


'''simple docstring'''



import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)


class 		_snake_case					(     nn.Module	):




					def __init__(      self):
								super().__init__()
								UpperCAmelCase__  :					Optional[Any]        =     nn.Linear(3							,							4)
								UpperCAmelCase__  :					Tuple        =     nn.BatchNormad(4)
								UpperCAmelCase__  :					List[str]        =     nn.Linear(4							,							5)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return self.lineara(self.batchnorm(self.lineara(_lowerCamelCase)))


class 		_snake_case					(     unittest.TestCase	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     ModelForTest()
								with TemporaryDirectory() as tmp_dir:
											offload_state_dict(_lowerCamelCase							,							model.state_dict())
											UpperCAmelCase__  :					Union[str, Any]        =     os.path.join(_lowerCamelCase							,							"""index.json""")
											self.assertTrue(os.path.isfile(_lowerCamelCase))
											# TODO: add tests on what is inside the index

											for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
														UpperCAmelCase__  :					Optional[int]        =     os.path.join(_lowerCamelCase							,							f'''{key}.dat''')
														self.assertTrue(os.path.isfile(_lowerCamelCase))
														# TODO: add tests on the fact weights are properly loaded




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     [torch.floataa, torch.floataa, torch.bfloataa]

								for dtype in dtypes:
											UpperCAmelCase__  :					int        =     torch.randn(2							,							3							,							dtype=_lowerCamelCase)
											with TemporaryDirectory() as tmp_dir:
														UpperCAmelCase__  :					List[str]        =     offload_weight(_lowerCamelCase							,							"""weight"""							,							_lowerCamelCase							,							{})
														UpperCAmelCase__  :					Dict        =     os.path.join(_lowerCamelCase							,							"""weight.dat""")
														self.assertTrue(os.path.isfile(_lowerCamelCase))
														self.assertDictEqual(_lowerCamelCase							,							{"""weight""": {"""shape""": [2, 3], """dtype""": str(_lowerCamelCase).split(""".""")[1]}})

														UpperCAmelCase__  :					Optional[Any]        =     load_offloaded_weight(_lowerCamelCase							,							index["""weight"""])
														self.assertTrue(torch.equal(_lowerCamelCase							,							_lowerCamelCase))




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     ModelForTest()
								UpperCAmelCase__  :					Optional[int]        =     model.state_dict()
								UpperCAmelCase__  :					Any        =     {k: v for k, v in state_dict.items() if """linear2""" not in k}
								UpperCAmelCase__  :					List[Any]        =     {k: v for k, v in state_dict.items() if """linear2""" in k}

								with TemporaryDirectory() as tmp_dir:
											offload_state_dict(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					str        =     OffloadedWeightsLoader(state_dict=_lowerCamelCase							,							save_folder=_lowerCamelCase)

											# Every key is there with the right value
											self.assertEqual(sorted(_lowerCamelCase)							,							sorted(state_dict.keys()))
											for key, param in state_dict.items():
														self.assertTrue(torch.allclose(_lowerCamelCase							,							weight_map[key]))

								UpperCAmelCase__  :					List[str]        =     {k: v for k, v in state_dict.items() if """weight""" in k}
								UpperCAmelCase__  :					Optional[int]        =     {k: v for k, v in state_dict.items() if """weight""" not in k}

								with TemporaryDirectory() as tmp_dir:
											offload_state_dict(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					Dict        =     OffloadedWeightsLoader(state_dict=_lowerCamelCase							,							save_folder=_lowerCamelCase)

											# Every key is there with the right value
											self.assertEqual(sorted(_lowerCamelCase)							,							sorted(state_dict.keys()))
											for key, param in state_dict.items():
														self.assertTrue(torch.allclose(_lowerCamelCase							,							weight_map[key]))

								with TemporaryDirectory() as tmp_dir:
											offload_state_dict(_lowerCamelCase							,							_lowerCamelCase)
											# Duplicates are removed
											UpperCAmelCase__  :					str        =     OffloadedWeightsLoader(state_dict=_lowerCamelCase							,							save_folder=_lowerCamelCase)

											# Every key is there with the right value
											self.assertEqual(sorted(_lowerCamelCase)							,							sorted(state_dict.keys()))
											for key, param in state_dict.items():
														self.assertTrue(torch.allclose(_lowerCamelCase							,							weight_map[key]))




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
								UpperCAmelCase__  :					Any        =     extract_submodules_state_dict(_lowerCamelCase							,							["""a.1""", """a.2"""])
								self.assertDictEqual(_lowerCamelCase							,							{"""a.1""": 0, """a.2""": 2})

								UpperCAmelCase__  :					Tuple        =     {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
								UpperCAmelCase__  :					int        =     extract_submodules_state_dict(_lowerCamelCase							,							["""a.1""", """a.2"""])
								self.assertDictEqual(_lowerCamelCase							,							{"""a.1.a""": 0, """a.2.a""": 2})



'''simple docstring'''



from __future__ import annotations

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

# initial triangle of Koch snowflake
__A	=numpy.array([0, 0])
__A	=numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A	=numpy.array([1, 0])
__A	=[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     initial_vectors
			for _ in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     iteration_step(UpperCamelCase__					)
			return vectors







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     []
			for i, start_vector in enumerate(vectors[:-1]					):
						UpperCAmelCase__  :					Tuple        =     vectors[i + 1]
						new_vectors.append(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     end_vector - start_vector
						new_vectors.append(start_vector + difference_vector / 3					)
						new_vectors.append(
						    start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,      6_0					)					)
						new_vectors.append(start_vector + difference_vector * 2 / 3					)
			new_vectors.append(vectors[-1]					)
			return new_vectors







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     numpy.radians(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     numpy.cos(UpperCamelCase__					), numpy.sin(UpperCamelCase__					)
			UpperCAmelCase__  :					Dict        =     numpy.array(((c, -s), (s, c))					)
			return numpy.dot(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					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()
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     zip(*UpperCamelCase__					)
			plt.plot(UpperCamelCase__ ,      UpperCamelCase__					)
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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