


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,
)


_A		:					int          =							namedtuple('''ExampleNamedTuple''', '''a b c''')



class 							a       (      unittest.TestCase ):





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

        __lowerCamelCase:					str	 =						send_to_device(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta.cpu()     ,						SCREAMING_SNAKE_CASE_				)				)

        __lowerCamelCase:					str	 =						send_to_device((tensor, [tensor, tensor], 1)     ,						SCREAMING_SNAKE_CASE_				)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta[0].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertIsInstance(resulta[1]     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta[1][0].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertTrue(torch.equal(resulta[1][1].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertEqual(resulta[2]     ,						1				)

        __lowerCamelCase:					Dict	 =						send_to_device({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}     ,						SCREAMING_SNAKE_CASE_				)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta["""a"""].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertIsInstance(resulta["""b"""]     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta["""b"""][0].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertTrue(torch.equal(resulta["""b"""][1].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertEqual(resulta["""c"""]     ,						1				)

        __lowerCamelCase:					Dict	 =						send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE_     ,						b=[tensor, tensor]     ,						c=1				)     ,						SCREAMING_SNAKE_CASE_				)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta.a.cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertIsInstance(resulta.b     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta.b[0].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertTrue(torch.equal(resulta.b[1].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertEqual(resulta.c     ,						1				)

        __lowerCamelCase:					Any	 =						send_to_device(UserDict({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}				)     ,						SCREAMING_SNAKE_CASE_				)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta["""a"""].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertIsInstance(resulta["""b"""]     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(torch.equal(resulta["""b"""][0].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertTrue(torch.equal(resulta["""b"""][1].cpu()     ,						SCREAMING_SNAKE_CASE_				)				)
        self.assertEqual(resulta["""c"""]     ,						1				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        with self.assertRaises(SCREAMING_SNAKE_CASE_				) as cm:
           __lowerCamelCase:					Union[str, Any]	 =						recursively_apply(torch.tensor     ,						(torch.tensor(1				), 1)     ,						error_on_other_type=SCREAMING_SNAKE_CASE_				)
        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 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        __lowerCamelCase:					Optional[int]	 =						torch.tensor([1, 2, 3, 4, 5]				)
        self.assertEqual(listify(SCREAMING_SNAKE_CASE_				)     ,						[1, 2, 3, 4, 5]				)

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

        __lowerCamelCase:					Union[str, 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(SCREAMING_SNAKE_CASE_				)     ,						[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        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 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        __lowerCamelCase:					Tuple	 =						RegressionModel()
        __lowerCamelCase:					List[str]	 =						model.forward
        __lowerCamelCase:					List[str]	 =						convert_outputs_to_fpaa(model.forward				)
        __lowerCamelCase:					str	 =						extract_model_from_parallel(SCREAMING_SNAKE_CASE_     ,						keep_fpaa_wrapper=SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Union[str, Any]	 =						pickle.dumps(SCREAMING_SNAKE_CASE_				)





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





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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        __lowerCamelCase:					int	 =						RegressionModel()
        # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
        __lowerCamelCase:					int	 =						torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Any	 =						extract_model_from_parallel(SCREAMING_SNAKE_CASE_				)

        self.assertEqual(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)





     @require_torch_min_version(version="""2.0"""				)
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        __lowerCamelCase:					Optional[Any]	 =						RegressionModel()
        __lowerCamelCase:					Optional[Any]	 =						torch.compile(SCREAMING_SNAKE_CASE_				)

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

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







     def 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        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"""]				)				)






_A		:					Optional[int]          =							{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]			)							->				str:
   return c.isdigit()



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]			)							->				str:
   __lowerCamelCase:					List[str]	 =						[]

   # iterate over the string in reverse order
   for c in expression.split()[::-1]:
      # push operand to stack
      if is_operand(snake_case			):
         stack.append(int(snake_case			)			)

      else:
         # pop values from stack can calculate the result
         # push the result onto the stack again
         __lowerCamelCase:					Any	 =						stack.pop()
         __lowerCamelCase:					int	 =						stack.pop()
         stack.append(calc[c](snake_case    ,   snake_case			)			)

   return stack.pop()


# Driver code
if __name__ == "__main__":
   _A		:					int          =							'''+ 9 * 2 6'''
   print(evaluate(test_expression))

   _A		:					str          =							'''/ * 10 2 + 4 1 '''
   print(evaluate(test_expression))


