import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=()		, _SCREAMING_SNAKE_CASE     :				int=None		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]="no"		, _SCREAMING_SNAKE_CASE     :				int="29500"			):
						UpperCamelCase_		:							List[str]  =		False
						UpperCamelCase_		:							str  =		False
						if any(key.startswith("""KAGGLE"""			) for key in os.environ.keys()			):
												UpperCamelCase_		:							List[Any]  =		True
						elif "IPython" in sys.modules:
												UpperCamelCase_		:							List[str]  =		'google.colab' in str(sys.modules["""IPython"""].get_ipython()			)

						try:
												UpperCamelCase_		:							List[Any]  =		PrecisionType(mixed_precision.lower()			)
						except ValueError:
												raise ValueError(
												    f'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''			)

						if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""		, SCREAMING_SNAKE_CASE__			) is not None):
												# TPU launch
												import torch_xla.distributed.xla_multiprocessing as xmp

												if len(AcceleratorState._shared_state			) > 0:
																		raise ValueError(
																		    """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
																		    """your training function. Restart your notebook and make sure no cells initializes an """
																		    """`Accelerator`."""			)
												if num_processes is None:
																		UpperCamelCase_		:							Any  =		8

												UpperCamelCase_		:							List[str]  =		PrepareForLaunch(SCREAMING_SNAKE_CASE__		, distributed_type="""TPU"""			)
												print(f'''Launching a training on {num_processes} TPU cores.'''			)
												xmp.spawn(SCREAMING_SNAKE_CASE__		, args=SCREAMING_SNAKE_CASE__		, nprocs=SCREAMING_SNAKE_CASE__		, start_method="""fork"""			)
						elif in_colab:
												# No need for a distributed launch otherwise as it's either CPU or one GPU.
												if torch.cuda.is_available():
																		print("""Launching training on one GPU."""			)
												else:
																		print("""Launching training on one CPU."""			)
												function(*SCREAMING_SNAKE_CASE__			)
						else:
												if num_processes is None:
																		raise ValueError(
																		    """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""			)

												if num_processes > 1:
																		# Multi-GPU launch
																		from torch.multiprocessing import start_processes
																		from torch.multiprocessing.spawn import ProcessRaisedException

																		if len(AcceleratorState._shared_state			) > 0:
																								raise ValueError(
																								    """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
																								    """inside your training function. Restart your notebook and make sure no cells initializes an """
																								    """`Accelerator`."""			)

																		if torch.cuda.is_initialized():
																								raise ValueError(
																								    """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
																								    """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
																								    """function."""			)

																		# torch.distributed will expect a few environment variable to be here. We set the ones common to each
																		# process here (the other ones will be set be the launcher).
																		with patch_environment(
																		    world_size=SCREAMING_SNAKE_CASE__		, master_addr="""127.0.01"""		, master_port=SCREAMING_SNAKE_CASE__		, mixed_precision=SCREAMING_SNAKE_CASE__			):
																								UpperCamelCase_		:							Optional[int]  =		PrepareForLaunch(SCREAMING_SNAKE_CASE__		, distributed_type="""MULTI_GPU"""			)
																								print(f'''Launching training on {num_processes} GPUs.'''			)
																								try:
																														start_processes(SCREAMING_SNAKE_CASE__		, args=SCREAMING_SNAKE_CASE__		, nprocs=SCREAMING_SNAKE_CASE__		, start_method="""fork"""			)
																								except ProcessRaisedException as e:
																														if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																																				raise RuntimeError(
																																				    """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
																																				    """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
																																				    """Please review your imports and test them when running the `notebook_launcher()` to identify """
																																				    """which one is problematic."""			) from e

												else:
																		# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
																		if is_mps_available():
																								UpperCamelCase_		:							Dict  =		'1'
																								print("""Launching training on MPS."""			)
																		elif torch.cuda.is_available():
																								print("""Launching training on one GPU."""			)
																		else:
																								print("""Launching training on CPU."""			)
																		function(*SCREAMING_SNAKE_CASE__			)







def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				List[Any]=()		, _SCREAMING_SNAKE_CASE     :				Tuple=2			):
						from torch.multiprocessing import start_processes

						with tempfile.NamedTemporaryFile() as tmp_file:
												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=SCREAMING_SNAKE_CASE__		, master_addr="""127.0.01"""		, master_port="""29500"""		, accelerate_mixed_precision="""no"""		, accelerate_debug_rdv_file=tmp_file.name		, accelerate_use_cpu="""yes"""		, ):
																		UpperCamelCase_		:							List[Any]  =		PrepareForLaunch(SCREAMING_SNAKE_CASE__		, debug=SCREAMING_SNAKE_CASE__			)
																		start_processes(SCREAMING_SNAKE_CASE__		, args=SCREAMING_SNAKE_CASE__		, nprocs=SCREAMING_SNAKE_CASE__		, start_method="""fork"""			)










from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase		   =			numpy.array([0, 0])
lowerCamelCase		   =			numpy.array([0.5, 0.866_0254])
lowerCamelCase		   =			numpy.array([1, 0])
lowerCamelCase		   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =initial_vectors
       for _ in range(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						List[str]            =iteration_step(SCREAMING_SNAKE_CASE__	)
       return vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =[]
       for i, start_vector in enumerate(vectors[:-1]	):
              _lowerCamelCase			:						Tuple            =vectors[i + 1]
              new_vectors.append(SCREAMING_SNAKE_CASE__	)
              _lowerCamelCase			:						Tuple            =end_vector - start_vector
              new_vectors.append(start_vector + difference_vector / 3	)
              new_vectors.append(
                  start_vector + difference_vector / 3 + rotate(difference_vector / 3						,     60	)	)
              new_vectors.append(start_vector + difference_vector * 2 / 3	)
       new_vectors.append(vectors[-1]	)
       return new_vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : numpy.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =numpy.radians(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =numpy.cos(SCREAMING_SNAKE_CASE__	), numpy.sin(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =numpy.array(((c, -s), (s, c))	)
       return numpy.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[Any]            =plt.gca()
       axes.set_aspect('equal'	)

       # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
       # y-coordinates as inputs, which are constructed from the vector-list using
       # zip()
       _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =zip(*SCREAMING_SNAKE_CASE__	)
       plt.plot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase		   =			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)


