



'''simple docstring'''






A_				:					Optional[Any]							=      [0.33, 0.44, 0.55, 0.44, 0.33]
A_				:					str							=      3
A_				:					List[Any]							=      5

# Buffer Space (with Parameters Space)
A_				:					list[float]							=      []
A_				:					list[float]							=      []

# Machine Time
A_				:					str							=      0




def        snake_case_   (	lowerCAmelCase_						)->						List[str]:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       # Choosing Dynamical Systems (All)
       for key, value in enumerate(lowerCAmelCase_						):
              # Evolution Parameter
              _UpperCAmelCase    :		List[Any]     	=					float(seed / value						)

              # Control Theory: Orbit Change
              _UpperCAmelCase    :		Any     	=					(buffer_space[(key + 1) % m] + e) % 1

              # Control Theory: Trajectory Change
              _UpperCAmelCase    :		List[str]     	=					(params_space[key] + e) % 1 + 3

              # Modification (Transition Function) - Jumps
              _UpperCAmelCase    :		int     	=					round(float(r * value * (1 - value)						)			,  10						)
              _UpperCAmelCase    :		Union[str, Any]     	=					r  # Saving to Parameters Space

       # Logistic Map
       assert max(lowerCAmelCase_						) < 1
       assert max(lowerCAmelCase_						) < 4

       # Machine Time
       machine_time += 1




def        snake_case_   (	)->						str:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       # PRNG (Xorshift by George Marsaglia)
       def xorshift(lowerCAmelCase_			,  lowerCAmelCase_						):
              x ^= y >> 13
              y ^= x << 17
              x ^= y >> 5
              return x

       # Choosing Dynamical Systems (Increment)
       _UpperCAmelCase    :		int     	=					machine_time % m

       # Evolution (Time Length)
       for _ in range(0			,  lowerCAmelCase_						):
              # Variables (Position + Parameters)
              _UpperCAmelCase    :		Tuple     	=					params_space[key]
              _UpperCAmelCase    :		Optional[Any]     	=					buffer_space[key]

              # Modification (Transition Function) - Flow
              _UpperCAmelCase    :		Any     	=					round(float(r * value * (1 - value)						)			,  10						)
              _UpperCAmelCase    :		str     	=					(machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

       # Choosing Chaotic Data
       _UpperCAmelCase    :		List[Any]     	=					int(buffer_space[(key + 2) % m] * (10**10)						)
       _UpperCAmelCase    :		Union[str, Any]     	=					int(buffer_space[(key - 2) % m] * (10**10)						)

       # Machine Time
       machine_time += 1

       return xorshift(lowerCAmelCase_			,  lowerCAmelCase_						) % 0xFF_FFF_FFF




def        snake_case_   (	)->						Union[str, Any]:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       _UpperCAmelCase    :		int     	=					K
       _UpperCAmelCase    :		List[Any]     	=					[0] * m
       _UpperCAmelCase    :		Optional[Any]     	=					0


if __name__ == "__main__":
   # Initialization
   reset()

   # Pushing Data (Input)
   import random

   A_				:					Dict							=      random.sample(range(0xffffffff), 1_0_0)
   for chunk in message:
      push(chunk)

   # for controlling
   A_				:					str							=      """"""

   # Pulling Data (Output)
   while inp in ("e", "E"):
      print(f"""{format(pull(), "#04x")}""")
      print(buffer_space)
      print(params_space)
      A_				:					Optional[Any]							=      input("""(e)exit? """).strip()









'''simple docstring'''






import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


_lowerCAmelCase       =   namedtuple('''ExampleNamedTuple''', '''a b c''')



class    A						(					unittest.TestCase						):




				'''simple docstring'''





				def 			a_			(self     )      ->			Tuple:
							__UpperCamelCase :     Optional[int]										=  torch.randn(5					,    2     )
							__UpperCamelCase :     List[str]										=  torch.device("cuda"     ) if torch.cuda.is_available() else torch.device("cpu"     )

							__UpperCamelCase :     Optional[Any]										=  send_to_device(_UpperCAmelCase					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta.cpu()					,    _UpperCAmelCase     )     )

							__UpperCamelCase :     Any										=  send_to_device((tensor, [tensor, tensor], 1)					,    _UpperCAmelCase     )
							self.assertIsInstance(_UpperCAmelCase					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta[0].cpu()					,    _UpperCAmelCase     )     )
							self.assertIsInstance(resulta[1]					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta[1][0].cpu()					,    _UpperCAmelCase     )     )
							self.assertTrue(torch.equal(resulta[1][1].cpu()					,    _UpperCAmelCase     )     )
							self.assertEqual(resulta[2]					,    1     )

							__UpperCamelCase :     Optional[int]										=  send_to_device({"a": tensor, "b": [tensor, tensor], "c": 1}					,    _UpperCAmelCase     )
							self.assertIsInstance(_UpperCAmelCase					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta["a"].cpu()					,    _UpperCAmelCase     )     )
							self.assertIsInstance(resulta["b"]					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta["b"][0].cpu()					,    _UpperCAmelCase     )     )
							self.assertTrue(torch.equal(resulta["b"][1].cpu()					,    _UpperCAmelCase     )     )
							self.assertEqual(resulta["c"]					,    1     )

							__UpperCamelCase :     Optional[Any]										=  send_to_device(ExampleNamedTuple(a=_UpperCAmelCase					,    b=[tensor, tensor]					,    c=1     )					,    _UpperCAmelCase     )
							self.assertIsInstance(_UpperCAmelCase					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta.a.cpu()					,    _UpperCAmelCase     )     )
							self.assertIsInstance(resulta.b					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta.b[0].cpu()					,    _UpperCAmelCase     )     )
							self.assertTrue(torch.equal(resulta.b[1].cpu()					,    _UpperCAmelCase     )     )
							self.assertEqual(resulta.c					,    1     )

