


"""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_  :  Any     		=    namedtuple("""ExampleNamedTuple""", """a b c""")






class  __A							(			unittest.TestCase     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=torch.randn(5					,       2		)
     a											=torch.device('''cuda'''		) if torch.cuda.is_available() else torch.device('''cpu'''		)

     a											=send_to_device(__A					,       __A		)
     self.assertTrue(torch.equal(resulta.cpu()					,       __A		)		)

     a											=send_to_device((tensor, [tensor, tensor], 1)					,       __A		)
     self.assertIsInstance(__A					,       __A		)
     self.assertTrue(torch.equal(resulta[0].cpu()					,       __A		)		)
     self.assertIsInstance(resulta[1]					,       __A		)
     self.assertTrue(torch.equal(resulta[1][0].cpu()					,       __A		)		)
     self.assertTrue(torch.equal(resulta[1][1].cpu()					,       __A		)		)
     self.assertEqual(resulta[2]					,       1		)

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

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

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     with self.assertRaises(__A		) as cm:
         a											=recursively_apply(torch.tensor					,       (torch.tensor(1		), 1)					,       error_on_other_type=__A		)
     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		)   ->	List[str]:
     a											=torch.tensor([1, 2, 3, 4, 5]		)
     self.assertEqual(listify(__A		)					,       [1, 2, 3, 4, 5]		)

     a											=torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]		)
     self.assertEqual(listify(__A		)					,       [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]		)

     a											=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(__A		)					,       [[[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		)   ->	Tuple:
     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		)   ->	List[Any]:
     a											=RegressionModel()
     a											=model.forward
     a											=convert_outputs_to_fpaa(model.forward		)
     a											=extract_model_from_parallel(__A					,       keep_fpaa_wrapper=__A		)
     a											=pickle.dumps(__A		)


 @require_cuda
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=RegressionModel()
     a											=model.forward
     a											=torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
     a											=convert_outputs_to_fpaa(model.forward		)
     a											=extract_model_from_parallel(__A					,       keep_fpaa_wrapper=__A		)
     a											=pickle.dumps(__A		)


 @require_cuda
 @require_torch_min_version(version='''2.0'''		)
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=RegressionModel()
     a											=model.forward
     a											=torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
     a											=convert_outputs_to_fpaa(model.forward		)
     a											=torch.compile(model.forward					,       backend='''inductor'''		)
     a											=torch.randn(4					,       10		).cuda()
     a											=model(__A		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=RegressionModel()
     # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
     a											=torch.nn.parallel.DataParallel(__A		)
     a											=extract_model_from_parallel(__A		)

     self.assertEqual(__A					,       __A		)


 @require_torch_min_version(version='''2.0'''		)
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=RegressionModel()
     a											=torch.compile(__A		)

     # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
     a											=torch.nn.parallel.DataParallel(__A		)
     a											=torch.compile(__A		)
     a											=extract_model_from_parallel(__A		)

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





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[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''']		)		)



"""simple docstring"""







def _A		(		lowercase						):







    """simple docstring"""
    a											=[]
    a											=1

    for i in range(len(lowercase						)						):
        if i + 1 < len(lowercase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            a											=1

    return encoded
def _A		(		lowercase						):







    """simple docstring"""
    return "".join(char * length for char, length in encoded						)


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="""run_length_encode""", verbose=True)
       testmod(name="""run_length_decode""", verbose=True)