'''simple docstring'''




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


__SCREAMING_SNAKE_CASE       :     Optional[List[str]]     =  None
__SCREAMING_SNAKE_CASE       :     Any     =  """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__SCREAMING_SNAKE_CASE       :     Dict     =  [
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]

@dataclass
class 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  bool						=						True
      __UpperCamelCase:  Optional[str]						=						None
      # Automatically constructed
      __UpperCamelCase:  ClassVar[str]						=						"PIL.Image.Image"
      __UpperCamelCase:  ClassVar[Any]						=						pa.struct({"bytes": pa.binary(), "path": pa.string()}    )
      __UpperCamelCase:  str						=						field(default="Image"							, init=snake_case__							, repr=snake_case__    )




      def __call__(       self						:				Any       ):
             return self.pa_type




      def      _A	(       self						:				Tuple						,					A						:				Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]       ):
             if config.PIL_AVAILABLE:
                    import PIL.Image
             else:
                    raise ImportError("To support encoding images, please install 'Pillow'."       )

             if isinstance(A						,					A       ):
                    _UpperCAmelCase     :  Optional[int]							      =    np.array(A       )

             if isinstance(A						,					A       ):
                    return {"path": value, "bytes": None}
             elif isinstance(A						,					A       ):
                    return {"path": None, "bytes": value}
             elif isinstance(A						,					np.ndarray       ):
                    # convert the image array to PNG/TIFF bytes
                    return encode_np_array(A       )
             elif isinstance(A						,					PIL.Image.Image       ):
                    # convert the PIL image to bytes (default format is PNG/TIFF)
                    return encode_pil_image(A       )
             elif value.get("path"       ) is not None and os.path.isfile(value["path"]       ):
                    # we set "bytes": None to not duplicate the data if they're already available locally
                    return {"bytes": None, "path": value.get("path"       )}
             elif value.get("bytes"       ) is not None or value.get("path"       ) is not None:
                    # store the image bytes, and path is used to infer the image format using the file extension
                    return {"bytes": value.get("bytes"       ), "path": value.get("path"       )}
             else:
                    raise ValueError(
                        F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""       )




      def      _A	(       self						:				List[str]						,					A						:				dict						,					A						:				Optional[Any]=None       ):
             if not self.decode:
                    raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."       )

             if config.PIL_AVAILABLE:
                    import PIL.Image
             else:
                    raise ImportError("To support decoding images, please install 'Pillow'."       )

             if token_per_repo_id is None:
                    _UpperCAmelCase     :  List[Any]							      =    {}

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    value["path"], value["bytes"]
             if bytes_ is None:
                    if path is None:
                           raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}."""       )
                    else:
                           if is_local_path(A       ):
                                  _UpperCAmelCase     :  List[str]							      =    PIL.Image.open(A       )
                           else:
                                  _UpperCAmelCase     :  int							      =    path.split("::"       )[-1]
                                  try:
                                         _UpperCAmelCase     :  Optional[Any]							      =    string_to_dict(A						,					config.HUB_DATASETS_URL       )["repo_id"]
                                         _UpperCAmelCase     :  int							      =    token_per_repo_id.get(A       )
                                  except ValueError:
                                         _UpperCAmelCase     :  Optional[Any]							      =    None
                                  with xopen(A						,					"rb"						,					use_auth_token=A       ) as f:
                                         _UpperCAmelCase     :  str							      =    BytesIO(f.read()       )
                                  _UpperCAmelCase     :  List[str]							      =    PIL.Image.open(bytes_       )
             else:
                    _UpperCAmelCase     :  Any							      =    PIL.Image.open(BytesIO(bytes_       )       )
             image.load()  # to avoid "Too many open files" errors
             return image




      def      _A	(       self						:				Optional[Any]       ):
             from .features import Value

             return (
                 self
                 if self.decode
                 else {
                     "bytes": Value("binary"       ),
                     "path": Value("string"       ),
                 }
             )




      def      _A	(       self						:				Optional[int]						,					A						:				Union[pa.StringArray, pa.StructArray, pa.ListArray]       ):
             if pa.types.is_string(storage.type       ):
                    _UpperCAmelCase     :  Any							      =    pa.array([None] * len(A       )						,					type=pa.binary()       )
                    _UpperCAmelCase     :  List[Any]							      =    pa.StructArray.from_arrays([bytes_array, storage]						,					["bytes", "path"]						,					mask=storage.is_null()       )
             elif pa.types.is_binary(storage.type       ):
                    _UpperCAmelCase     :  str							      =    pa.array([None] * len(A       )						,					type=pa.string()       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    pa.StructArray.from_arrays([storage, path_array]						,					["bytes", "path"]						,					mask=storage.is_null()       )
             elif pa.types.is_struct(storage.type       ):
                    if storage.type.get_field_index("bytes"       ) >= 0:
                           _UpperCAmelCase     :  Tuple							      =    storage.field("bytes"       )
                    else:
                           _UpperCAmelCase     :  str							      =    pa.array([None] * len(A       )						,					type=pa.binary()       )
                    if storage.type.get_field_index("path"       ) >= 0:
                           _UpperCAmelCase     :  Tuple							      =    storage.field("path"       )
                    else:
                           _UpperCAmelCase     :  Optional[Any]							      =    pa.array([None] * len(A       )						,					type=pa.string()       )
                    _UpperCAmelCase     :  List[Any]							      =    pa.StructArray.from_arrays([bytes_array, path_array]						,					["bytes", "path"]						,					mask=storage.is_null()       )
             elif pa.types.is_list(storage.type       ):
                    _UpperCAmelCase     :  List[Any]							      =    pa.array(
                        [encode_np_array(np.array(A       )       )["bytes"] if arr is not None else None for arr in storage.to_pylist()]						,					type=pa.binary()						,					)
                    _UpperCAmelCase     :  Union[str, Any]							      =    pa.array([None] * len(A       )						,					type=pa.string()       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    pa.StructArray.from_arrays(
                        [bytes_array, path_array]						,					["bytes", "path"]						,					mask=bytes_array.is_null()       )
             return array_cast(A						,					self.pa_type       )





