

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()
_lowerCamelCase      :					List[Any]					    =      logging.get_logger(__name__)





def      A__					(			__A  :						str			)					->Union[str, Any]:
      __A    =SwinConfig.from_pretrained(
          '''microsoft/swin-tiny-patch4-window7-224'''     ,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
      __A    =MaskFormerConfig(backbone_config=__A			)

      __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(__A     ,				__A     ,				repo_type='''dataset'''			)     ,				'''r'''			)			)
      __A    ={int(__A			): v for k, v in idalabel.items()}

      return config





def      A__					(			__A  :						str			)					->str:
      __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      A__					(			__A  :						Tuple     ,				__A  :						str     ,				__A  :						int			)					->List[Any]:
      __A    =dct.pop(__A			)
      __A    =val





def      A__					(			__A  :						Optional[Any]     ,				__A  :						int			)					->Tuple:
      __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      A__					(			__A  :						Union[str, Any]     ,				__A  :						str			)					->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      A__					(			)					->torch.Tensor:
      __A    ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __A    =Image.open(requests.get(__A     ,				stream=__A			).raw			)
      return im





@torch.no_grad()
def      A__					(			__A  :						str     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->Tuple:
      __A    =get_maskformer_config(__A			)

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

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

      # rename keys
      __A    =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():
            __A    =torch.from_numpy(__A			)

      # load 🤗 model
      __A    =MaskFormerForInstanceSegmentation(__A			)
      model.eval()

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

      __A    ,		__A    =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
      __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=__A     ,				reduce_labels=__A			)

      __A    =image_processor(__A     ,				return_tensors='''pt'''			)

      __A    =model(**__A			)

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

      if model_name == "maskformer-swin-tiny-ade":
            __A    =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]     ,				__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__":
   _lowerCamelCase      :					Any					    =      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.'''
   )

   _lowerCamelCase      :					Tuple					    =      parser.parse_args()
   convert_maskformer_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration





def      A__					(			__A  :						Any     ,				__A  :						str     ,				__A  :						str     ,				__A  :						Path     ,				__A  :						str = None     ,				__A  :						str = None     ,				__A  :						str = None     ,				)					->Optional[Any]:
      if config_name_or_path is None:
            __A    ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            __A    =generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            __A    =question_encoder_name_or_path

      __A    =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      __A    =RagConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)

      __A    =gen_config
      __A    =question_encoder_config

      __A    =model_class.from_pretrained_question_encoder_generator(
          __A     ,				__A     ,				config=__A			)
      rag_model.save_pretrained(__A			)

      # Sanity check.
      model_class.from_pretrained(__A			)

      # Save tokenizers.
      __A    =AutoTokenizer.from_pretrained(__A			)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
      __A    =AutoTokenizer.from_pretrained(__A			)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type''',
       choices=['''rag_sequence''', '''rag_token'''],
       required=True,
       type=str,
       help='''RAG model type: rag_sequence, rag_token''',
   )
   parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
   parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
   parser.add_argument(
       '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
   )

   parser.add_argument(
       '''--generator_tokenizer_name_or_path''',
       type=str,
       help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
   )
   parser.add_argument(
       '''--question_encoder_tokenizer_name_or_path''',
       type=str,
       help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
   )
   parser.add_argument(
       '''--config_name_or_path''',
       type=str,
       help=(
           '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
           ''' ``model_type``'''
       ),
   )

   _lowerCamelCase      :					str					    =      parser.parse_args()

   _lowerCamelCase      :					Optional[int]					    =      Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )


