




'''simple docstring'''


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()
UpperCAmelCase_ 		=      logging.get_logger(__name__)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					SwinConfig.from_pretrained(
      'microsoft/swin-tiny-patch4-window7-224'				,					out_features=['stage1', 'stage2', 'stage3', 'stage4']			)
  __a			=					MaskFormerConfig(backbone_config=lowerCamelCase_			)

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

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

  return config


def 						SCREAMING_SNAKE_CASE       (      a_	:							Any			):
  __a			=					[]
  # 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 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							int				,					a_	:							List[str]			):
  __a			=					dct.pop(lowerCamelCase_			)
  __a			=					val


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


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]				,					a_	:							Optional[int]			):
  # fmt: off
  __a			=					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)
    __a			=					state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"			)
    __a			=					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
    __a			=					in_proj_weight[: hidden_size, :]
    __a			=					in_proj_bias[:config.hidden_size]
    __a			=					in_proj_weight[hidden_size : hidden_size * 2, :]
    __a			=					in_proj_bias[hidden_size : hidden_size * 2]
    __a			=					in_proj_weight[-hidden_size :, :]
    __a			=					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)
    __a			=					state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"			)
    __a			=					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
    __a			=					in_proj_weight[: hidden_size, :]
    __a			=					in_proj_bias[:config.hidden_size]
    __a			=					in_proj_weight[hidden_size : hidden_size * 2, :]
    __a			=					in_proj_bias[hidden_size : hidden_size * 2]
    __a			=					in_proj_weight[-hidden_size :, :]
    __a			=					in_proj_bias[-hidden_size :]
  # fmt: on


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


@torch.no_grad()
def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]				,					a_	:							Dict				,					a_	:							Optional[Any] = False			):
  __a			=					get_maskformer_config(lowerCamelCase_			)

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

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

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

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

  # load 🤗 model
  __a			=					MaskFormerForInstanceSegmentation(lowerCamelCase_			)
  model.eval()

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

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

  # verify results
  __a			=					prepare_img()
  if "vistas" in model_name:
    __a			=					65
  elif "cityscapes" in model_name:
    __a			=					6_5535
  else:
    __a			=					255
  __a			=					True if 'ade' in model_name else False
  __a			=					MaskFormerImageProcessor(ignore_index=lowerCamelCase_				,					reduce_labels=lowerCamelCase_			)

  __a			=					image_processor(lowerCamelCase_				,					return_tensors='pt'			)

  __a			=					model(**lowerCamelCase_			)

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

  if model_name == "maskformer-swin-tiny-ade":
    __a			=					torch.tensor(
        [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]			)
  assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,					lowerCamelCase_				,					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(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
    model.save_pretrained(lowerCamelCase_			)
    image_processor.save_pretrained(lowerCamelCase_			)

  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__":
  UpperCAmelCase_ 		=      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."
  )

  UpperCAmelCase_ 		=      parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )






'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_ 		=      "path-to-your-trained-model"
UpperCAmelCase_ 		=      StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCAmelCase_ 		=      "A photo of sks dog in a bucket"
UpperCAmelCase_ 		=      pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")