      def      _A	(       self						:				List[str]						,					A						:				pa.StructArray       ):

             @no_op_if_value_is_null
             def path_to_bytes(A						:				Dict       ):
                    with xopen(A						,					"rb"       ) as f:
                           _UpperCAmelCase     :  List[str]							      =    f.read()
                    return bytes_

             _UpperCAmelCase     :  str							      =    pa.array(
                 [
                     (path_to_bytes(x["path"]       ) if x["bytes"] is None else x["bytes"]) if x is not None else None
                     for x in storage.to_pylist()
                 ]						,					type=pa.binary()						,					)
             _UpperCAmelCase     :  int							      =    pa.array(
                 [os.path.basename(A       ) if path is not None else None for path in storage.field("path"       ).to_pylist()]						,					type=pa.string()						,					)
             _UpperCAmelCase     :  Union[str, Any]							      =    pa.StructArray.from_arrays([bytes_array, path_array]						,					["bytes", "path"]						,					mask=bytes_array.is_null()       )
             return array_cast(A						,					self.pa_type       )

def 	UpperCamelCase_							(					)    ->    List[str]:


       """simple docstring"""







       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("To support encoding images, please install 'Pillow'."    )

       global _IMAGE_COMPRESSION_FORMATS
       if _IMAGE_COMPRESSION_FORMATS is None:
              PIL.Image.init()
              _UpperCAmelCase     :  Any							      =    list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
       return _IMAGE_COMPRESSION_FORMATS

def 	UpperCamelCase_							(					_UpperCAmelCase  :      "PIL.Image.Image"    )    ->    bytes:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    BytesIO()
       if image.format in list_image_compression_formats():
              _UpperCAmelCase     :  Tuple							      =    image.format
       else:
              _UpperCAmelCase     :  Optional[Any]							      =    "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
       image.save(_UpperCAmelCase			,	format=_UpperCAmelCase    )
       return buffer.getvalue()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      "PIL.Image.Image"    )    ->    dict:


       """simple docstring"""







       if hasattr(_UpperCAmelCase			,	"filename"    ) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(_UpperCAmelCase    )}

def 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray    )    ->    dict:


       """simple docstring"""







       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("To support encoding images, please install 'Pillow'."    )

       _UpperCAmelCase     :  Union[str, Any]							      =    array.dtype
       _UpperCAmelCase     :  Tuple							      =    dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
       _UpperCAmelCase     :  List[str]							      =    dtype.kind
       _UpperCAmelCase     :  Dict							      =    dtype.itemsize

       _UpperCAmelCase     :  Any							      =    None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              _UpperCAmelCase     :  Dict							      =    np.dtype("|u1"    )
              if dtype_kind not in ["u", "i"]:
                     raise TypeError(
                         F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""    )
              if dtype is not dest_dtype:
                     warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""    )
    # Exact match
       elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
              _UpperCAmelCase     :  Optional[Any]							      =    dtype
       else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
              while dtype_itemsize >= 1:
                     _UpperCAmelCase     :  Union[str, Any]							      =    dtype_byteorder + dtype_kind + str(_UpperCAmelCase    )
                     _UpperCAmelCase     :  List[Any]							      =    np.dtype(_UpperCAmelCase    )
                     if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                            warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""    )
                            break
                     else:
                            dtype_itemsize //= 2

       if dest_dtype is None:
              raise TypeError(
                  F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""    )
       _UpperCAmelCase     :  Optional[Any]							      =    PIL.Image.fromarray(array.astype(_UpperCAmelCase    )    )
       return {"path": None, "bytes": image_to_bytes(_UpperCAmelCase    )}


def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    )    ->    List[dict]:


       """simple docstring"""







       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("To support encoding images, please install 'Pillow'."    )

       if objs:
              _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    first_non_null_value(_UpperCAmelCase    )
              if isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                     return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
              if isinstance(_UpperCAmelCase			,	np.ndarray    ):
                     _UpperCAmelCase     :  Dict							      =    no_op_if_value_is_null(_UpperCAmelCase    )
                     return [obj_to_image_dict_func(_UpperCAmelCase    ) for obj in objs]
              elif isinstance(_UpperCAmelCase			,	PIL.Image.Image    ):
                     _UpperCAmelCase     :  Optional[int]							      =    no_op_if_value_is_null(_UpperCAmelCase    )
                     return [obj_to_image_dict_func(_UpperCAmelCase    ) for obj in objs]
              else:
                     return objs
       else:
              return objs


