
'''simple docstring'''



import random

class     A_ :

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_   ):
								_UpperCamelCase											=   x
								_UpperCamelCase											=   y




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




			@classmethod
			def       a	(					cls   ):
								return cls(x=random.random()			,     y=random.random()   )




def  lowercase__(		_UpperCamelCase  : Any				)->   str:






					"""simple docstring"""

					if number_of_simulations < 1:
										raise ValueError("At least one simulation is necessary to estimate PI."				)

					_UpperCamelCase											=   0
					for _ in range(_UpperCamelCase				):
										_UpperCamelCase											=   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

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










'''simple docstring'''



import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class     A_ (  tf.keras.layers.Layer  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = None			,     A_ = None			,     A_ = None			,     A_ = "longest"			,     A_ = True			,     A_ = 5_12			,     A_ = None			,     A_ = True			,     A_ = True			,     A_ = True			,     **A_			,     ):
								super().__init__()
								if use_fast_bert_tokenizer:
													_UpperCamelCase											=   FastBertTokenizer(
													    A_			,     token_out_type=tf.intaa			,     lower_case_nfd_strip_accents=A_			,     **A_   )
								else:
													_UpperCamelCase											=   tf.lookup.StaticVocabularyTable(
													    tf.lookup.KeyValueTensorInitializer(
													        keys=A_			,     key_dtype=tf.string			,     values=tf.range(tf.size(A_			,     out_type=tf.intaa   )			,     dtype=tf.intaa   )			,     value_dtype=tf.intaa			,     )			,     num_oov_buckets=1			,     )
													_UpperCamelCase											=   BertTokenizerLayer(
													    A_			,     token_out_type=tf.intaa			,     lower_case=A_			,     **A_   )

								_UpperCamelCase											=   vocab_list
								_UpperCamelCase											=   do_lower_case
								_UpperCamelCase											=   cls_token_id or vocab_list.index("[CLS]"   )
								_UpperCamelCase											=   sep_token_id or vocab_list.index("[SEP]"   )
								_UpperCamelCase											=   pad_token_id or vocab_list.index("[PAD]"   )
								_UpperCamelCase											=   ShrinkLongestTrimmer(max_length - 3			,     axis=1   )  # Allow room for special tokens
								_UpperCamelCase											=   max_length
								_UpperCamelCase											=   padding
								_UpperCamelCase											=   truncation
								_UpperCamelCase											=   pad_to_multiple_of
								_UpperCamelCase											=   return_token_type_ids
								_UpperCamelCase											=   return_attention_mask




			@classmethod
			def       a	(					cls			,     A_			,     **A_   ):  # noqa: F821
								_UpperCamelCase											=   kwargs.pop("do_lower_case"			,     A_   )
								_UpperCamelCase											=   tokenizer.do_lower_case if do_lower_case is None else do_lower_case
								_UpperCamelCase											=   kwargs.pop("cls_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.cls_token_id if cls_token_id is None else cls_token_id
								_UpperCamelCase											=   kwargs.pop("sep_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.sep_token_id if sep_token_id is None else sep_token_id
								_UpperCamelCase											=   kwargs.pop("pad_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.pad_token_id if pad_token_id is None else pad_token_id

								_UpperCamelCase											=   tokenizer.get_vocab()
								_UpperCamelCase											=   sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,     key=lambda A_   : x[1]   )
								_UpperCamelCase											=   [entry[0] for entry in vocab]
								return cls(
								    vocab_list=A_			,     do_lower_case=A_			,     cls_token_id=A_			,     sep_token_id=A_			,     pad_token_id=A_			,     **A_			,     )




			@classmethod
			def       a	(					cls			,     A_			,     *A_			,     **A_   ):
								try:
													_UpperCamelCase											=   BertTokenizer.from_pretrained(A_			,     *A_			,     **A_   )
								except:  # noqa: E722
													from .tokenization_bert_fast import BertTokenizerFast

