
"""simple docstring"""




import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

UpperCAmelCase__				:   Dict   							=			0.25 + 0.0j
UpperCAmelCase__				:   List[Any]   							=			-0.4 + 0.6j
UpperCAmelCase__				:   Any   							=			-0.1 + 0.6_5_1j
UpperCAmelCase__				:   Tuple   							=			-2.0
UpperCAmelCase__				:   Any   							=			5_6
UpperCAmelCase__				:   Optional[int]   							=			2.0
UpperCAmelCase__				:   Union[str, Any]   							=			6_6_6

def lowercase_  (       _snake_case       ,_snake_case						):
							return numpy.exp(_snake_case						) + c_parameter

def lowercase_  (       _snake_case       ,_snake_case						):
							return z_values * z_values + c_parameter

def lowercase_  (       _snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       numpy.linspace(-window_size       ,_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       x.reshape((nb_pixels, 1)						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       numpy.linspace(-window_size       ,_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Tuple									=       y.reshape((1, nb_pixels)						)
							return x + 1.0j * y

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case = None       ,):
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       z_a.astype("""complex64"""						)
							for _ in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	List[str]									=       eval_function(_snake_case       ,_snake_case						)
														if infinity is not None:
																					numpy.nan_to_num(_snake_case       ,copy=_snake_case       ,nan=_snake_case						)
																					SCREAMING_SNAKE_CASE__						:	Any									=       infinity
							return z_n

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,):
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       (abs(_snake_case						)).transpose()
							SCREAMING_SNAKE_CASE__						:	str									=       abs_z_final[::-1, :]
							pyplot.matshow(abs_z_final < escape_radius						)
							pyplot.title(f'''Julia set of ${function_label}$, $c={function_params}$'''						)
							pyplot.show()

def lowercase_  (       ):
							warnings.filterwarnings(
							    """ignore"""       ,category=_snake_case       ,message="""overflow encountered in multiply"""						)
							warnings.filterwarnings(
							    """ignore"""       ,category=_snake_case       ,message="""invalid value encountered in multiply"""       ,)
							warnings.filterwarnings(
							    """ignore"""       ,category=_snake_case       ,message="""overflow encountered in absolute"""						)
							warnings.filterwarnings(
							    """ignore"""       ,category=_snake_case       ,message="""overflow encountered in exp"""						)


if __name__ == "__main__":
					UpperCAmelCase__				:   Tuple   							=			prepare_grid(window_size, nb_pixels)

					ignore_overflow_warnings()  # See file header for explanations

					UpperCAmelCase__				:   Optional[int]   							=			2_4
					UpperCAmelCase__				:   Optional[int]   							=			2 * abs(c_cauliflower) + 1
					UpperCAmelCase__				:   int   							=			iterate_function(
					    eval_quadratic_polynomial,
					    c_cauliflower,
					    nb_iterations,
					    z_a,
					    infinity=1.1 * escape_radius,
					)
					show_results('z^2+c', c_cauliflower, escape_radius, z_final)

					UpperCAmelCase__				:   Optional[Any]   							=			6_4
					UpperCAmelCase__				:   List[str]   							=			2 * abs(c_polynomial_a) + 1
					UpperCAmelCase__				:   Dict   							=			iterate_function(
					    eval_quadratic_polynomial,
					    c_polynomial_a,
					    nb_iterations,
					    z_a,
					    infinity=1.1 * escape_radius,
					)
					show_results('z^2+c', c_polynomial_a, escape_radius, z_final)

					UpperCAmelCase__				:   int   							=			1_6_1
					UpperCAmelCase__				:   List[str]   							=			2 * abs(c_polynomial_a) + 1
					UpperCAmelCase__				:   Optional[int]   							=			iterate_function(
					    eval_quadratic_polynomial,
					    c_polynomial_a,
					    nb_iterations,
					    z_a,
					    infinity=1.1 * escape_radius,
					)
					show_results('z^2+c', c_polynomial_a, escape_radius, z_final)

					UpperCAmelCase__				:   Any   							=			1_2
					UpperCAmelCase__				:   int   							=			10000.0
					UpperCAmelCase__				:   Dict   							=			iterate_function(
					    eval_exponential,
					    c_exponential,
					    nb_iterations,
					    z_a + 2,
					    infinity=1.0E10,
					)
					show_results('e^z+c', c_exponential, escape_radius, z_final)






"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def lowercase_  (       *_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.cuda.device_count()
							print(f'''Device count: {num_processes}'''						)
							with patch_environment(
							    world_size=_snake_case       ,master_addr="""127.0.01"""       ,master_port="""29500"""       ,mixed_precision=args[1].mixed_precision						):
														SCREAMING_SNAKE_CASE__						:	int									=       PrepareForLaunch(_snake_case       ,distributed_type="""MULTI_GPU"""						)
														torch.multiprocessing.start_processes(_snake_case       ,args=_snake_case       ,nprocs=_snake_case       ,start_method="""spawn"""						)


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					UpperCAmelCase__				:   str   							=			rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					UpperCAmelCase__				:   int   							=			[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					UpperCAmelCase__				:   List[str]   							=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					UpperCAmelCase__				:   Dict   							=			argparse.Namespace(cpu=False, mixed_precision='fp16')
					UpperCAmelCase__				:   Any   							=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])




