




import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_A     =			logging.get_logger(__name__)





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  SwinConfig.from_pretrained(
						    '''microsoft/swin-tiny-patch4-window7-224''' ,  out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']  )
						lowerCAmelCase_      			=  MaskFormerConfig(backbone_config=_A  )

						lowerCAmelCase_      			=  '''huggingface/label-files'''
						if "ade20k-full" in model_name:
												# this should be ok
												lowerCAmelCase_      			=  847
												lowerCAmelCase_      			=  '''maskformer-ade20k-full-id2label.json'''
						elif "ade" in model_name:
												# this should be ok
												lowerCAmelCase_      			=  150
												lowerCAmelCase_      			=  '''ade20k-id2label.json'''
						elif "coco-stuff" in model_name:
												# this should be ok
												lowerCAmelCase_      			=  171
												lowerCAmelCase_      			=  '''maskformer-coco-stuff-id2label.json'''
						elif "coco" in model_name:
												# TODO
												lowerCAmelCase_      			=  133
												lowerCAmelCase_      			=  '''coco-panoptic-id2label.json'''
						elif "cityscapes" in model_name:
												# this should be ok
												lowerCAmelCase_      			=  19
												lowerCAmelCase_      			=  '''cityscapes-id2label.json'''
						elif "vistas" in model_name:
												# this should be ok
												lowerCAmelCase_      			=  65
												lowerCAmelCase_      			=  '''mapillary-vistas-id2label.json'''

						lowerCAmelCase_      			=  json.load(open(hf_hub_download(_A ,  _A ,  repo_type='''dataset'''  ) ,  '''r'''  )  )
						lowerCAmelCase_      			=  {int(_A  ): v for k, v in idalabel.items()}

						return config





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  []
						# stem
						# fmt: off
						rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')  )
						rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')  )
						rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')  )
						rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.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.layers.{i}.blocks.{j}.norm1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")  )
																		rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")  )

												if i < 3:
																		rename_keys.append((f"backbone.layers.{i}.downsample.reduction.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")  )
																		rename_keys.append((f"backbone.layers.{i}.downsample.norm.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")  )
																		rename_keys.append((f"backbone.layers.{i}.downsample.norm.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")  )
												rename_keys.append((f"backbone.norm{i}.weight", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")  )
												rename_keys.append((f"backbone.norm{i}.bias", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")  )

						# FPN
						rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')  )
						rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')  )
						rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')  )
						for source_index, target_index in zip(range(3 ,  0 ,  -1  ) ,  range(0 ,  3  )  ):
												rename_keys.append((f"sem_seg_head.adapter_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")  )
												rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")  )
												rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")  )
												rename_keys.append((f"sem_seg_head.layer_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")  )
												rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")  )
												rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")  )
						rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')  )
						rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')  )

						# Transformer decoder
						for idx in range(config.decoder_config.decoder_layers  ):
												# self-attention out projection
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")  )
												# cross-attention out projection
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")  )
												# MLP 1
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", f"model.transformer_module.decoder.layers.{idx}.fc1.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", f"model.transformer_module.decoder.layers.{idx}.fc1.bias")  )
												# MLP 2
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", f"model.transformer_module.decoder.layers.{idx}.fc2.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", f"model.transformer_module.decoder.layers.{idx}.fc2.bias")  )
												# layernorm 1 (self-attention layernorm)
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")  )
												# layernorm 2 (cross-attention layernorm)
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")  )
												# layernorm 3 (final layernorm)
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")  )

						rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')  )
						rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')  )

						# heads on top
						rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')  )

						rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')  )
						rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')  )

						rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')  )
						rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')  )

						for i in range(3  ):
												rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.weight", f"mask_embedder.{i}.0.weight")  )
												rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.bias", f"mask_embedder.{i}.0.bias")  )
						# fmt: on

						return rename_keys





def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  dct.pop(_A  )
						lowerCAmelCase_      			=  val