													_UpperCamelCase											=   BertTokenizerFast.from_pretrained(A_			,     *A_			,     **A_   )
								return cls.from_tokenizer(A_			,     **A_   )




			def       a	(					self			,     A_   ):
								if self.do_lower_case:
													_UpperCamelCase											=   case_fold_utfa(A_   )
								_UpperCamelCase											=   self.tf_tokenizer.tokenize(A_   )
								return tokens.merge_dims(1			,     -1   )




			def       a	(					self			,     A_			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     ):
								if padding is None:
													_UpperCamelCase											=   self.padding
								if padding not in ("longest", "max_length"):
													raise ValueError("Padding must be either 'longest' or 'max_length'!"   )
								if max_length is not None and text_pair is not None:
													# Because we have to instantiate a Trimmer to do it properly
													raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"   )
								if max_length is None:
													_UpperCamelCase											=   self.max_length
								if truncation is None:
													_UpperCamelCase											=   self.truncation
								if pad_to_multiple_of is None:
													_UpperCamelCase											=   self.pad_to_multiple_of
								if return_token_type_ids is None:
													_UpperCamelCase											=   self.return_token_type_ids
								if return_attention_mask is None:
													_UpperCamelCase											=   self.return_attention_mask
								if not isinstance(A_			,     tf.Tensor   ):
													_UpperCamelCase											=   tf.convert_to_tensor(A_   )
								if text_pair is not None and not isinstance(A_			,     tf.Tensor   ):
													_UpperCamelCase											=   tf.convert_to_tensor(A_   )
								if text_pair is not None:
													if text.shape.rank > 1:
																		raise ValueError("text argument should not be multidimensional when a text pair is supplied!"   )
													if text_pair.shape.rank > 1:
																		raise ValueError("text_pair should not be multidimensional!"   )
								if text.shape.rank == 2:
													_UpperCamelCase				,	_UpperCamelCase											=   text[:, 0], text[:, 1]
								_UpperCamelCase											=   self.unpaired_tokenize(A_   )
								if text_pair is None:  # Unpaired text
													if truncation:
																		_UpperCamelCase											=   text[:, : max_length - 2]  # Allow room for special tokens
													_UpperCamelCase				,	_UpperCamelCase											=   combine_segments(
													    (text,)			,     start_of_sequence_id=self.cls_token_id			,     end_of_segment_id=self.sep_token_id   )
								else:  # Paired text
													_UpperCamelCase											=   self.unpaired_tokenize(A_   )
													if truncation:
																		_UpperCamelCase				,	_UpperCamelCase											=   self.paired_trimmer.trim([text, text_pair]   )
													_UpperCamelCase				,	_UpperCamelCase											=   combine_segments(
													    (text, text_pair)			,     start_of_sequence_id=self.cls_token_id			,     end_of_segment_id=self.sep_token_id   )
								if padding == "longest":
													_UpperCamelCase											=   input_ids.bounding_shape(axis=1   )
													if pad_to_multiple_of is not None:
																		# No ceiling division in tensorflow, so we negate floordiv instead
																		_UpperCamelCase											=   pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,     A_   ))
								else:
													_UpperCamelCase											=   max_length

								_UpperCamelCase				,	_UpperCamelCase											=   pad_model_inputs(A_			,     max_seq_length=A_			,     pad_value=self.pad_token_id   )
								_UpperCamelCase											=   {"input_ids": input_ids}
								if return_attention_mask:
													_UpperCamelCase											=   attention_mask
								if return_token_type_ids:
													_UpperCamelCase				,	_UpperCamelCase											=   pad_model_inputs(
													    A_			,     max_seq_length=A_			,     pad_value=self.pad_token_id   )
													_UpperCamelCase											=   token_type_ids
								return output




			def       a	(					self   ):
								return {
								    "vocab_list": self.vocab_list,
								    "do_lower_case": self.do_lower_case,
								    "cls_token_id": self.cls_token_id,
								    "sep_token_id": self.sep_token_id,
								    "pad_token_id": self.pad_token_id,
								}



