

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

import yaml

class __UpperCamelCase     (				yaml.SafeLoader		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= [self.constructed_objects[key_node] for key_node, _ in node.value]
						UpperCAmelCase__:	int									= [tuple(lowerCamelCase__						) if isinstance(lowerCamelCase__				,      lowerCamelCase__						) else key for key in keys]
						UpperCAmelCase__:	Union[str, Any]									= Counter(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= [key for key in counter if counter[key] > 1]
						if duplicate_keys:
							raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}"						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__=False						):
						UpperCAmelCase__:	Tuple									= super().construct_mapping(lowerCamelCase__				,      deep=lowerCamelCase__						)
						self._check_no_duplicates_on_constructed_node(lowerCamelCase__						)
						return mapping



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= list(readme_content.splitlines()      )
	if full_content and full_content[0] == "---" and "---" in full_content[1:]:
		UpperCAmelCase__:	int									= full_content[1:].index("---"      ) + 1
		UpperCAmelCase__:	Union[str, Any]									= "\n".join(full_content[1:sep_idx]      )
		return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

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

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"train_eval_index"}  # train-eval-index in the YAML metadata





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__						):
						with open(lowerCamelCase__				,      encoding="utf-8"						) as readme_file:
							UpperCAmelCase__							,						UpperCAmelCase__:	int									= _split_yaml_from_readme(readme_file.read()						)
						if yaml_string is not None:
							return cls.from_yaml_string(lowerCamelCase__						)
						else:
							return cls()





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						if path.exists():
							with open(lowerCamelCase__				,      encoding="utf-8"						) as readme_file:
								UpperCAmelCase__:	Optional[int]									= readme_file.read()
						else:
							UpperCAmelCase__:	Tuple									= None
						UpperCAmelCase__:	List[str]									= self._to_readme(lowerCamelCase__						)
						with open(lowerCamelCase__				,      "w"				,      encoding="utf-8"						) as readme_file:
							readme_file.write(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__ = None						):
						if readme_content is not None:
							UpperCAmelCase__							,						UpperCAmelCase__:	Any									= _split_yaml_from_readme(lowerCamelCase__						)
							UpperCAmelCase__:	Any									= "---\n" + self.to_yaml_string() + "---\n" + content
						else:
							UpperCAmelCase__:	List[str]									= "---\n" + self.to_yaml_string() + "---\n"
						return full_content





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= yaml.load(lowerCamelCase__				,      Loader=_NoDuplicateSafeLoader						) or {}

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





					def    _UpperCAmelCase    (				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=lowerCamelCase__				,      allow_unicode=lowerCamelCase__				,      encoding="utf-8"				,      ).decode("utf-8"						)
_lowerCAmelCase  :      Optional[Any]			 ={
    """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

					_lowerCAmelCase  :      str			 =ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
					ap.add_argument("""readme_filepath""")
					_lowerCAmelCase  :      str			 =ap.parse_args()

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


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()