






"""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 _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	SwinConfig.from_pretrained(
          """microsoft/swin-tiny-patch4-window7-224"""				,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							)
      UpperCAmelCase          =	MaskFormerConfig(backbone_config=_snake_case							)

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

      UpperCAmelCase          =	json.load(open(hf_hub_download(_snake_case				,	_snake_case				,	repo_type="""dataset"""							)				,	"""r"""							)							)
      UpperCAmelCase          =	{int(_snake_case							): v for k, v in idalabel.items()}

      return config

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      # 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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	dct.pop(_snake_case							)
      UpperCAmelCase          =	val

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
      for i in range(len(backbone_config.depths							)							):
            UpperCAmelCase          =	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)
                  UpperCAmelCase          =	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''							)
                  UpperCAmelCase          =	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
                  UpperCAmelCase          =	in_proj_weight[:dim, :]
                  UpperCAmelCase          =	in_proj_bias[: dim]
                  UpperCAmelCase          =	in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase          =	in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase          =	in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase          =	in_proj_bias[-dim :]
                  # fmt: on

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	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)
            UpperCAmelCase          =	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''							)
            UpperCAmelCase          =	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
            UpperCAmelCase          =	in_proj_weight[: hidden_size, :]
            UpperCAmelCase          =	in_proj_bias[:config.hidden_size]
            UpperCAmelCase          =	in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase          =	in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase          =	in_proj_weight[-hidden_size :, :]
            UpperCAmelCase          =	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)
            UpperCAmelCase          =	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''							)
            UpperCAmelCase          =	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
            UpperCAmelCase          =	in_proj_weight[: hidden_size, :]
            UpperCAmelCase          =	in_proj_bias[:config.hidden_size]
            UpperCAmelCase          =	in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase          =	in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase          =	in_proj_weight[-hidden_size :, :]
            UpperCAmelCase          =	in_proj_bias[-hidden_size :]
      # fmt: on

def _a			(		):







      """simple docstring"""






      UpperCAmelCase          =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCAmelCase          =	Image.open(requests.get(_snake_case				,	stream=_snake_case							).raw							)
      return im

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case = False							):







      """simple docstring"""






      UpperCAmelCase          =	get_maskformer_config(_snake_case							)

      # load original state_dict
      with open(_snake_case				,	"""rb"""							) as f:
            UpperCAmelCase          =	pickle.load(_snake_case							)
      UpperCAmelCase          =	data["""model"""]

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

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

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

      # load 🤗 model
      UpperCAmelCase          =	MaskFormerForInstanceSegmentation(_snake_case							)
      model.eval()

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

      UpperCAmelCase      , UpperCAmelCase          =	model.load_state_dict(_snake_case				,	strict=_snake_case							)
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(_snake_case							) == 0, F'''Unexpected keys: {unexpected_keys}'''

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

      UpperCAmelCase          =	image_processor(_snake_case				,	return_tensors="""pt"""							)

      UpperCAmelCase          =	model(**_snake_case							)

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

      if model_name == "maskformer-swin-tiny-ade":
            UpperCAmelCase          =	torch.tensor(
                [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]							)
      assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,	_snake_case				,	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(_snake_case							).mkdir(exist_ok=_snake_case							)
            model.save_pretrained(_snake_case							)
            image_processor.save_pretrained(_snake_case							)

      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"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