							__UpperCamelCase :     Dict										=  send_to_device(UserDict({"a": tensor, "b": [tensor, tensor], "c": 1}     )					,    _UpperCAmelCase     )
							self.assertIsInstance(_UpperCAmelCase					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta["a"].cpu()					,    _UpperCAmelCase     )     )
							self.assertIsInstance(resulta["b"]					,    _UpperCAmelCase     )
							self.assertTrue(torch.equal(resulta["b"][0].cpu()					,    _UpperCAmelCase     )     )
							self.assertTrue(torch.equal(resulta["b"][1].cpu()					,    _UpperCAmelCase     )     )
							self.assertEqual(resulta["c"]					,    1     )
				def 			a_			(self     )      ->			Optional[Any]:
							with self.assertRaises(_UpperCAmelCase     ) as cm:
										__UpperCamelCase :     int										=  recursively_apply(torch.tensor					,    (torch.tensor(1     ), 1)					,    error_on_other_type=_UpperCAmelCase     )
							self.assertEqual(
							    str(cm.exception     )					,    "Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."					,    )
				def 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     Dict										=  torch.tensor([1, 2, 3, 4, 5]     )
							self.assertEqual(listify(_UpperCAmelCase     )					,    [1, 2, 3, 4, 5]     )

							__UpperCamelCase :     int										=  torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]     )
							self.assertEqual(listify(_UpperCAmelCase     )					,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]     )

							__UpperCamelCase :     Dict										=  torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]     )
							self.assertEqual(
							    listify(_UpperCAmelCase     )					,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]     )
				def 			a_			(self     )      ->			Optional[Any]:
							with patch_environment(aa=1					,    BB=2     ):
										self.assertEqual(os.environ.get("AA"     )					,    "1"     )
										self.assertEqual(os.environ.get("BB"     )					,    "2"     )

							self.assertNotIn("AA"					,    os.environ     )
							self.assertNotIn("BB"					,    os.environ     )
				def 			a_			(self     )      ->			Dict:
							__UpperCamelCase :     Tuple										=  RegressionModel()
							__UpperCamelCase :     List[str]										=  model.forward
							__UpperCamelCase :     Any										=  convert_outputs_to_fpaa(model.forward     )
							__UpperCamelCase :     List[str]										=  extract_model_from_parallel(_UpperCAmelCase					,    keep_fpaa_wrapper=_UpperCAmelCase     )
							__UpperCamelCase :     Dict										=  pickle.dumps(_UpperCAmelCase     )
				@require_cuda
				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     List[str]										=  RegressionModel()
							__UpperCamelCase :     List[Any]										=  model.forward
							__UpperCamelCase :     List[str]										=  torch.cuda.amp.autocast(dtype=torch.floataa     )(model.forward     )
							__UpperCamelCase :     str										=  convert_outputs_to_fpaa(model.forward     )
							__UpperCamelCase :     int										=  extract_model_from_parallel(_UpperCAmelCase					,    keep_fpaa_wrapper=_UpperCAmelCase     )
							__UpperCamelCase :     int										=  pickle.dumps(_UpperCAmelCase     )
				@require_cuda
				@require_torch_min_version(version="2.0"     )
				def 			a_			(self     )      ->			Tuple:
							__UpperCamelCase :     List[Any]										=  RegressionModel()
							__UpperCamelCase :     Tuple										=  model.forward
							__UpperCamelCase :     Optional[int]										=  torch.cuda.amp.autocast(dtype=torch.floataa     )(model.forward     )
							__UpperCamelCase :     List[Any]										=  convert_outputs_to_fpaa(model.forward     )
							__UpperCamelCase :     Union[str, Any]										=  torch.compile(model.forward					,    backend="inductor"     )
							__UpperCamelCase :     int										=  torch.randn(4					,    1_0     ).cuda()
							__UpperCamelCase :     int										=  model(_UpperCAmelCase     )
				def 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     List[Any]										=  RegressionModel()
							# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
							__UpperCamelCase :     Optional[Any]										=  torch.nn.parallel.DataParallel(_UpperCAmelCase     )
							__UpperCamelCase :     Union[str, Any]										=  extract_model_from_parallel(_UpperCAmelCase     )

							self.assertEqual(_UpperCAmelCase					,    _UpperCAmelCase     )
				@require_torch_min_version(version="2.0"     )
				def 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     int										=  RegressionModel()
							__UpperCamelCase :     List[str]										=  torch.compile(_UpperCAmelCase     )

							# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
							__UpperCamelCase :     Union[str, Any]										=  torch.nn.parallel.DataParallel(_UpperCAmelCase     )
							__UpperCamelCase :     List[str]										=  torch.compile(_UpperCAmelCase     )
							__UpperCamelCase :     Optional[int]										=  extract_model_from_parallel(_UpperCAmelCase     )

							self.assertEqual(compiled_model._orig_mod					,    compiled_model_unwrapped._orig_mod     )





				def 			a_			(self     )      ->			str:
							self.assertEqual(find_device([1, "a", torch.tensor([1, 2, 3]     )]     )					,    torch.device("cpu"     )     )
							self.assertEqual(find_device({"a": 1, "b": torch.tensor([1, 2, 3]     )}     )					,    torch.device("cpu"     )     )
							self.assertIsNone(find_device([1, "a"]     )     )



