




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__a							     =					logging.get_logger('transformers.models.encodec')

__a							     =					{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
__a							     =					{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
__a							     =					{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
__a							     =					{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
__a							     =					{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
__a							     =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__a							     =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__a							     =					[]
__a							     =					[]







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     for attribute in key.split('''.''' ):
          UpperCAmelCase_     :				Optional[Any]  =     getattr(_lowercase					,		_lowercase )

     if weight_type is not None:
          UpperCAmelCase_     :				List[Any]  =     getattr(_lowercase					,		_lowercase ).shape
     else:
          UpperCAmelCase_     :				Optional[int]  =     hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
              f''' {value.shape} for {full_name}''' )

     if weight_type == "weight":
          UpperCAmelCase_     :				Tuple  =     value
     elif weight_type == "weight_g":
          UpperCAmelCase_     :				Any  =     value
     elif weight_type == "weight_v":
          UpperCAmelCase_     :				List[Any]  =     value
     elif weight_type == "bias":
          UpperCAmelCase_     :				str  =     value
     elif weight_type == "running_mean":
          UpperCAmelCase_     :				Union[str, Any]  =     value
     elif weight_type == "running_var":
          UpperCAmelCase_     :				str  =     value
     elif weight_type == "num_batches_tracked":
          UpperCAmelCase_     :				List[Any]  =     value
     elif weight_type == "weight_ih_l0":
          UpperCAmelCase_     :				str  =     value
     elif weight_type == "weight_hh_l0":
          UpperCAmelCase_     :				str  =     value
     elif weight_type == "bias_ih_l0":
          UpperCAmelCase_     :				Optional[Any]  =     value
     elif weight_type == "bias_hh_l0":
          UpperCAmelCase_     :				int  =     value
     elif weight_type == "weight_ih_l1":
          UpperCAmelCase_     :				List[Any]  =     value
     elif weight_type == "weight_hh_l1":
          UpperCAmelCase_     :				int  =     value
     elif weight_type == "bias_ih_l1":
          UpperCAmelCase_     :				int  =     value
     elif weight_type == "bias_hh_l1":
          UpperCAmelCase_     :				List[str]  =     value
     else:
          UpperCAmelCase_     :				int  =     value

     logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.''' )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     for key in ignore_keys:
          if key.endswith('''.*''' ):
               if name.startswith(key[:-1] ):
                    return True
          elif ".*." in key:
               UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     key.split('''.*.''' )
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Tuple  =     []

     if model_name == "encodec_24khz" or "encodec_32khz":
          UpperCAmelCase_     :				Tuple  =     MAPPING_24K
     elif model_name == "encodec_48khz":
          UpperCAmelCase_     :				str  =     MAPPING_48K
     else:
          raise ValueError(f'''Unsupported model: {model_name}''' )

     for name, value in orig_dict.items():
          if should_ignore(_lowercase					,		_lowercase ):
               logger.info(f'''{name} was ignored''' )
               continue

          UpperCAmelCase_     :				int  =     False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     key.split('''.*.''' )
                    if prefix in name and suffix in name:
                         UpperCAmelCase_     :				Optional[Any]  =     suffix

               if key in name:
                    # HACK otherwise .embed gets initialized with .embed_avg too
                    if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ):
                         continue

                    UpperCAmelCase_     :				int  =     True
                    if "*" in mapped_key:
                         UpperCAmelCase_     :				Optional[int]  =     name.split(_lowercase )[0].split('''.''' )[-2]
                         UpperCAmelCase_     :				Optional[int]  =     mapped_key.replace('''*'''					,		_lowercase )
                    if "weight_g" in name:
                         UpperCAmelCase_     :				List[Any]  =     '''weight_g'''
                    elif "weight_v" in name:
                         UpperCAmelCase_     :				Tuple  =     '''weight_v'''
                    elif "weight_ih_l0" in name:
                         UpperCAmelCase_     :				Optional[int]  =     '''weight_ih_l0'''
                    elif "weight_hh_l0" in name:
                         UpperCAmelCase_     :				Optional[int]  =     '''weight_hh_l0'''
                    elif "bias_ih_l0" in name:
                         UpperCAmelCase_     :				Dict  =     '''bias_ih_l0'''
                    elif "bias_hh_l0" in name:
                         UpperCAmelCase_     :				Any  =     '''bias_hh_l0'''
                    elif "weight_ih_l1" in name:
                         UpperCAmelCase_     :				int  =     '''weight_ih_l1'''
                    elif "weight_hh_l1" in name:
                         UpperCAmelCase_     :				int  =     '''weight_hh_l1'''
                    elif "bias_ih_l1" in name:
                         UpperCAmelCase_     :				Dict  =     '''bias_ih_l1'''
                    elif "bias_hh_l1" in name:
                         UpperCAmelCase_     :				Union[str, Any]  =     '''bias_hh_l1'''
                    elif "bias" in name:
                         UpperCAmelCase_     :				List[str]  =     '''bias'''
                    elif "weight" in name:
                         UpperCAmelCase_     :				int  =     '''weight'''
                    elif "running_mean" in name:
                         UpperCAmelCase_     :				Tuple  =     '''running_mean'''
                    elif "running_var" in name:
                         UpperCAmelCase_     :				Dict  =     '''running_var'''
                    elif "num_batches_tracked" in name:
                         UpperCAmelCase_     :				Tuple  =     '''num_batches_tracked'''
                    else:
                         UpperCAmelCase_     :				Dict  =     None
                    set_recursively(_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase )
               continue
          if not is_used:
               unused_weights.append(_lowercase )

     logger.warning(f'''Unused weights: {unused_weights}''' )