def __UpperCamelCase			(       _A ,  _A  ):
						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.layers.{i}.blocks.{j}.attn.qkv.weight"  )
																		lowerCAmelCase_      			=  state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.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			(       _A ,  _A  ):
						# fmt: off
						lowerCAmelCase_      			=  config.decoder_config.hidden_size
						for idx in range(config.decoder_config.decoder_layers  ):
												# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
												lowerCAmelCase_      			=  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"  )
												lowerCAmelCase_      			=  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"  )
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase_      			=  in_proj_weight[: hidden_size, :]
												lowerCAmelCase_      			=  in_proj_bias[:config.hidden_size]
												lowerCAmelCase_      			=  in_proj_weight[hidden_size : hidden_size * 2, :]
												lowerCAmelCase_      			=  in_proj_bias[hidden_size : hidden_size * 2]
												lowerCAmelCase_      			=  in_proj_weight[-hidden_size :, :]
												lowerCAmelCase_      			=  in_proj_bias[-hidden_size :]
												# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
												lowerCAmelCase_      			=  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"  )
												lowerCAmelCase_      			=  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"  )
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase_      			=  in_proj_weight[: hidden_size, :]
												lowerCAmelCase_      			=  in_proj_bias[:config.hidden_size]
												lowerCAmelCase_      			=  in_proj_weight[hidden_size : hidden_size * 2, :]
												lowerCAmelCase_      			=  in_proj_bias[hidden_size : hidden_size * 2]
												lowerCAmelCase_      			=  in_proj_weight[-hidden_size :, :]
												lowerCAmelCase_      			=  in_proj_bias[-hidden_size :]
						# fmt: on





def __UpperCamelCase			(       ):
						lowerCAmelCase_      			=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowerCAmelCase_      			=  Image.open(requests.get(_A ,  stream=_A  ).raw  )
						return im





@torch.no_grad()
def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A = False  ):
						lowerCAmelCase_      			=  get_maskformer_config(_A  )

						# load original state_dict
						with open(_A ,  '''rb'''  ) as f:
												lowerCAmelCase_      			=  pickle.load(_A  )
						lowerCAmelCase_      			=  data['''model''']

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

						# rename keys
						lowerCAmelCase_      			=  create_rename_keys(_A  )
						for src, dest in rename_keys:
												rename_key(_A ,  _A ,  _A  )
						read_in_swin_q_k_v(_A ,  config.backbone_config  )
						read_in_decoder_q_k_v(_A ,  _A  )

						# update to torch tensors
						for key, value in state_dict.items():
												lowerCAmelCase_      			=  torch.from_numpy(_A  )

						# load 🤗 model
						lowerCAmelCase_      			=  MaskFormerForInstanceSegmentation(_A  )
						model.eval()

						for name, param in model.named_parameters():
												print(_A ,  param.shape  )

						lowerCAmelCase_			,					lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(_A  ) == 0, f"Unexpected keys: {unexpected_keys}"

						# verify results
						lowerCAmelCase_      			=  prepare_img()
						if "vistas" in model_name:
												lowerCAmelCase_      			=  65
						elif "cityscapes" in model_name:
												lowerCAmelCase_      			=  65535
						else:
												lowerCAmelCase_      			=  255
						lowerCAmelCase_      			=  True if '''ade''' in model_name else False
						lowerCAmelCase_      			=  MaskFormerImageProcessor(ignore_index=_A ,  reduce_labels=_A  )

						lowerCAmelCase_      			=  image_processor(_A ,  return_tensors='''pt'''  )

						lowerCAmelCase_      			=  model(**_A  )

						print('''Logits:''' ,  outputs.class_queries_logits[0, :3, :3]  )

						if model_name == "maskformer-swin-tiny-ade":
												lowerCAmelCase_      			=  torch.tensor(
												    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]  )
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,  _A ,  atol=1E-4  )
						print('''Looks ok!'''  )

						if pytorch_dump_folder_path is not None:
												print(f"Saving model and image processor to {pytorch_dump_folder_path}"  )
												Path(_A  ).mkdir(exist_ok=_A  )
												model.save_pretrained(_A  )
												image_processor.save_pretrained(_A  )

						if push_to_hub:
												print('''Pushing model and image processor to the hub...'''  )
												model.push_to_hub(f"nielsr/{model_name}"  )
												image_processor.push_to_hub(f"nielsr/{model_name}"  )


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default='''maskformer-swin-tiny-ade''',
						    type=str,
						    help=('''Name of the MaskFormer model you\'d like to convert''',),
						)
						parser.add_argument(
						    '''--checkpoint_path''',
						    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
						    type=str,
						    help='''Path to the original state dict (.pth file).''',
						)
						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_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  A		(  unittest.TestCase     ):



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  UNetaDModel(
								    block_out_channels=(32, 64),	layers_per_block=2,	sample_size=32,	in_channels=3,	out_channels=3,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),	)
								return model







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.dummy_uncond_unet
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy''',	return_dict=UpperCamelCase__   )[0]

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								lowerCAmelCase_      			=  image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCAmelCase_      			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch
class  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''google/ncsnpp-celebahq-256'''
								lowerCAmelCase_      			=  UNetaDModel.from_pretrained(UpperCamelCase__   )
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=20,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								lowerCAmelCase_      			=  np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




