


'''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()
__snake_case    						=logging.get_logger(__name__)





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

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

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

   return config





def 	a_					( lowerCamelCase	:     Tuple				):
   lowerCAmelCase     =			[]
   # 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_					( lowerCamelCase	:     Any	, lowerCamelCase	:     int	, lowerCamelCase	:     List[Any]				):
   lowerCAmelCase     =			dct.pop(lowerCamelCase				)
   lowerCAmelCase     =			val





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





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     List[str]				):
   # fmt: off
   lowerCAmelCase     =			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)
      lowerCAmelCase     =			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''				)
      lowerCAmelCase     =			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
      lowerCAmelCase     =			in_proj_weight[: hidden_size, :]
      lowerCAmelCase     =			in_proj_bias[:config.hidden_size]
      lowerCAmelCase     =			in_proj_weight[hidden_size : hidden_size * 2, :]
      lowerCAmelCase     =			in_proj_bias[hidden_size : hidden_size * 2]
      lowerCAmelCase     =			in_proj_weight[-hidden_size :, :]
      lowerCAmelCase     =			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)
      lowerCAmelCase     =			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''				)
      lowerCAmelCase     =			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
      lowerCAmelCase     =			in_proj_weight[: hidden_size, :]
      lowerCAmelCase     =			in_proj_bias[:config.hidden_size]
      lowerCAmelCase     =			in_proj_weight[hidden_size : hidden_size * 2, :]
      lowerCAmelCase     =			in_proj_bias[hidden_size : hidden_size * 2]
      lowerCAmelCase     =			in_proj_weight[-hidden_size :, :]
      lowerCAmelCase     =			in_proj_bias[-hidden_size :]
   # fmt: on





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





@torch.no_grad()
def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     str	, lowerCamelCase	:     str	, lowerCamelCase	:     bool = False				):
   lowerCAmelCase     =			get_maskformer_config(lowerCamelCase				)

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

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

   # rename keys
   lowerCAmelCase     =			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():
      lowerCAmelCase     =			torch.from_numpy(lowerCamelCase				)

   # load 🤗 model
   lowerCAmelCase     =			MaskFormerForInstanceSegmentation(lowerCamelCase				)
   model.eval()

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

   lowerCAmelCase     ,  lowerCAmelCase     =			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
   lowerCAmelCase     =			prepare_img()
   if "vistas" in model_name:
      lowerCAmelCase     =			65
   elif "cityscapes" in model_name:
      lowerCAmelCase     =			65535
   else:
      lowerCAmelCase     =			255
   lowerCAmelCase     =			True if 'ade' in model_name else False
   lowerCAmelCase     =			MaskFormerImageProcessor(ignore_index=lowerCamelCase	, reduce_labels=lowerCamelCase				)

   lowerCAmelCase     =			image_processor(lowerCamelCase	, return_tensors='pt'				)

   lowerCAmelCase     =			model(**lowerCamelCase				)

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

   if model_name == "maskformer-swin-tiny-ade":
      lowerCAmelCase     =			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__":
       __snake_case    						=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."""
       )

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









'''simple docstring'''




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


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Union[str, Any]		=     '''speech_to_text_2'''
     lowerCamelCase				:	Any		=     ['''past_key_values''']
     lowerCamelCase				:	Optional[Any]		=     {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]=1_0_0_0_0    ,      UpperCAmelCase__       :      int=6    ,      UpperCAmelCase__       :      Optional[Any]=2_0_4_8    ,      UpperCAmelCase__       :      Union[str, Any]=4    ,      UpperCAmelCase__       :      List[Any]=0.0    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      str="relu"    ,      UpperCAmelCase__       :      Any=2_5_6    ,      UpperCAmelCase__       :      Optional[Any]=0.1    ,      UpperCAmelCase__       :      Any=0.0    ,      UpperCAmelCase__       :      Tuple=0.0    ,      UpperCAmelCase__       :      List[Any]=0.02    ,      UpperCAmelCase__       :      List[Any]=2    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      List[str]=1    ,      UpperCAmelCase__       :      Any=0    ,      UpperCAmelCase__       :      Dict=2    ,      UpperCAmelCase__       :      int=1_0_2_4    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Dict:
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			d_model
        lowerCAmelCase     =			decoder_ffn_dim
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			decoder_attention_heads
        lowerCAmelCase     =			dropout
        lowerCAmelCase     =			attention_dropout
        lowerCAmelCase     =			activation_dropout
        lowerCAmelCase     =			activation_function
        lowerCAmelCase     =			init_std
        lowerCAmelCase     =			decoder_layerdrop
        lowerCAmelCase     =			use_cache
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			scale_embedding  # scale factor will be sqrt(d_model) if True
        lowerCAmelCase     =			max_target_positions

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





