





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()
__lowercase  :   Optional[Any]						      =logging.get_logger(__name__)




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224"  ,    out_features=["stage1", "stage2", "stage3", "stage4"]  )
   UpperCAmelCase_											=MaskFormerConfig(backbone_config=lowercase__  )

   UpperCAmelCase_											="huggingface/label-files"
   if "ade20k-full" in model_name:
      # this should be ok
      UpperCAmelCase_											=8_4_7
      UpperCAmelCase_											="maskformer-ade20k-full-id2label.json"
   elif "ade" in model_name:
      # this should be ok
      UpperCAmelCase_											=1_5_0
      UpperCAmelCase_											="ade20k-id2label.json"
   elif "coco-stuff" in model_name:
      # this should be ok
      UpperCAmelCase_											=1_7_1
      UpperCAmelCase_											="maskformer-coco-stuff-id2label.json"
   elif "coco" in model_name:
      # TODO
      UpperCAmelCase_											=1_3_3
      UpperCAmelCase_											="coco-panoptic-id2label.json"
   elif "cityscapes" in model_name:
      # this should be ok
      UpperCAmelCase_											=1_9
      UpperCAmelCase_											="cityscapes-id2label.json"
   elif "vistas" in model_name:
      # this should be ok
      UpperCAmelCase_											=6_5
      UpperCAmelCase_											="mapillary-vistas-id2label.json"

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

   return config




def     a__ (					lowercase__  ):






   '''simple docstring'''






   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__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=dct.pop(lowercase__  )
   UpperCAmelCase_											=val




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   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__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   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__ (					):






   '''simple docstring'''






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




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__ = False  ):






   '''simple docstring'''






   UpperCAmelCase_											=get_maskformer_config(lowercase__  )

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

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

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

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

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

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

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

   # verify results
   UpperCAmelCase_											=prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase_											=6_5
   elif "cityscapes" in model_name:
      UpperCAmelCase_											=6_5_5_3_5
   else:
      UpperCAmelCase_											=2_5_5
   UpperCAmelCase_											=True if "ade" in model_name else False
   UpperCAmelCase_											=MaskFormerImageProcessor(ignore_index=lowercase__  ,    reduce_labels=lowercase__  )

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

   UpperCAmelCase_											=model(**lowercase__  )

   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]  ,    lowercase__  ,    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(lowercase__  ).mkdir(exist_ok=lowercase__  )
      model.save_pretrained(lowercase__  )
      image_processor.save_pretrained(lowercase__  )

   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__":
     __lowercase  :   Dict						      =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."""
     )

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











from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml

class      A       (	yaml.SafeLoader						):



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=[self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase_											=[tuple(_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else key for key in keys]
         UpperCAmelCase_											=Counter(_lowerCAmelCase       )
         UpperCAmelCase_											=[key for key in counter if counter[key] > 1]
         if duplicate_keys:
            raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'       )






      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Optional[Any]=False       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=super().construct_mapping(_lowerCAmelCase		,  deep=_lowerCAmelCase       )
         self._check_no_duplicates_on_constructed_node(_lowerCAmelCase       )
         return mapping




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=list(readme_content.splitlines()  )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      UpperCAmelCase_											=full_content[1:].index("---"  ) + 1
      UpperCAmelCase_											="\n".join(full_content[1:sep_idx]  )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :]  )

   return None, "\n".join(lowercase__  )

class      A       (	__lowercase						):
      # class attributes
      _snake_case							     ={'''train_eval_index'''}  # train-eval-index in the YAML metadata



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Any		,  _lowerCAmelCase:						Path       )						->    "DatasetMetadata":
         '''simple docstring'''

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as readme_file:
            UpperCAmelCase_						,       UpperCAmelCase_											=_split_yaml_from_readme(readme_file.read()       )
         if yaml_string is not None:
            return cls.from_yaml_string(_lowerCAmelCase       )
         else:
            return cls()



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Path       )						->    List[str]:
         '''simple docstring'''

         if path.exists():
            with open(_lowerCAmelCase		,  encoding="utf-8"       ) as readme_file:
               UpperCAmelCase_											=readme_file.read()
         else:
            UpperCAmelCase_											=None
         UpperCAmelCase_											=self._to_readme(_lowerCAmelCase       )
         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as readme_file:
            readme_file.write(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    str:
         '''simple docstring'''

         if readme_content is not None:
            UpperCAmelCase_						,       UpperCAmelCase_											=_split_yaml_from_readme(_lowerCAmelCase       )
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n" + content
         else:
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n"
         return full_content



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Optional[Any]		,  _lowerCAmelCase:						str       )						->    "DatasetMetadata":
         '''simple docstring'''

         UpperCAmelCase_											=yaml.load(_lowerCAmelCase		,  Loader=_NoDuplicateSafeLoader       ) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase_											={
             (key.replace("-"		,  "_"       ) if key.replace("-"		,  "_"       ) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**_lowerCAmelCase       )






      def 	lowerCAmelCase__    (	self:						int       )						->    str:
         '''simple docstring'''

         return yaml.safe_dump(
             {
                 (key.replace("_"		,  "-"       ) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }		,  sort_keys=_lowerCAmelCase		,  allow_unicode=_lowerCAmelCase		,  encoding="utf-8"		,  ).decode("utf-8"       )

__lowercase  :   int						      ={
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
     from argparse import ArgumentParser

     __lowercase  :   List[Any]						      =ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
     ap.add_argument("""readme_filepath""")
     __lowercase  :   int						      =ap.parse_args()

     __lowercase  :   Optional[int]						      =Path(args.readme_filepath)
     __lowercase  :   Tuple						      =DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)




