

from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def 	snake_case__       (					__lowercase       )		->		Dict:







						"""simple docstring"""

						def is_in_circle(__lowercase							,     __lowercase       ) -> bool:
												A__	:							str						    = 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
						A__	:							int						    = mean(
						    int(is_in_circle(uniform(-1.0							,     1.0       )							,     uniform(-1.0							,     1.0       )       )       )
						    for _ in range(_UpperCamelCase       )       )
						# The ratio of the area for circle to square is pi/4.
						A__	:							Tuple						    = 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__       (					__lowercase							,     __lowercase							,     __lowercase = 0.0							,     __lowercase = 1.0							,     )		->		float:







						"""simple docstring"""

						return mean(
						    function_to_integrate(uniform(_UpperCamelCase							,     _UpperCamelCase       )       ) for _ in range(_UpperCamelCase       )       ) * (max_value - min_value)
def 	snake_case__       (					__lowercase							,     __lowercase = 0.0							,     __lowercase = 1.0       )		->		None:







						"""simple docstring"""

						def identity_function(__lowercase       ) -> float:
												return x

						A__	:							Optional[int]						    = area_under_curve_estimator(
						    _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase       )
						A__	:							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 	snake_case__       (					__lowercase       )		->		None:







						"""simple docstring"""

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

						A__	:							Tuple						    = area_under_curve_estimator(
						    _UpperCamelCase							,     _UpperCamelCase							,     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()




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Dict			,		_A      :     NestedDataStructureLike[PathLike]			,		_A      :     Optional[NamedSplit] = None			,		_A      :     Optional[Features] = None			,		_A      :     str = None			,		_A      :     bool = False			,		_A      :     bool = False			,		_A      :     Optional[str] = None			,		_A      :     Optional[int] = None			,		**_A      :     Dict			,		):
											super().__init__(
											    _A			,		split=_A			,		features=_A			,		cache_dir=_A			,		keep_in_memory=_A			,		streaming=_A			,		num_proc=_A			,		**_A			,		)
											A__	:							Optional[int]						    = field
											A__	:							Dict						    = path_or_paths if isinstance(_A			,		_A) else {self.split: path_or_paths}
											A__	:							int						    = Json(
											    cache_dir=_A			,		data_files=_A			,		features=_A			,		field=_A			,		**_A			,		)







					def  _lowercase			(       self      :     Dict):
											# Build iterable dataset
											if self.streaming:
																	A__	:							Union[str, Any]						    = self.builder.as_streaming_dataset(split=self.split)
											# Build regular (map-style) dataset
											else:
																	A__	:							Optional[Any]						    = None
																	A__	:							Any						    = None
																	A__	:							Tuple						    = None
																	A__	:							Tuple						    = None

																	self.builder.download_and_prepare(
																	    download_config=_A			,		download_mode=_A			,		verification_mode=_A			,		base_path=_A			,		num_proc=self.num_proc			,		)
																	A__	:							Any						    = self.builder.as_dataset(
																	    split=self.split			,		verification_mode=_A			,		in_memory=self.keep_in_memory)
											return dataset




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]			,		_A      :     Dataset			,		_A      :     Union[PathLike, BinaryIO]			,		_A      :     Optional[int] = None			,		_A      :     Optional[int] = None			,		**_A      :     Any			,		):
											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

											A__	:							Tuple						    = dataset
											A__	:							Tuple						    = path_or_buf
											A__	:							int						    = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											A__	:							Tuple						    = num_proc
											A__	:							Any						    = "utf-8"
											A__	:							Optional[int]						    = to_json_kwargs







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Any						    = self.to_json_kwargs.pop("path_or_buf"			,		_A)
											A__	:							Union[str, Any]						    = self.to_json_kwargs.pop("orient"			,		"records")
											A__	:							List[Any]						    = self.to_json_kwargs.pop("lines"			,		True if orient == "records" else False)
											A__	:							List[Any]						    = self.to_json_kwargs.pop("index"			,		False if orient in ["split", "table"] else True)
											A__	:							int						    = self.to_json_kwargs.pop("compression"			,		_A)

											if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																	raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

											if isinstance(self.path_or_buf			,		(str, bytes, os.PathLike)):
																	with fsspec.open(self.path_or_buf			,		"wb"			,		compression=_A) as buffer:
																							A__	:							int						    = self._write(file_obj=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											else:
																	if compression:
																							raise NotImplementedError(
																							    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																							    " was passed. Please provide a local path instead.")
																	A__	:							Optional[Any]						    = self._write(
																	    file_obj=self.path_or_buf			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											return written







					def  _lowercase			(       self      :     List[Any]			,		_A      :     int):
											A__    ,       A__    ,       A__    ,       A__    ,       A__	:							Union[str, Any]						    = args

											A__	:							Dict						    = query_table(
											    table=self.dataset.data			,		key=slice(_A			,		offset + self.batch_size)			,		indices=self.dataset._indices			,		)
											A__	:							Dict						    = batch.to_pandas().to_json(
											    path_or_buf=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**_A)
											if not json_str.endswith("\n"):
																	json_str += "\n"
											return json_str.encode(self.encoding)







					def  _lowercase			(       self      :     List[str]			,		_A      :     BinaryIO			,		_A      :     str			,		_A      :     List[Any]			,		_A      :     Optional[Any]			,		**_A      :     Optional[Any]			,		):
											A__	:							Any						    = 0

											if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0			,		len(self.dataset)			,		self.batch_size)			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																							A__	:							List[str]						    = self._batch_json((offset, orient, lines, index, to_json_kwargs))
																							written += file_obj.write(_A)
											else:
																	A__    ,       A__	:							Union[str, Any]						    = len(self.dataset), self.batch_size
																	with multiprocessing.Pool(self.num_proc) as pool:
																							for json_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_json			,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,		_A			,		_A)]			,		)			,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																													written += file_obj.write(_A)

											return written