'''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()
__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  logging.get_logger(__name__)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    SwinConfig.from_pretrained(
           "microsoft/swin-tiny-patch4-window7-224"			,	out_features=["stage1", "stage2", "stage3", "stage4"]    )
       _UpperCAmelCase     :  List[Any]							      =    MaskFormerConfig(backbone_config=_UpperCAmelCase    )

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

       _UpperCAmelCase     :  List[Any]							      =    json.load(open(hf_hub_download(_UpperCAmelCase			,	_UpperCAmelCase			,	repo_type="dataset"    )			,	"r"    )    )
       _UpperCAmelCase     :  Tuple							      =    {int(_UpperCAmelCase    ): v for k, v in idalabel.items()}

       return config

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[int]    )    ->    List[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  Dict							      =    []
       # 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 	UpperCamelCase_							(					_UpperCAmelCase  :      int			,	_UpperCAmelCase  :      Tuple			,	_UpperCAmelCase  :      List[Any]    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    dct.pop(_UpperCAmelCase    )
       _UpperCAmelCase     :  List[str]							      =    val

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Tuple    )    ->    Optional[int]:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      str    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  Union[str, Any]							      =    config.decoder_config.hidden_size
       for idx in range(config.decoder_config.decoder_layers    ):
              # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
              _UpperCAmelCase     :  Dict							      =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""    )
              _UpperCAmelCase     :  Dict							      =    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     :  int							      =    in_proj_weight[: hidden_size, :]
              _UpperCAmelCase     :  Union[str, Any]							      =    in_proj_bias[:config.hidden_size]
              _UpperCAmelCase     :  List[str]							      =    in_proj_weight[hidden_size : hidden_size * 2, :]
              _UpperCAmelCase     :  List[str]							      =    in_proj_bias[hidden_size : hidden_size * 2]
              _UpperCAmelCase     :  int							      =    in_proj_weight[-hidden_size :, :]
              _UpperCAmelCase     :  Optional[Any]							      =    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     :  Optional[Any]							      =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""    )
              _UpperCAmelCase     :  Tuple							      =    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     :  Any							      =    in_proj_weight[: hidden_size, :]
              _UpperCAmelCase     :  Tuple							      =    in_proj_bias[:config.hidden_size]
              _UpperCAmelCase     :  Dict							      =    in_proj_weight[hidden_size : hidden_size * 2, :]
              _UpperCAmelCase     :  Dict							      =    in_proj_bias[hidden_size : hidden_size * 2]
              _UpperCAmelCase     :  Optional[int]							      =    in_proj_weight[-hidden_size :, :]
              _UpperCAmelCase     :  Union[str, Any]							      =    in_proj_bias[-hidden_size :]
       # fmt: on

def 	UpperCamelCase_							(					)    ->    torch.Tensor:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    "http://images.cocodataset.org/val2017/000000039769.jpg"
       _UpperCAmelCase     :  Any							      =    Image.open(requests.get(_UpperCAmelCase			,	stream=_UpperCAmelCase    ).raw    )
       return im

@torch.no_grad()
def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      bool = False    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    get_maskformer_config(_UpperCAmelCase    )

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

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

       # rename keys
       _UpperCAmelCase     :  Any							      =    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():
              _UpperCAmelCase     :  Tuple							      =    torch.from_numpy(_UpperCAmelCase    )

       # load 🤗 model
       _UpperCAmelCase     :  Union[str, Any]							      =    MaskFormerForInstanceSegmentation(_UpperCAmelCase    )
       model.eval()

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

       _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    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
       _UpperCAmelCase     :  Optional[int]							      =    prepare_img()
       if "vistas" in model_name:
              _UpperCAmelCase     :  int							      =    65
       elif "cityscapes" in model_name:
              _UpperCAmelCase     :  Tuple							      =    65_535
       else:
              _UpperCAmelCase     :  Any							      =    255
       _UpperCAmelCase     :  Optional[Any]							      =    True if "ade" in model_name else False
       _UpperCAmelCase     :  Optional[int]							      =    MaskFormerImageProcessor(ignore_index=_UpperCAmelCase			,	reduce_labels=_UpperCAmelCase    )

       _UpperCAmelCase     :  Optional[int]							      =    image_processor(_UpperCAmelCase			,	return_tensors="pt"    )

       _UpperCAmelCase     :  List[Any]							      =    model(**_UpperCAmelCase    )

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

       if model_name == "maskformer-swin-tiny-ade":
              _UpperCAmelCase     :  Tuple							      =    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__":
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""maskformer-swin-tiny-ade""",
      type=str,
      help=("""Name of the MaskFormer model you'd like to convert""",),
  )
  parser.add_argument(
      """--checkpoint_path""",
      default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
      type=str,
      help="""Path to the original state dict (.pth file).""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

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

