


'''simple docstring'''






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


def    _snake_case  (	A			)		->       Any:

							# A local function to see if a dot lands in the circle.
							def is_in_circle(A					,       A			) -> bool:
														lowerCAmelCase__			  =      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
							lowerCAmelCase__			  =      mean(
							    int(is_in_circle(uniform(-1.0					,       1.0			)					,       uniform(-1.0					,       1.0			)			)			)
							    for _ in range(A			)			)
							# The ratio of the area for circle to square is pi/4.
							lowerCAmelCase__			  =      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    _snake_case  (	A					,       A					,       A = 0.0					,       A = 1.0					,       )		->       float:
							return mean(
							    function_to_integrate(uniform(A					,       A			)			) for _ in range(A			)			) * (max_value - min_value)


def    _snake_case  (	A					,       A = 0.0					,       A = 1.0			)		->       None:

							def identity_function(A			) -> float:
														return x

							lowerCAmelCase__			  =      area_under_curve_estimator(
							    A					,       A					,       A					,       A			)
							lowerCAmelCase__			  =      (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    _snake_case  (	A			)		->       None:

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

							lowerCAmelCase__			  =      area_under_curve_estimator(
							    A					,       A					,       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()



'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d