



"""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    : int							=							logging.get_logger(__name__)

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str    )  ->       Union[str, Any]:





     '''simple docstring'''

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

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

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

     return config

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]    )  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   []
     # stem
     # fmt: off
     rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")    )
     rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")    )
     rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")    )
     rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")    )
     # stages
     for i in range(len(config.backbone_config.depths    )    ):
          for j in range(config.backbone_config.depths[i]    ):
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')    )
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')    )

          if i < 3:
               rename_keys.append((f'backbone.layers.{i}.downsample.reduction.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')    )
               rename_keys.append((f'backbone.layers.{i}.downsample.norm.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')    )
               rename_keys.append((f'backbone.layers.{i}.downsample.norm.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')    )
          rename_keys.append((f'backbone.norm{i}.weight', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')    )
          rename_keys.append((f'backbone.norm{i}.bias', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')    )

     # FPN
     rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")    )
     rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")    )
     rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")    )
     for source_index, target_index in zip(range(3						,  0						,  -1    )						,  range(0						,  3    )    ):
          rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')    )
          rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')    )
          rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')    )
          rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')    )
          rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')    )
          rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')    )
     rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")    )
     rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")    )

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers    ):
          # self-attention out projection
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')    )
          # cross-attention out projection
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')    )
          # MLP 1
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias')    )
          # MLP 2
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias')    )
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')    )
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')    )
          # layernorm 3 (final layernorm)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')    )

     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")    )
     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")    )

     # heads on top
     rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")    )

     rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")    )
     rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")    )

     rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")    )
     rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")    )

     for i in range(3    ):
          rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight')    )
          rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias')    )
     # fmt: on

     return rename_keys

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						Dict    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   dct.pop(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Any        =   val

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       str:





     '''simple docstring'''

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   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      :     List[Any]        =   state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'    )
          _UpperCAmelCase      :     Union[str, Any]        =   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      :     List[Any]        =   in_proj_weight[: hidden_size, :]
          _UpperCAmelCase      :     List[Any]        =   in_proj_bias[:config.hidden_size]
          _UpperCAmelCase      :     str        =   in_proj_weight[hidden_size : hidden_size * 2, :]
          _UpperCAmelCase      :     Dict        =   in_proj_bias[hidden_size : hidden_size * 2]
          _UpperCAmelCase      :     Union[str, Any]        =   in_proj_weight[-hidden_size :, :]
          _UpperCAmelCase      :     List[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      :     Dict        =   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      :     str        =   in_proj_weight[: hidden_size, :]
          _UpperCAmelCase      :     List[str]        =   in_proj_bias[:config.hidden_size]
          _UpperCAmelCase      :     Any        =   in_proj_weight[hidden_size : hidden_size * 2, :]
          _UpperCAmelCase      :     str        =   in_proj_bias[hidden_size : hidden_size * 2]
          _UpperCAmelCase      :     int        =   in_proj_weight[-hidden_size :, :]
          _UpperCAmelCase      :     List[str]        =   in_proj_bias[-hidden_size :]
     # fmt: on

def 						__snake_case						(				)  ->       torch.Tensor:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   "http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase      :     List[str]        =   Image.open(requests.get(SCREAMING_SNAKE_CASE__						,  stream=SCREAMING_SNAKE_CASE__    ).raw    )
     return im

@torch.no_grad()
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						bool = False    )  ->       Any:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   get_maskformer_config(SCREAMING_SNAKE_CASE__    )

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

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

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

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

     # load 🤗 model
     _UpperCAmelCase      :     List[Any]        =   MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__    )
     model.eval()

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

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

     # verify results
     _UpperCAmelCase      :     str        =   prepare_img()
     if "vistas" in model_name:
          _UpperCAmelCase      :     Any        =   65
     elif "cityscapes" in model_name:
          _UpperCAmelCase      :     Union[str, Any]        =   65_535
     else:
          _UpperCAmelCase      :     int        =   255
     _UpperCAmelCase      :     Dict        =   True if "ade" in model_name else False
     _UpperCAmelCase      :     int        =   MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE__						,  reduce_labels=SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     List[Any]        =   image_processor(SCREAMING_SNAKE_CASE__						,  return_tensors="pt"    )

     _UpperCAmelCase      :     int        =   model(**SCREAMING_SNAKE_CASE__    )

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

     if model_name == "maskformer-swin-tiny-ade":
          _UpperCAmelCase      :     Tuple        =   torch.tensor(
              [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]    )
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,  SCREAMING_SNAKE_CASE__						,  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(SCREAMING_SNAKE_CASE__    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
          model.save_pretrained(SCREAMING_SNAKE_CASE__    )
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE__    )

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





"""simple docstring"""




import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase    : Dict							=							"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase    : List[Any]							=							importlib.util.spec_from_file_location(
    "transformers",
    os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
_lowerCAmelCase    : Tuple							=							spec.loader.load_module()

_lowerCAmelCase    : Tuple							=							transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCAmelCase    : Optional[int]							=							re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")


_lowerCAmelCase    : Optional[int]							=							{
    "CLIPConfigMixin",
    "DecisionTransformerConfigMixin",
    "EncoderDecoderConfigMixin",
    "RagConfigMixin",
    "SpeechEncoderDecoderConfigMixin",
    "VisionEncoderDecoderConfigMixin",
    "VisionTextDualEncoderConfigMixin",
}

def 						__snake_case						(				)  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   []

     for config_class in list(CONFIG_MAPPING.values()    ):
          _UpperCAmelCase      :     Union[str, Any]        =   False

          # source code of `config_class`
          _UpperCAmelCase      :     Optional[int]        =   inspect.getsource(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     List[Any]        =   _re_checkpoint.findall(SCREAMING_SNAKE_CASE__    )

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               _UpperCAmelCase      :     Optional[Any]        =   f'https://huggingface.co/{ckpt_name}'
               if ckpt_link == ckpt_link_from_name:
                    _UpperCAmelCase      :     Optional[Any]        =   True
                    break

          _UpperCAmelCase      :     int        =   config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(SCREAMING_SNAKE_CASE__    )

     if len(SCREAMING_SNAKE_CASE__    ) > 0:
          _UpperCAmelCase      :     List[str]        =   "\n".join(sorted(SCREAMING_SNAKE_CASE__    )    )
          raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'    )


if __name__ == "__main__":
 check_config_docstrings_have_checkpoints()
