



"""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,
)


UpperCAmelCase:   Union[str, Any]       			=							namedtuple("""ExampleNamedTuple""", """a b c""")





class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					torch.randn(5							,2					)
			_lowercase							:      List[str]			=					torch.device("""cuda"""					) if torch.cuda.is_available() else torch.device("""cpu"""					)

			_lowercase							:      int			=					send_to_device(UpperCAmelCase_							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta.cpu()							,UpperCAmelCase_					)					)

			_lowercase							:      str			=					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					)

			_lowercase							:      Any			=					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					)

			_lowercase							:      List[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					)

			_lowercase							:      Union[str, Any]			=					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 					lowerCamelCase__ (			self					):
			with self.assertRaises(UpperCAmelCase_					) as cm:
					_lowercase							:      List[str]			=					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 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					torch.tensor([1, 2, 3, 4, 5]					)
			self.assertEqual(listify(UpperCAmelCase_					)							,[1, 2, 3, 4, 5]					)

			_lowercase							:      Any			=					torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)
			self.assertEqual(listify(UpperCAmelCase_					)							,[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)

			_lowercase							:      Any			=					torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)
			self.assertEqual(
			    listify(UpperCAmelCase_					)							,[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)






	def 					lowerCamelCase__ (			self					):
			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 					lowerCamelCase__ (			self					):
			_lowercase							:      Union[str, Any]			=					RegressionModel()
			_lowercase							:      Optional[Any]			=					model.forward
			_lowercase							:      Tuple			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      str			=					extract_model_from_parallel(UpperCAmelCase_							,keep_fpaa_wrapper=UpperCAmelCase_					)
			_lowercase							:      List[str]			=					pickle.dumps(UpperCAmelCase_					)






	@require_cuda
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					RegressionModel()
			_lowercase							:      int			=					model.forward
			_lowercase							:      List[str]			=					torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
			_lowercase							:      Dict			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      str			=					extract_model_from_parallel(UpperCAmelCase_							,keep_fpaa_wrapper=UpperCAmelCase_					)
			_lowercase							:      Tuple			=					pickle.dumps(UpperCAmelCase_					)






	@require_cuda
	@require_torch_min_version(version="""2.0"""					)
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Dict			=					RegressionModel()
			_lowercase							:      List[str]			=					model.forward
			_lowercase							:      Any			=					torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
			_lowercase							:      Any			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      Union[str, Any]			=					torch.compile(model.forward							,backend="""inductor"""					)
			_lowercase							:      Dict			=					torch.randn(4							,10					).cuda()
			_lowercase							:      str			=					model(UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					RegressionModel()
			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase							:      int			=					torch.nn.parallel.DataParallel(UpperCAmelCase_					)
			_lowercase							:      List[str]			=					extract_model_from_parallel(UpperCAmelCase_					)

			self.assertEqual(UpperCAmelCase_							,UpperCAmelCase_					)






	@require_torch_min_version(version="""2.0"""					)
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					RegressionModel()
			_lowercase							:      str			=					torch.compile(UpperCAmelCase_					)

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

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






	def 					lowerCamelCase__ (			self					):
			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"""]					)					)









from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase_  						=   TypeVar("T", bound=bool)
class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=value
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None




     @property
     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         return self._value




     @staticmethod
     def   __A				(	_UpperCamelCase			: RandomizedHeapNode[T] | None				,_UpperCamelCase			: RandomizedHeapNode[T] | None						)		->   RandomizedHeapNode[T] | None:


         '''simple docstring'''





         if not roota:
             return roota

         if not roota:
             return roota

         if roota.value > roota.value:
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=roota, roota

         if random.choice([True, False]						):
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=roota.right, roota.left

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(roota.left				,_UpperCamelCase						)

         return roota






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Any				,_UpperCamelCase			: Iterable[T] | None = ()						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None

         if data:
             for item in data:
                 self.insert(_UpperCamelCase						)




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root				,RandomizedHeapNode(_UpperCamelCase						)						)




     def   __A				(	self			: Dict						)		->   T | None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.top()

         if self._root is None:
             return None

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root.left				,self._root.right						)

         return result




     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         if not self._root:
             raise IndexError("""Can't get top element for the empty heap."""						)
         return self._root.value




     def   __A				(	self			: Optional[int]						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: List[Any]						)		->   list[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         while self:
             result.append(self.pop()						)

         return result




     def __bool__(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()






