

from math import asin, atan, cos, radians, sin, sqrt, tan

A_						 =    637_8137.0
A_						 =    635_6752.31_4245
A_						 =    637_8137

def 		UpperCamelCase      (lowercase_:				float					,   lowercase_:				float					,   lowercase_:				float					,   lowercase_:				float      )  ->   Any:
	A__							:					str       =	(AXIS_A - AXIS_B) / AXIS_A
	A__							:					int       =	atan((1 - flattening) * tan(radians(_UpperCamelCase      )      )      )
	A__							:					Any       =	atan((1 - flattening) * tan(radians(_UpperCamelCase      )      )      )
	A__							:					List[str]       =	radians(_UpperCamelCase      )
	A__							:					Union[str, Any]       =	radians(_UpperCamelCase      )
	# Equation
	A__							:					Optional[Any]       =	sin((phi_a - phi_a) / 2      )
	A__							:					str       =	sin((lambda_a - lambda_a) / 2      )
	# Square both values
	sin_sq_phi *= sin_sq_phi
	sin_sq_lambda *= sin_sq_lambda
	A__							:					List[str]       =	sqrt(sin_sq_phi + (cos(_UpperCamelCase      ) * cos(_UpperCamelCase      ) * sin_sq_lambda)      )
	return 2 * RADIUS * asin(_UpperCamelCase      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A_ :				Tuple						 =    datasets.utils.logging.get_logger(__name__)



@dataclass
class 					_a		(datasets.BuilderConfig       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[datasets.Features]													= None
					UpperCAmelCase__:     str													= "utf-8"
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     bool													= True  # deprecated
					UpperCAmelCase__:     Optional[int]													= None  # deprecated
					UpperCAmelCase__:     int													= 10 << 20  # 10MB
					UpperCAmelCase__:     Optional[bool]													= None



class 					_a		(datasets.ArrowBasedBuilder       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= JsonConfig






					def 						__A    (				self    ):
						if self.config.block_size is not None:
							logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""    )
							A__							:					Union[str, Any]       =	self.config.block_size
						if self.config.use_threads is not True:
							logger.warning(
							    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""    )
						if self.config.newlines_in_values is not None:
							raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""    )
						return datasets.DatasetInfo(features=self.config.features    )






					def 						__A    (				self					, A__    ):
						if not self.config.data_files:
							raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""    )
						A__							:					int       =	dl_manager.download_and_extract(self.config.data_files    )
						if isinstance(A__					, (str, list, tuple)    ):
							A__							:					Optional[Any]       =	data_files
							if isinstance(A__					, A__    ):
								A__							:					List[str]       =	[files]
							A__							:					int       =	[dl_manager.iter_files(A__    ) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={"""files""": files}    )]
						A__							:					List[str]       =	[]
						for split_name, files in data_files.items():
							if isinstance(A__					, A__    ):
								A__							:					Optional[int]       =	[files]
							A__							:					Optional[int]       =	[dl_manager.iter_files(A__    ) for file in files]
							splits.append(datasets.SplitGenerator(name=A__					, gen_kwargs={"""files""": files}    )    )
						return splits






					def 						__A    (				self					, A__    ):
						if self.config.features is not None:
							# adding missing columns
							for column_name in set(self.config.features    ) - set(pa_table.column_names    ):
								A__							:					Optional[Any]       =	self.config.features.arrow_schema.field(A__    ).type
								A__							:					str       =	pa_table.append_column(A__					, pa.array([None] * len(A__    )					, type=A__    )    )
							# more expensive cast to support nested structures with keys in a different order
							# allows str <-> int/float or str to Audio for example
							A__							:					Optional[int]       =	table_cast(A__					, self.config.features.arrow_schema    )
						return pa_table






					def 						__A    (				self					, A__    ):
						for file_idx, file in enumerate(itertools.chain.from_iterable(A__    )    ):
							# If the file is one json object and if we need to look at the list of items in one specific field
							if self.config.field is not None:
								with open(A__					, encoding=self.config.encoding					, errors=self.config.encoding_errors    ) as f:
									A__							:					Optional[Any]       =	json.load(A__    )

								# We keep only the field we are interested in
								A__							:					Optional[int]       =	dataset[self.config.field]

								# We accept two format: a list of dicts or a dict of lists
								if isinstance(A__					, (list, tuple)    ):
									A__							:					Union[str, Any]       =	set().union(*[row.keys() for row in dataset]    )
									A__							:					Any       =	{col: [row.get(A__    ) for row in dataset] for col in keys}
								else:
									A__							:					Any       =	dataset
								A__							:					Any       =	pa.Table.from_pydict(A__    )
								yield file_idx, self._cast_table(A__    )

							# If the file has one json object per line
							else:
								with open(A__					, """rb"""    ) as f:
									A__							:					List[str]       =	0
									# Use block_size equal to the chunk size divided by 32 to leverage multithreading
									# Set a default minimum value of 16kB if the chunk size is really small
									A__							:					List[str]       =	max(self.config.chunksize // 32					, 16 << 10    )
									A__							:					Any       =	(
									    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
									)
									while True:
										A__							:					Dict       =	f.read(self.config.chunksize    )
										if not batch:
											break
										# Finish current line
										try:
											batch += f.readline()
										except (AttributeError, io.UnsupportedOperation):
											batch += readline(A__    )
										# PyArrow only accepts utf-8 encoded bytes
										if self.config.encoding != "utf-8":
											A__							:					List[Any]       =	batch.decode(self.config.encoding					, errors=A__    ).encode("""utf-8"""    )
										try:
											while True:
												try:
													A__							:					str       =	paj.read_json(
													    io.BytesIO(A__    )					, read_options=paj.ReadOptions(block_size=A__    )    )
													break
												except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
													if (
													    isinstance(A__					, pa.ArrowInvalid    )
													    and "straddling" not in str(A__    )
													    or block_size > len(A__    )
													):
														raise
													else:
														# Increase the block size in case it was too small.
														# The block size will be reset for the next file.
														logger.debug(
														    F"""Batch of {len(A__    )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""    )
														block_size *= 2
										except pa.ArrowInvalid as e:
											try:
												with open(
												    A__					, encoding=self.config.encoding					, errors=self.config.encoding_errors    ) as f:
													A__							:					Optional[Any]       =	json.load(A__    )
											except json.JSONDecodeError:
												logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
												raise e
											# If possible, parse the file as a list of json objects and exit the loop
											if isinstance(A__					, A__    ):  # list is the only sequence type supported in JSON
												try:
													A__							:					str       =	set().union(*[row.keys() for row in dataset]    )
													A__							:					List[str]       =	{col: [row.get(A__    ) for row in dataset] for col in keys}
													A__							:					int       =	pa.Table.from_pydict(A__    )
												except (pa.ArrowInvalid, AttributeError) as e:
													logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
													raise ValueError(F"""Not able to read records in the JSON file at {file}."""    ) from None
												yield file_idx, self._cast_table(A__    )
												break
											else:
												logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
												raise ValueError(
												    F"""Not able to read records in the JSON file at {file}. """
												    F"""You should probably indicate the field of the JSON file containing your records. """
												    F"""This JSON file contain the following fields: {str(list(dataset.keys()    )    )}. """
												    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """    ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
										yield (file_idx, batch_idx), self._cast_table(A__    )
										batch_idx += 1



