import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowercase__      :    Optional[Any]          =     (3_0, 4_8_7)
lowercase__      :    Optional[Any]          =     (3_6, 1_0_2_4)
lowercase__      :    Tuple          =     (1_0_0, 1_0_0)
lowercase__      :    Optional[Any]          =     1_0_0

lowercase__      :    Optional[int]          =     datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

lowercase__      :    List[Any]          =     os.path.split(__file__)
lowercase__      :    Union[str, Any]          =     os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     str:
							with ArrowWriter(features=snake_case__       ,  path=os.path.join(snake_case__       ,  '''beta.arrow'''							)							) as writer:
														for key, record in dummy_data:
																					lowerCAmelCase     		=		my_features.encode_example(snake_case__							)
																					writer.write(snake_case__							)
														lowerCAmelCase		,				lowerCAmelCase     		=		writer.finalize()

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     List[str]:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							for _ in dataset:
														pass

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Tuple:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							dataset.set_format('''numpy'''							)
							for _ in dataset:
														pass

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Union[str, Any]:
							lowerCAmelCase     		=		1_0
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							for i in range(0       ,  len(snake_case__							)       ,  snake_case__							):
														lowerCAmelCase     		=		dataset[i : i + batch_size]

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Union[str, Any]:
							lowerCAmelCase     		=		1_0
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							dataset.set_format('''numpy'''							)
							for i in range(0       ,  len(snake_case__							)       ,  snake_case__							):
														lowerCAmelCase     		=		dataset[i : i + batch_size]

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Tuple:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							for col in feats:
														lowerCAmelCase     		=		dataset[col]

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Optional[int]:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							dataset.set_format('''numpy'''							)
							for col in feats:
														lowerCAmelCase     		=		dataset[col]

def        SCREAMING_SNAKE_CASE_					(						)					->     Optional[Any]:
							lowerCAmelCase     		=		{}
							lowerCAmelCase     		=		(
							    read_unformated,
							    read_formatted_as_numpy,
							    read_batch_unformated,
							    read_batch_formatted_as_numpy,
							    read_col_unformated,
							    read_col_formatted_as_numpy,
							)
							with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCAmelCase     		=		datasets.Features({'''image''': ArrayaD(snake_case__       ,  dtype='''float32'''							)}							)
														lowerCAmelCase     		=		generate_examples(features=snake_case__       ,  num_examples=snake_case__							)
														lowerCAmelCase     		=		write(snake_case__       ,  snake_case__       ,  snake_case__							)
														for read_func in read_functions:
																					lowerCAmelCase     		=		read_func(snake_case__       ,  snake_case__							)

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
														# )
														lowerCAmelCase     		=		datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''							)							)							)}							)
														lowerCAmelCase     		=		generate_examples(
														    features=snake_case__       ,  num_examples=snake_case__       ,  seq_shapes={'''image''': SPEED_TEST_SHAPE}							)
														lowerCAmelCase     		=		write(snake_case__       ,  snake_case__       ,  snake_case__							)
														for read_func in read_functions:
																					lowerCAmelCase     		=		read_func(snake_case__       ,  snake_case__							)

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
														# )
														lowerCAmelCase     		=		datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''							)							)}							)
														lowerCAmelCase     		=		generate_examples(
														    features=snake_case__       ,  num_examples=snake_case__       ,  seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}       ,  )
														lowerCAmelCase     		=		write(snake_case__       ,  snake_case__       ,  snake_case__							)
														for read_func in read_functions:
																					lowerCAmelCase     		=		read_func(snake_case__       ,  snake_case__							)

							with open(snake_case__       ,  '''wb'''							) as f:
														f.write(json.dumps(snake_case__							).encode('''utf-8'''							)							)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_array_xd()




import random




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		x
								lowerCAmelCase     		=		y


	def       SCREAMING_SNAKE_CASE_    (							self    )						->bool:
								return (self.x**2 + self.y**2) <= 1




	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Tuple:
								return cls(x=random.random()		,      y=random.random()    )

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

							lowerCAmelCase     		=		0
							for _ in range(snake_case__							):
														lowerCAmelCase     		=		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

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



