

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 							__UpperCamelCase			( lowercase_  ):





				'''simple docstring'''



				__a							: Optional[int]										='''deta'''
				__a							: str										={
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}



				def __init__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=9_00       , UpperCAmelCase_=20_48       , UpperCAmelCase_=6       , UpperCAmelCase_=20_48       , UpperCAmelCase_=8       , UpperCAmelCase_=6       , UpperCAmelCase_=10_24       , UpperCAmelCase_=8       , UpperCAmelCase_=0.0       , UpperCAmelCase_=True       , UpperCAmelCase_="relu"       , UpperCAmelCase_=2_56       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.02       , UpperCAmelCase_=1.0       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_="sine"       , UpperCAmelCase_=5       , UpperCAmelCase_=4       , UpperCAmelCase_=4       , UpperCAmelCase_=True       , UpperCAmelCase_=3_00       , UpperCAmelCase_=True       , UpperCAmelCase_=True       , UpperCAmelCase_=1       , UpperCAmelCase_=5       , UpperCAmelCase_=2       , UpperCAmelCase_=1       , UpperCAmelCase_=1       , UpperCAmelCase_=5       , UpperCAmelCase_=2       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.25       , **UpperCAmelCase_       , ):
											if backbone_config is None:
																		logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
																		lowerCAmelCase   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']						)
											else:
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):
																									lowerCAmelCase   =		backbone_config.pop('''model_type'''						)
																									lowerCAmelCase   =		CONFIG_MAPPING[backbone_model_type]
																									lowerCAmelCase   =		config_class.from_dict(UpperCAmelCase_						)

											lowerCAmelCase   =		backbone_config
											lowerCAmelCase   =		num_queries
											lowerCAmelCase   =		max_position_embeddings
											lowerCAmelCase   =		d_model
											lowerCAmelCase   =		encoder_ffn_dim
											lowerCAmelCase   =		encoder_layers
											lowerCAmelCase   =		encoder_attention_heads
											lowerCAmelCase   =		decoder_ffn_dim
											lowerCAmelCase   =		decoder_layers
											lowerCAmelCase   =		decoder_attention_heads
											lowerCAmelCase   =		dropout
											lowerCAmelCase   =		attention_dropout
											lowerCAmelCase   =		activation_dropout
											lowerCAmelCase   =		activation_function
											lowerCAmelCase   =		init_std
											lowerCAmelCase   =		init_xavier_std
											lowerCAmelCase   =		encoder_layerdrop
											lowerCAmelCase   =		auxiliary_loss
											lowerCAmelCase   =		position_embedding_type
											# deformable attributes
											lowerCAmelCase   =		num_feature_levels
											lowerCAmelCase   =		encoder_n_points
											lowerCAmelCase   =		decoder_n_points
											lowerCAmelCase   =		two_stage
											lowerCAmelCase   =		two_stage_num_proposals
											lowerCAmelCase   =		with_box_refine
											lowerCAmelCase   =		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
											lowerCAmelCase   =		class_cost
											lowerCAmelCase   =		bbox_cost
											lowerCAmelCase   =		giou_cost
											# Loss coefficients
											lowerCAmelCase   =		mask_loss_coefficient
											lowerCAmelCase   =		dice_loss_coefficient
											lowerCAmelCase   =		bbox_loss_coefficient
											lowerCAmelCase   =		giou_loss_coefficient
											lowerCAmelCase   =		eos_coefficient
											lowerCAmelCase   =		focal_alpha
											super().__init__(is_encoder_decoder=UpperCAmelCase_       , **UpperCAmelCase_						)



				@property
				def       __snake_case		( self						):
											return self.encoder_attention_heads



				@property
				def       __snake_case		( self						):
											return self.d_model






				def       __snake_case		( self						):
											lowerCAmelCase   =		copy.deepcopy(self.__dict__						)
											lowerCAmelCase   =		self.backbone_config.to_dict()
											lowerCAmelCase   =		self.__class__.model_type
											return output









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

import yaml


class 							__UpperCamelCase			( yaml.SafeLoader  ):





				'''simple docstring'''



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






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=False						):
											lowerCAmelCase   =		super().construct_mapping(UpperCAmelCase_       , deep=UpperCAmelCase_						)
											self._check_no_duplicates_on_constructed_node(UpperCAmelCase_						)
											return mapping




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

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


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										={"""train_eval_index"""}  # train-eval-index in the YAML metadata



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											with open(UpperCAmelCase_       , encoding='''utf-8'''						) as readme_file:
																		lowerCAmelCase   ,     lowerCAmelCase   =		_split_yaml_from_readme(readme_file.read()						)
											if yaml_string is not None:
																		return cls.from_yaml_string(UpperCAmelCase_						)
											else:
																		return cls()



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



				def       __snake_case		( self       , UpperCAmelCase_ = None						):
											if readme_content is not None:
																		lowerCAmelCase   ,     lowerCAmelCase   =		_split_yaml_from_readme(UpperCAmelCase_						)
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n''' + content
											else:
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n'''
											return full_content



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											lowerCAmelCase   =		yaml.load(UpperCAmelCase_       , Loader=_NoDuplicateSafeLoader						) or {}

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






				def       __snake_case		( self						):
											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)