@torch.no_grad()
def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=None					,		):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_     :				int  =     EncodecConfig.from_pretrained(_lowercase )
     else:
          UpperCAmelCase_     :				List[str]  =     EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          UpperCAmelCase_     :				Union[str, Any]  =     [8, 5, 4, 4]
          UpperCAmelCase_     :				str  =     [2.2]
          UpperCAmelCase_     :				Optional[Any]  =     64
          UpperCAmelCase_     :				str  =     32000
          UpperCAmelCase_     :				Tuple  =     2048
          UpperCAmelCase_     :				str  =     False
          UpperCAmelCase_     :				List[Any]  =     False
          UpperCAmelCase_     :				int  =     False
     elif model_name == "encodec_48khz":
          UpperCAmelCase_     :				List[str]  =     [8, 5, 4, 2]
          UpperCAmelCase_     :				Optional[int]  =     [3.0, 6.0, 12.0, 24.0]
          UpperCAmelCase_     :				Optional[int]  =     48000
          UpperCAmelCase_     :				List[str]  =     2
          UpperCAmelCase_     :				Optional[Any]  =     False
          UpperCAmelCase_     :				Any  =     '''time_group_norm'''
          UpperCAmelCase_     :				Union[str, Any]  =     True
          UpperCAmelCase_     :				Union[str, Any]  =     1.0
          UpperCAmelCase_     :				List[Any]  =     0.01
     else:
          raise ValueError(f'''Unknown model name: {model_name}''' )

     UpperCAmelCase_     :				int  =     EncodecModel(_lowercase )

     UpperCAmelCase_     :				List[Any]  =     EncodecFeatureExtractor(
         feature_size=config.audio_channels					,		sampling_rate=config.sampling_rate					,		chunk_length_s=config.chunk_length_s					,		overlap=config.overlap					,		)
     feature_extractor.save_pretrained(_lowercase )

     UpperCAmelCase_     :				int  =     torch.load(_lowercase )
     if "best_state" in original_checkpoint:
          # we might have a training state saved, in which case discard the yaml results and just retain the weights
          UpperCAmelCase_     :				Optional[int]  =     original_checkpoint['''best_state''']
     recursively_load_weights(_lowercase					,		_lowercase					,		_lowercase )
     model.save_pretrained(_lowercase )

     if repo_id:
          print('''Pushing to the hub...''' )
          feature_extractor.push_to_hub(_lowercase )
          model.push_to_hub(_lowercase )


if __name__ == "__main__":
     __a							     =					argparse.ArgumentParser()
     parser.add_argument(
         '--model',
         default='encodec_24khz',
         type=str,
         help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
     )
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     __a							     =					parser.parse_args()
     convert_checkpoint(
         args.model,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )






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 					=      logging.get_logger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   SwinConfig.from_pretrained(
		    """microsoft/swin-tiny-patch4-window7-224"""       , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     )
		SCREAMING_SNAKE_CASE__								=   MaskFormerConfig(backbone_config=UpperCamelCase__     )

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

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

		return config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   []
		# 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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Optional[int]     ):
		SCREAMING_SNAKE_CASE__								=   dct.pop(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   val






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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[Any]     ):
		# fmt: off
		SCREAMING_SNAKE_CASE__								=   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)
				SCREAMING_SNAKE_CASE__								=   state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''     )
				SCREAMING_SNAKE_CASE__								=   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
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[: hidden_size, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[:config.hidden_size]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[hidden_size : hidden_size * 2, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[-hidden_size :, :]
				SCREAMING_SNAKE_CASE__								=   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)
				SCREAMING_SNAKE_CASE__								=   state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''     )
				SCREAMING_SNAKE_CASE__								=   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
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[: hidden_size, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[:config.hidden_size]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[hidden_size : hidden_size * 2, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[-hidden_size :, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[-hidden_size :]
		# fmt: on






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






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				bool = False     ):
		SCREAMING_SNAKE_CASE__								=   get_maskformer_config(UpperCamelCase__     )

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

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

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

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

		# load 🤗 model
		SCREAMING_SNAKE_CASE__								=   MaskFormerForInstanceSegmentation(UpperCamelCase__     )
		model.eval()

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

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

		# verify results
		SCREAMING_SNAKE_CASE__								=   prepare_img()
		if "vistas" in model_name:
				SCREAMING_SNAKE_CASE__								=   65
		elif "cityscapes" in model_name:
				SCREAMING_SNAKE_CASE__								=   65_535
		else:
				SCREAMING_SNAKE_CASE__								=   255
		SCREAMING_SNAKE_CASE__								=   True if """ade""" in model_name else False
		SCREAMING_SNAKE_CASE__								=   MaskFormerImageProcessor(ignore_index=UpperCamelCase__       , reduce_labels=UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   image_processor(UpperCamelCase__       , return_tensors="""pt"""     )

		SCREAMING_SNAKE_CASE__								=   model(**UpperCamelCase__     )

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

		if model_name == "maskformer-swin-tiny-ade":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]     )
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       , UpperCamelCase__       , 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(UpperCamelCase__     ).mkdir(exist_ok=UpperCamelCase__     )
				model.save_pretrained(UpperCamelCase__     )
				image_processor.save_pretrained(UpperCamelCase__     )

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