow(self.mean					,     2			) + self.var - 1.0 - self.logvar					,     dim=[1, 2, 3]			)
													else:
																return 0.5 * torch.sum(
																    torch.pow(self.mean - other.mean					,     2			) / other.var
																    + self.var / other.var
																    - 1.0
																    - self.logvar
																    + other.logvar					,     dim=[1, 2, 3]					,     )

							def     snake_case       (  self					,     __a					,     __a=[1, 2, 3]			):
										if self.deterministic:
													return torch.Tensor([0.0]			)
										__lowerCAmelCase       					=    np.log(2.0 * np.pi			)
										return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean					,     2			) / self.var					,     dim=__a			)



							def     snake_case       (  self			):
										return self.mean








"""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 _lowerCamelCase			(	_UpperCamelCase   ):

			'''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(_UpperCamelCase					,				_UpperCamelCase					,				repo_type="dataset"   )					,				"r"   )   )
			__lowerCAmelCase       					=    {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
			__lowerCAmelCase       					=    {v: k for k, v in idalabel.items()}

			__lowerCAmelCase       					=    SwinConfig(
			    embed_dim=_UpperCamelCase					,				depths=_UpperCamelCase					,				num_heads=_UpperCamelCase					,				window_size=_UpperCamelCase					,				out_features=["stage1", "stage2", "stage3", "stage4"]					,				)
			__lowerCAmelCase       					=    UperNetConfig(
			    backbone_config=_UpperCamelCase					,				auxiliary_in_channels=_UpperCamelCase					,				num_labels=_UpperCamelCase					,				idalabel=_UpperCamelCase					,				labelaid=_UpperCamelCase					,				)

			return config


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''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 _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    dct.pop(_UpperCamelCase   )
			__lowerCAmelCase       					=    val


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''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 _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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

			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''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(_UpperCamelCase					,				map_location="cpu"					,				file_name=_UpperCamelCase   )[
			    "state_dict"
			]

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

			__lowerCAmelCase       					=    get_upernet_config(_UpperCamelCase   )
			__lowerCAmelCase       					=    UperNetForSemanticSegmentation(_UpperCamelCase   )
			model.eval()

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

			# rename keys
			__lowerCAmelCase       					=    create_rename_keys(_UpperCamelCase   )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )
			read_in_q_k_v(_UpperCamelCase					,				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(_UpperCamelCase   )
									if "norm" in key:
												__lowerCAmelCase       					=    reverse_correct_unfold_norm_order(_UpperCamelCase   )

			model.load_state_dict(_UpperCamelCase   )

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

			__lowerCAmelCase       					=    SegformerImageProcessor()
			__lowerCAmelCase       					=    processor(_UpperCamelCase					,				return_tensors="pt"   ).pixel_values

			with torch.no_grad():
						__lowerCAmelCase       					=    model(_UpperCamelCase   )
						__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(
						    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]   )
			elif model_name == "upernet-swin-small":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]   )
			elif model_name == "upernet-swin-base":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]   )
			elif model_name == "upernet-swin-large":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]   )
			print("Logits:"					,				outputs.logits[0, 0, :3, :3]   )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]					,				_UpperCamelCase					,				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(_UpperCamelCase   )
						print(f"Saving processor to {pytorch_dump_folder_path}"   )
						processor.save_pretrained(_UpperCamelCase   )

			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__":
	A  :     int    					=    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."
	)

	A  :     List[Any]    					=    parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




