






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

from .haversine_distance import haversine_distance

UpperCamelCase        =				6_378_137.0
UpperCamelCase        =				6_356_752.314_245
UpperCamelCase        =				6_378_137




def 				A		(					lowercase__  :  float ,		lowercase__  :  float ,		lowercase__  :  float ,		lowercase__  :  float  )		-> float:
	UpperCamelCase__ :List[str]						=      (AXIS_A - AXIS_B) / AXIS_A
	# Parametric latitudes
	# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
	UpperCamelCase__ :str						=      atan((1 - flattening) * tan(radians(lowercase__  )  )  )
	UpperCamelCase__ :Dict						=      atan((1 - flattening) * tan(radians(lowercase__  )  )  )

	# Compute central angle between two points
	# using haversine theta. sigma =  haversine_distance / equatorial radius
	UpperCamelCase__ :int						=      haversine_distance(lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__  ) / EQUATORIAL_RADIUS

	# Intermediate P and Q values
	UpperCamelCase__ :str						=      (b_lata + b_lata) / 2
	UpperCamelCase__ :List[str]						=      (b_lata - b_lata) / 2

	# Intermediate X value
	# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
	UpperCamelCase__ :str						=      (sin(lowercase__  ) ** 2) * (cos(lowercase__  ) ** 2)
	UpperCamelCase__ :Union[str, Any]						=      cos(sigma / 2  ) ** 2
	UpperCamelCase__ :Tuple						=      (sigma - sin(lowercase__  )) * (x_numerator / x_demonimator)

	# Intermediate Y value
	# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
	UpperCamelCase__ :Optional[int]						=      (cos(lowercase__  ) ** 2) * (sin(lowercase__  ) ** 2)
	UpperCamelCase__ :Optional[Any]						=      sin(sigma / 2  ) ** 2
	UpperCamelCase__ :List[str]						=      (sigma + sin(lowercase__  )) * (y_numerator / y_denominator)

	return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
							import doctest

							doctest.testmod()

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
 # setup instructions, if using on-demand hardware

 # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
 # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
 # Throw an error if user passes both BYO and on-demand cluster args
 # Otherwise, use default values
 _snake_case      = argparse.ArgumentParser()
 parser.add_argument('''--user''', type=str, default='''ubuntu''')
 parser.add_argument('''--host''', type=str, default='''localhost''')
 parser.add_argument('''--key_path''', type=str, default=None)
 parser.add_argument('''--instance''', type=str, default='''V100:1''')
 parser.add_argument('''--provider''', type=str, default='''cheapest''')
 parser.add_argument('''--use_spot''', type=bool, default=False)
 parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
 _snake_case  ,      _snake_case      = parser.parse_known_args()
 if args.host != "localhost":
  if args.instance != "V100:1" or args.provider != "cheapest":
   raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
  _snake_case      = rh.cluster(
      name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
  )
 else:
  _snake_case      = rh.cluster(
      name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 _snake_case      = args.example.rsplit('''/''', 1)[0]

 # Set up remote environment
 cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
 # Note transformers is copied into the home directory on the remote machine, so we can install from there
 cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
 cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

 # Run example. You can bypass the CLI wrapper and paste your own code here.
 cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

 # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
 # from my_script... import train
 # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
 # launch_train_gpu = rh.function(fn=train,
 #                                system=gpu,
 #                                reqs=reqs,
 #                                name='train_bert_glue')
 #
 # We can pass in arguments just like we would to a function:
 # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
 #                  stream_logs=True)


