



"""simple docstring"""





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()
lowerCamelCase      =		logging.get_logger("""transformers.models.encodec""")

lowerCamelCase      =		{
    "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",
}
lowerCamelCase      =		{
    "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",
}
lowerCamelCase      =		{
    "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",
}
lowerCamelCase      =		{
    "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",
}
lowerCamelCase      =		{
    "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",
}
lowerCamelCase      =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCamelCase      =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCamelCase      =		[]
lowerCamelCase      =		[]
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      for attribute in key.split("."   ):
            UpperCAmelCase_    				=							getattr(UpperCAmelCase__			,    UpperCAmelCase__   )

      if weight_type is not None:
            UpperCAmelCase_    				=							getattr(UpperCAmelCase__			,    UpperCAmelCase__   ).shape
      else:
            UpperCAmelCase_    				=							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_    				=							value
      elif weight_type == "weight_g":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_v":
            UpperCAmelCase_    				=							value
      elif weight_type == "bias":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_mean":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_var":
            UpperCAmelCase_    				=							value
      elif weight_type == "num_batches_tracked":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_ih_l0":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_hh_l0":
            UpperCAmelCase_    				=							value
      elif weight_type == "bias_ih_l0":
            UpperCAmelCase_    				=							value
      elif weight_type == "bias_hh_l0":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_ih_l1":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_hh_l1":
            UpperCAmelCase_    				=							value
      elif weight_type == "bias_ih_l1":
            UpperCAmelCase_    				=							value
      elif weight_type == "bias_hh_l1":
            UpperCAmelCase_    				=							value
      else:
            UpperCAmelCase_    				=							value

      logger.info(f"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."""   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      for key in ignore_keys:
            if key.endswith(".*"   ):
                  if name.startswith(key[:-1]   ):
                        return True
            elif ".*." in key:
                  UpperCAmelCase_    				=							key.split(".*."   )
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]

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

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

            UpperCAmelCase_    				=							False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        UpperCAmelCase_    				=							key.split(".*."   )
                        if prefix in name and suffix in name:
                              UpperCAmelCase_    				=							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_    				=							True
                        if "*" in mapped_key:
                              UpperCAmelCase_    				=							name.split(UpperCAmelCase__   )[0].split("."   )[-2]
                              UpperCAmelCase_    				=							mapped_key.replace("*"			,    UpperCAmelCase__   )
                        if "weight_g" in name:
                              UpperCAmelCase_    				=							"""weight_g"""
                        elif "weight_v" in name:
                              UpperCAmelCase_    				=							"""weight_v"""
                        elif "weight_ih_l0" in name:
                              UpperCAmelCase_    				=							"""weight_ih_l0"""
                        elif "weight_hh_l0" in name:
                              UpperCAmelCase_    				=							"""weight_hh_l0"""
                        elif "bias_ih_l0" in name:
                              UpperCAmelCase_    				=							"""bias_ih_l0"""
                        elif "bias_hh_l0" in name:
                              UpperCAmelCase_    				=							"""bias_hh_l0"""
                        elif "weight_ih_l1" in name:
                              UpperCAmelCase_    				=							"""weight_ih_l1"""
                        elif "weight_hh_l1" in name:
                              UpperCAmelCase_    				=							"""weight_hh_l1"""
                        elif "bias_ih_l1" in name:
                              UpperCAmelCase_    				=							"""bias_ih_l1"""
                        elif "bias_hh_l1" in name:
                              UpperCAmelCase_    				=							"""bias_hh_l1"""
                        elif "bias" in name:
                              UpperCAmelCase_    				=							"""bias"""
                        elif "weight" in name:
                              UpperCAmelCase_    				=							"""weight"""
                        elif "running_mean" in name:
                              UpperCAmelCase_    				=							"""running_mean"""
                        elif "running_var" in name:
                              UpperCAmelCase_    				=							"""running_var"""
                        elif "num_batches_tracked" in name:
                              UpperCAmelCase_    				=							"""num_batches_tracked"""
                        else:
                              UpperCAmelCase_    				=							None
                        set_recursively(UpperCAmelCase__			,    UpperCAmelCase__			,    UpperCAmelCase__			,    UpperCAmelCase__			,    UpperCAmelCase__   )
                  continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase__   )

      logger.warning(f"""Unused weights: {unused_weights}"""   )
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    lowerCAmelCase__=None			,    ):
      if config_path is not None:
            UpperCAmelCase_    				=							EncodecConfig.from_pretrained(UpperCAmelCase__   )
      else:
            UpperCAmelCase_    				=							EncodecConfig()

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

      UpperCAmelCase_    				=							EncodecModel(UpperCAmelCase__   )

      UpperCAmelCase_    				=							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(UpperCAmelCase__   )

      UpperCAmelCase_    				=							torch.load(UpperCAmelCase__   )
      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_    				=							original_checkpoint["""best_state"""]
      recursively_load_weights(UpperCAmelCase__			,    UpperCAmelCase__			,    UpperCAmelCase__   )
      model.save_pretrained(UpperCAmelCase__   )

      if repo_id:
            print("Pushing to the hub..."   )
            feature_extractor.push_to_hub(UpperCAmelCase__   )
            model.push_to_hub(UpperCAmelCase__   )


if __name__ == "__main__":
  lowerCamelCase      =		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."""
  )

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









"""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()
lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							SwinConfig.from_pretrained(
          "microsoft/swin-tiny-patch4-window7-224"			,    out_features=["stage1", "stage2", "stage3", "stage4"]   )
      UpperCAmelCase_    				=							MaskFormerConfig(backbone_config=lowerCAmelCase__   )

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

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

      return config
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      # 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__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dct.pop(lowerCAmelCase__   )
      UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
      for i in range(len(backbone_config.depths   )   ):
            UpperCAmelCase_    				=							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)
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""   )
                  UpperCAmelCase_    				=							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
                  UpperCAmelCase_    				=							in_proj_weight[:dim, :]
                  UpperCAmelCase_    				=							in_proj_bias[: dim]
                  UpperCAmelCase_    				=							in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_    				=							in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[-dim :]
                  # fmt: on
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # fmt: off
      UpperCAmelCase_    				=							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)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							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)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							in_proj_bias[-hidden_size :]
      # fmt: on
def      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   )
      return im
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							get_maskformer_config(lowerCAmelCase__   )

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

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

      # rename keys
      UpperCAmelCase_    				=							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():
            UpperCAmelCase_    				=							torch.from_numpy(lowerCAmelCase__   )

      # load 🤗 model
      UpperCAmelCase_    				=							MaskFormerForInstanceSegmentation(lowerCAmelCase__   )
      model.eval()

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

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

      UpperCAmelCase_    				=							image_processor(lowerCAmelCase__			,    return_tensors="pt"   )

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

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

      if model_name == "maskformer-swin-tiny-ade":
            UpperCAmelCase_    				=							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]			,    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__":
  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
  )




