


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			int              =      "deta"
   __UpperCAmelCase			:			Union[str, Any]              =      {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }


   def __init__(self					:						Any	,  __UpperCAmelCase					:						Tuple=None	,  __UpperCAmelCase					:						Tuple=9_0_0	,  __UpperCAmelCase					:						int=2_0_4_8	,  __UpperCAmelCase					:						Dict=6	,  __UpperCAmelCase					:						Any=2_0_4_8	,  __UpperCAmelCase					:						List[str]=8	,  __UpperCAmelCase					:						Any=6	,  __UpperCAmelCase					:						Tuple=1_0_2_4	,  __UpperCAmelCase					:						Optional[Any]=8	,  __UpperCAmelCase					:						str=0.0	,  __UpperCAmelCase					:						Any=True	,  __UpperCAmelCase					:						Union[str, Any]="relu"	,  __UpperCAmelCase					:						Any=2_5_6	,  __UpperCAmelCase					:						Dict=0.1	,  __UpperCAmelCase					:						str=0.0	,  __UpperCAmelCase					:						Dict=0.0	,  __UpperCAmelCase					:						Union[str, Any]=0.02	,  __UpperCAmelCase					:						Union[str, Any]=1.0	,  __UpperCAmelCase					:						int=True	,  __UpperCAmelCase					:						int=False	,  __UpperCAmelCase					:						Dict="sine"	,  __UpperCAmelCase					:						Dict=5	,  __UpperCAmelCase					:						List[str]=4	,  __UpperCAmelCase					:						Tuple=4	,  __UpperCAmelCase					:						str=True	,  __UpperCAmelCase					:						Union[str, Any]=3_0_0	,  __UpperCAmelCase					:						Optional[int]=True	,  __UpperCAmelCase					:						Tuple=True	,  __UpperCAmelCase					:						Any=1	,  __UpperCAmelCase					:						Optional[int]=5	,  __UpperCAmelCase					:						List[str]=2	,  __UpperCAmelCase					:						List[str]=1	,  __UpperCAmelCase					:						Any=1	,  __UpperCAmelCase					:						Optional[Any]=5	,  __UpperCAmelCase					:						List[str]=2	,  __UpperCAmelCase					:						Any=0.1	,  __UpperCAmelCase					:						Tuple=0.25	,  **__UpperCAmelCase					:						List[Any]	,  )						->    str:
         """simple docstring"""



         if backbone_config is None:
               logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
               UpperCAmelCase__								=  CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]		)
         else:
               if isinstance(a__	,  a__		):
                     UpperCAmelCase__								=  backbone_config.pop("model_type"		)
                     UpperCAmelCase__								=  CONFIG_MAPPING[backbone_model_type]
                     UpperCAmelCase__								=  config_class.from_dict(a__		)

         UpperCAmelCase__								=  backbone_config
         UpperCAmelCase__								=  num_queries
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  d_model
         UpperCAmelCase__								=  encoder_ffn_dim
         UpperCAmelCase__								=  encoder_layers
         UpperCAmelCase__								=  encoder_attention_heads
         UpperCAmelCase__								=  decoder_ffn_dim
         UpperCAmelCase__								=  decoder_layers
         UpperCAmelCase__								=  decoder_attention_heads
         UpperCAmelCase__								=  dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  activation_function
         UpperCAmelCase__								=  init_std
         UpperCAmelCase__								=  init_xavier_std
         UpperCAmelCase__								=  encoder_layerdrop
         UpperCAmelCase__								=  auxiliary_loss
         UpperCAmelCase__								=  position_embedding_type
         # deformable attributes
         UpperCAmelCase__								=  num_feature_levels
         UpperCAmelCase__								=  encoder_n_points
         UpperCAmelCase__								=  decoder_n_points
         UpperCAmelCase__								=  two_stage
         UpperCAmelCase__								=  two_stage_num_proposals
         UpperCAmelCase__								=  with_box_refine
         UpperCAmelCase__								=  assign_first_stage
         if two_stage is True and with_box_refine is False:
               raise ValueError("If two_stage is True, with_box_refine must be True."		)
         # Hungarian matcher
         UpperCAmelCase__								=  class_cost
         UpperCAmelCase__								=  bbox_cost
         UpperCAmelCase__								=  giou_cost
         # Loss coefficients
         UpperCAmelCase__								=  mask_loss_coefficient
         UpperCAmelCase__								=  dice_loss_coefficient
         UpperCAmelCase__								=  bbox_loss_coefficient
         UpperCAmelCase__								=  giou_loss_coefficient
         UpperCAmelCase__								=  eos_coefficient
         UpperCAmelCase__								=  focal_alpha
         super().__init__(is_encoder_decoder=a__	,  **a__		)


   @property
   def       lowercase_				(self					:						Tuple		)						->    List[str]:
         """simple docstring"""



         return self.encoder_attention_heads


   @property
   def       lowercase_				(self					:						str		)						->    List[str]:
         """simple docstring"""



         return self.d_model





   def       lowercase_				(self					:						Any		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  copy.deepcopy(self.__dict__		)
         UpperCAmelCase__								=  self.backbone_config.to_dict()
         UpperCAmelCase__								=  self.__class__.model_type
         return output




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

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



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





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''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(__A							)





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  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(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



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


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



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


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  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(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    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=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    '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

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)



