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 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       _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=	8_47
              _A=	'maskformer-ade20k-full-id2label.json'
       elif "ade" in model_name:
              # this should be ok
              _A=	1_50
              _A=	'ade20k-id2label.json'
       elif "coco-stuff" in model_name:
              # this should be ok
              _A=	1_71
              _A=	'maskformer-coco-stuff-id2label.json'
       elif "coco" in model_name:
              # TODO
              _A=	1_33
              _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 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[Any]:




       '''simple docstring'''






       _A=	dct.pop(lowerCAmelCase_   )
       _A=	val

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       _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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       _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 							UpperCamelCase				(    )		->						torch.Tensor:




       '''simple docstring'''






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

@torch.no_grad()
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = False   )		->						str:




       '''simple docstring'''






       _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,			_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_55_35
       else:
              _A=	2_55
       _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(
                  [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]   )
       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
      )
from ..utils import DummyObject, requires_backends





class      lowerCAmelCase						(					metaclass=_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""transformers""", """torch""", """note_seq"""]



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(self				,      ['transformers', 'torch', 'note_seq']       )



     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )





     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )