






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_			:Union[str, Any]	    =		logging.get_logger(__name__)

A_			:List[Any]	    =		{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}




class 							__A	(     a					):





				"""simple docstring"""







				UpperCamelCase__					: Union[str, Any]     					="""ernie"""


				def __init__(					self				,    lowerCamelCase__=30522				,    lowerCamelCase__=768				,    lowerCamelCase__=12				,    lowerCamelCase__=12				,    lowerCamelCase__=3072				,    lowerCamelCase__="gelu"				,    lowerCamelCase__=0.1				,    lowerCamelCase__=0.1				,    lowerCamelCase__=512				,    lowerCamelCase__=2				,    lowerCamelCase__=3				,    lowerCamelCase__=False				,    lowerCamelCase__=0.02				,    lowerCamelCase__=1E-12				,    lowerCamelCase__=0				,    lowerCamelCase__="absolute"				,    lowerCamelCase__=True				,    lowerCamelCase__=None				,    **lowerCamelCase__				,    ):




									"""simple docstring"""

									super().__init__(pad_token_id=lowerCamelCase__				,    **lowerCamelCase__       )

									__UpperCamelCase :   Tuple									=vocab_size
									__UpperCamelCase :   Tuple									=hidden_size
									__UpperCamelCase :   Union[str, Any]									=num_hidden_layers
									__UpperCamelCase :   int									=num_attention_heads
									__UpperCamelCase :   Any									=hidden_act
									__UpperCamelCase :   str									=intermediate_size
									__UpperCamelCase :   List[str]									=hidden_dropout_prob
									__UpperCamelCase :   str									=attention_probs_dropout_prob
									__UpperCamelCase :   List[Any]									=max_position_embeddings
									__UpperCamelCase :   int									=type_vocab_size
									__UpperCamelCase :   Union[str, Any]									=task_type_vocab_size
									__UpperCamelCase :   Optional[Any]									=use_task_id
									__UpperCamelCase :   List[Any]									=initializer_range
									__UpperCamelCase :   Tuple									=layer_norm_eps
									__UpperCamelCase :   Optional[int]									=position_embedding_type
									__UpperCamelCase :   str									=use_cache
									__UpperCamelCase :   str									=classifier_dropout




class 							__A	(     a					):





				"""simple docstring"""







				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									if self.task == "multiple-choice":
														__UpperCamelCase :   Dict									={0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
														__UpperCamelCase :   Optional[Any]									={0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									        ('token_type_ids', dynamic_axis),
									        ('task_type_ids', dynamic_axis),
									    ]       )









import random




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=x
									__UpperCamelCase :   int									=y


				def  __lowercase				(					self       ):




									"""simple docstring"""

									return (self.x**2 + self.y**2) <= 1







				@classmethod
				def  __lowercase				(					cls       ):




									"""simple docstring"""

									return cls(x=random.random()				,    y=random.random()       )






def 				A	(			a_   )	->		float:
					if number_of_simulations < 1:
										raise ValueError('At least one simulation is necessary to estimate PI.'   )

					__UpperCamelCase :   List[str]									=0
					for _ in range(a_   ):
										__UpperCamelCase :   Optional[int]									=Point.random_unit_square()

										if random_point.is_in_unit_circle():
															number_in_unit_circle += 1

					return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							A_			:Optional[int]	    =		'''Please enter the desired number of Monte Carlo simulations: '''
							A_			:int	    =		estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")

