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()
_lowercase							:      Dict      							=logging.get_logger(__name__)



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

       A						:     Tuple         ='huggingface/label-files'
       if "ade20k-full" in model_name:
              # this should be ok
              A						:     Tuple         =847
              A						:     int         ='maskformer-ade20k-full-id2label.json'
       elif "ade" in model_name:
              # this should be ok
              A						:     str         =150
              A						:     Tuple         ='ade20k-id2label.json'
       elif "coco-stuff" in model_name:
              # this should be ok
              A						:     Union[str, Any]         =171
              A						:     Dict         ='maskformer-coco-stuff-id2label.json'
       elif "coco" in model_name:
              # TODO
              A						:     int         =133
              A						:     int         ='coco-panoptic-id2label.json'
       elif "cityscapes" in model_name:
              # this should be ok
              A						:     List[Any]         =19
              A						:     Tuple         ='cityscapes-id2label.json'
       elif "vistas" in model_name:
              # this should be ok
              A						:     str         =65
              A						:     Union[str, Any]         ='mapillary-vistas-id2label.json'

       A						:     Union[str, Any]         =json.load(open(hf_hub_download(lowercase,		lowercase,		repo_type='dataset'					),		'r'					)					)
       A						:     Dict         ={int(lowercase					): v for k, v in idalabel.items()}

       return config



def A__  (			lowercase:     Any					)				->		Tuple:
       A						:     Union[str, Any]         =[]
       # 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__  (			lowercase:     Dict,		lowercase:     Dict,		lowercase:     Optional[int]					)				->		Optional[Any]:
       A						:     str         =dct.pop(lowercase					)
       A						:     List[Any]         =val



def A__  (			lowercase:     int,		lowercase:     Dict					)				->		str:
       A						:     Dict         =[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
       for i in range(len(backbone_config.depths					)					):
              A						:     Tuple         =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						:     Optional[Any]         =state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'					)
                     A						:     str         =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						:     Tuple         =in_proj_weight[:dim, :]
                     A						:     Any         =in_proj_bias[: dim]
                     A						:     Optional[Any]         =in_proj_weight[
                         dim : dim * 2, :
                     ]
                     A						:     Optional[int]         =in_proj_bias[
                         dim : dim * 2
                     ]
                     A						:     int         =in_proj_weight[
                         -dim :, :
                     ]
                     A						:     List[Any]         =in_proj_bias[-dim :]
                     # fmt: on



def A__  (			lowercase:     Optional[int],		lowercase:     Optional[Any]					)				->		Dict:
       # fmt: off
       A						:     Any         =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						:     Any         =state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'					)
              A						:     int         =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						:     Optional[Any]         =in_proj_weight[: hidden_size, :]
              A						:     Union[str, Any]         =in_proj_bias[:config.hidden_size]
              A						:     Optional[Any]         =in_proj_weight[hidden_size : hidden_size * 2, :]
              A						:     List[Any]         =in_proj_bias[hidden_size : hidden_size * 2]
              A						:     List[str]         =in_proj_weight[-hidden_size :, :]
              A						:     int         =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						:     Union[str, Any]         =state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'					)
              A						:     List[Any]         =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						:     Optional[int]         =in_proj_weight[: hidden_size, :]
              A						:     Any         =in_proj_bias[:config.hidden_size]
              A						:     Dict         =in_proj_weight[hidden_size : hidden_size * 2, :]
              A						:     int         =in_proj_bias[hidden_size : hidden_size * 2]
              A						:     Any         =in_proj_weight[-hidden_size :, :]
              A						:     Dict         =in_proj_bias[-hidden_size :]
       # fmt: on



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



@torch.no_grad()
def A__  (			lowercase:     str,		lowercase:     str,		lowercase:     str,		lowercase:     bool = False					)				->		Dict:
       A						:     Union[str, Any]         =get_maskformer_config(lowercase					)

       # load original state_dict
       with open(lowercase,		'rb'					) as f:
              A						:     Optional[Any]         =pickle.load(lowercase					)
       A						:     Optional[int]         =data['model']

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

       # rename keys
       A						:     List[str]         =create_rename_keys(lowercase					)
       for src, dest in rename_keys:
              rename_key(lowercase,		lowercase,		lowercase					)
       read_in_swin_q_k_v(lowercase,		config.backbone_config					)
       read_in_decoder_q_k_v(lowercase,		lowercase					)

       # update to torch tensors
       for key, value in state_dict.items():
              A						:     Dict         =torch.from_numpy(lowercase					)

       # load 🤗 model
       A						:     int         =MaskFormerForInstanceSegmentation(lowercase					)
       model.eval()

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

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

       # verify results
       A						:     Dict         =prepare_img()
       if "vistas" in model_name:
              A						:     Optional[int]         =65
       elif "cityscapes" in model_name:
              A						:     int         =65_535
       else:
              A						:     Optional[int]         =255
       A						:     List[str]         =True if 'ade' in model_name else False
       A						:     Optional[int]         =MaskFormerImageProcessor(ignore_index=lowercase,		reduce_labels=lowercase					)

       A						:     Tuple         =image_processor(lowercase,		return_tensors='pt'					)

       A						:     Optional[Any]         =model(**lowercase					)

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

       if model_name == "maskformer-swin-tiny-ade":
              A						:     Optional[Any]         =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],		lowercase,		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(lowercase					).mkdir(exist_ok=lowercase					)
              model.save_pretrained(lowercase					)
              image_processor.save_pretrained(lowercase					)

       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__":
    _lowercase							:      Optional[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.'''
    )

    _lowercase							:      int      							=parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






