

from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)






@dataclass
class _UpperCamelCase							(							__A   ):
   '''simple docstring'''







   lowerCamelCase__           =42
   lowerCamelCase__           =42
   lowerCamelCase__           =42


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .pipeline_if import IFPipeline
   from .pipeline_if_imgaimg import IFImgaImgPipeline
   from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
   from .pipeline_if_inpainting import IFInpaintingPipeline
   from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
   from .pipeline_if_superresolution import IFSuperResolutionPipeline
   from .safety_checker import IFSafetyChecker
   from .watermark import IFWatermarker


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__ (					_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       384
	SCREAMING_SNAKE_CASE   :      Dict				     =       7
	if "tiny" in model_name:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       96
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (2, 2, 6, 2)
		SCREAMING_SNAKE_CASE   :      Dict				     =       (3, 6, 12, 24)
	elif "small" in model_name:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       96
		SCREAMING_SNAKE_CASE   :      Dict				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      Dict				     =       (3, 6, 12, 24)
	elif "base" in model_name:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       128
		SCREAMING_SNAKE_CASE   :      Tuple				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      str				     =       (4, 8, 16, 32)
		SCREAMING_SNAKE_CASE   :      Any				     =       12
		SCREAMING_SNAKE_CASE   :      int				     =       512
	elif "large" in model_name:
		SCREAMING_SNAKE_CASE   :      Any				     =       192
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (6, 12, 24, 48)
		SCREAMING_SNAKE_CASE   :      Any				     =       12
		SCREAMING_SNAKE_CASE   :      Any				     =       768

	# set label information
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       150
	SCREAMING_SNAKE_CASE   :      int				     =       "huggingface/label-files"
	SCREAMING_SNAKE_CASE   :      Dict				     =       "ade20k-id2label.json"
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       json.load(open(hf_hub_download(_a							,			_a							,			repo_type="dataset")							,			"r"))
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       {int(_a): v for k, v in idalabel.items()}
	SCREAMING_SNAKE_CASE   :      int				     =       {v: k for k, v in idalabel.items()}

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       SwinConfig(
	    embed_dim=_a							,			depths=_a							,			num_heads=_a							,			window_size=_a							,			out_features=["stage1", "stage2", "stage3", "stage4"]							,			)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UperNetConfig(
	    backbone_config=_a							,			auxiliary_in_channels=_a							,			num_labels=_a							,			idalabel=_a							,			labelaid=_a							,			)

	return config

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       []

	# 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__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       dct.pop(_a)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       val

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
	for i in range(len(backbone_config.depths)):
		SCREAMING_SNAKE_CASE   :      Tuple				     =       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)
			SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight")
			SCREAMING_SNAKE_CASE   :      List[str]				     =       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
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       in_proj_weight[:dim, :]
			SCREAMING_SNAKE_CASE   :      int				     =       in_proj_bias[: dim]
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_proj_weight[
			    dim : dim * 2, :
			]
			SCREAMING_SNAKE_CASE   :      str				     =       in_proj_bias[
			    dim : dim * 2
			]
			SCREAMING_SNAKE_CASE   :      List[str]				     =       in_proj_weight[
			    -dim :, :
			]
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       in_proj_bias[-dim :]
			# fmt: on

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       x.shape
	SCREAMING_SNAKE_CASE   :      List[str]				     =       x.reshape(_a							,			4							,			in_channel // 4)
	SCREAMING_SNAKE_CASE   :      str				     =       x[:, [0, 2, 1, 3], :].transpose(1							,			2).reshape(_a							,			_a)
	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       x.shape
	SCREAMING_SNAKE_CASE   :      Tuple				     =       x.reshape(_a							,			in_channel // 4							,			4)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       x[:, :, [0, 2, 1, 3]].transpose(1							,			2).reshape(_a							,			_a)

	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      str				     =       x.shape[0]
	SCREAMING_SNAKE_CASE   :      int				     =       x.reshape(4							,			in_channel // 4)
	SCREAMING_SNAKE_CASE   :      int				     =       x[[0, 2, 1, 3], :].transpose(0							,			1).reshape(_a)
	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       x.shape[0]
	SCREAMING_SNAKE_CASE   :      Dict				     =       x.reshape(in_channel // 4							,			4)
	SCREAMING_SNAKE_CASE   :      Dict				     =       x[:, [0, 2, 1, 3]].transpose(0							,			1).reshape(_a)
	return x

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       {
	    "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",
	}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model_name_to_url[model_name]
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.hub.load_state_dict_from_url(_a							,			map_location="cpu"							,			file_name=_a)[
	    "state_dict"
	]

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

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       get_upernet_config(_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       UperNetForSemanticSegmentation(_a)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       state_dict.pop(_a)
		if "bn" in key:
			SCREAMING_SNAKE_CASE   :      Any				     =       key.replace("bn"							,			"batch_norm")
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       val

	# rename keys
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       create_rename_keys(_a)
	for src, dest in rename_keys:
		rename_key(_a							,			_a							,			_a)
	read_in_q_k_v(_a							,			config.backbone_config)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       reverse_correct_unfold_reduction_order(_a)
			if "norm" in key:
				SCREAMING_SNAKE_CASE   :      str				     =       reverse_correct_unfold_norm_order(_a)

	model.load_state_dict(_a)

	# verify on image
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       Image.open(requests.get(_a							,			stream=_a).raw).convert("RGB")

	SCREAMING_SNAKE_CASE   :      Tuple				     =       SegformerImageProcessor()
	SCREAMING_SNAKE_CASE   :      Dict				     =       processor(_a							,			return_tensors="pt").pixel_values

	with torch.no_grad():
		SCREAMING_SNAKE_CASE   :      str				     =       model(_a)
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       outputs.logits

	print(logits.shape)
	print("First values of logits:"							,			logits[0, 0, :3, :3])
	# assert values
	if model_name == "upernet-swin-tiny":
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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":
		SCREAMING_SNAKE_CASE   :      Tuple				     =       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":
		SCREAMING_SNAKE_CASE   :      str				     =       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":
		SCREAMING_SNAKE_CASE   :      Any				     =       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]							,			_a							,			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(_a)
		print(f"Saving processor to {pytorch_dump_folder_path}")
		processor.save_pretrained(_a)

	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_						     =    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_						     =    parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)