




'''simple docstring'''





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

import yaml




class 			_snake_case		(     yaml.SafeLoader							):




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[int]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__=False							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				super().construct_mapping(a__    ,							deep=a__							)
										self._check_no_duplicates_on_constructed_node(a__							)
										return mapping


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






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

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




class 			_snake_case		(     lowercase_							):
			# class attributes
			lowerCAmelCase_					:				Tuple							=   {"train_eval_index"}  # train-eval-index in the YAML metadata




			@classmethod
			def        lowerCAmelCase__    ( cls    ,							a__							)    ->    "DatasetMetadata":

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__ = None							)    ->    str:

										'''simple docstring'''




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




			@classmethod
			def        lowerCAmelCase__    ( cls    ,							a__							)    ->    "DatasetMetadata":

										'''simple docstring'''




										snake_case_      =				yaml.load(a__    ,							Loader=_NoDuplicateSafeLoader							) or {}

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




			def        lowerCAmelCase__    ( self							)    ->    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=a__    ,							allow_unicode=a__    ,							encoding="utf-8"    ,							).decode("utf-8"							)

_SCREAMING_SNAKE_CASE							:       str						=			{
    "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

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

		_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			Path(args.readme_filepath)
		_SCREAMING_SNAKE_CASE							:       Any						=			DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)










'''simple docstring'''





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 			_snake_case		(     unittest.TestCase						,						lowercase_							):




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				load_tool("text-classification"							)
										self.tool.setup()
										snake_case_      =				load_tool("text-classification"    ,							remote=a__							)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				self.tool("That's quite cool"    ,							["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				self.remote_tool("That's quite cool"    ,							["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self.tool(text="That's quite cool"    ,							labels=["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.remote_tool(text="That's quite cool"    ,							labels=["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




